Skip to content

Advertisement

You're viewing the new version of our site. Please leave us feedback.

Learn more

BMC Bioinformatics

Open Access

The parameter sensitivity of random forests

BMC BioinformaticsBMC series – open, inclusive and trusted201617:331

https://doi.org/10.1186/s12859-016-1228-x

Received: 15 December 2015

Accepted: 26 August 2016

Published: 1 September 2016

Abstract

Background

The Random Forest (RF) algorithm for supervised machine learning is an ensemble learning method widely used in science and many other fields. Its popularity has been increasing, but relatively few studies address the parameter selection process: a critical step in model fitting. Due to numerous assertions regarding the performance reliability of the default parameters, many RF models are fit using these values. However there has not yet been a thorough examination of the parameter-sensitivity of RFs in computational genomic studies. We address this gap here.

Results

We examined the effects of parameter selection on classification performance using the RF machine learning algorithm on two biological datasets with distinct p/n ratios: sequencing summary statistics (low p/n) and microarray-derived data (high p/n). Here, p, refers to the number of variables and, n, the number of samples. Our findings demonstrate that parameterization is highly correlated with prediction accuracy and variable importance measures (VIMs). Further, we demonstrate that different parameters are critical in tuning different datasets, and that parameter-optimization significantly enhances upon the default parameters.

Conclusions

Parameter performance demonstrated wide variability on both low and high p/n data. Therefore, there is significant benefit to be gained by model tuning RFs away from their default parameter settings.

Keywords

Machine-learningRandom forestParameterizationComputational biologyEnsemble methodsOptimizationMicroarraySeqControl

Background

Machine learning (ML) techniques are widely used in the analysis of high-throughput data to answer a broad range of biological questions. Applications in the field of medicine have transformed our understanding of complex genomic interactions and measurements [1]. ML has been successfully applied to biological disciplines including proteomics [2, 3], drug development [4, 5], DNA sequence analysis [68], cancer classification [913], clinical decision making [14, 15], and biomarker discovery [16, 17]. The versatility of ML algorithms to broad ranges of data and applications offers powerful, yet generalizable solutions to biological questions.

Recently, the random forest (RF) algorithm [18] for ML has achieved broad popularity. RF is a form of ensemble learning and possesses several characteristics that impart versatility. It can be applied to two-class or multi-class prediction problems, model interactions among variables, can take on a mixture of categorical and continuous variables, provides variable importance measures (VIMs), and has good predictive performance even for data with more variables (p) than samples (n; i.e. p > > n); potentially involving highly noisy and significantly correlated variables [19, 20]. Due to their non-parametric nature, RFs are fairly robust with relatively straightforward applications for inexperienced users [21, 22]. Consequently, this algorithm has expanded to a framework of models [23].

To train a random forest model, a bootstrap [24] sample is drawn, with the number of samples specified by the parameter sampsize [25]. By default, the bootstrap sample has the same number of samples as the original data: some samples are represented multiple times, whereas others are absent, leading to approximately 37 % of samples being absent in any given tree. These are referred to as the out-of-bag (OOB) samples [26]. Independent of the sampsize setting, after each sample is drawn, a decision tree is created. In the most commonly-used implementation, fully-grown or unpruned decision trees are created [18]. The number of trees is denoted by the parameter n tree [21]. This collection of models is known as bootstrap aggregation or bagging [27] and is commonly applied to high-variance and low-bias learners such as trees [28, 29]. Since individual trees are more prone to over-fitting than a collection of trees, an ensemble method has a significant advantage [27, 29]; however, this is limited by the correlation between the trees and can be mitigated by choosing a number of randomly selected input variables at each split of the tree. The number of random variables used at each split is denoted by the parameter m try . Of this subset of randomly selected variables, the one that forms the best split is selected [25, 30]. The best split is selected on the basis of a specific objective function, most typically maximization of the Gini coefficient or total gain in purity. This produces the most homogeneous groups and lowest OOB error [21]. Several empirical studies have shown the benefit of aggregating multiple trees to create a strong learner whereas, independently they would be considered unstable with lower classification accuracy [27, 3134].

Machine learning algorithms frequently require estimation of model parameters and hyper-parameters, commonly through grid-searching [35]. Surprisingly, though, this is not common practice in the literature for RFs, where default values are often used as it is widely believed that this method is parameter-insensitive, or at least robust to changes from default parameter settings [3638]. To test this assumption, we performed an exhaustive analysis of the parameter-sensitivity of RFs in two large, representative bioinformatics datasets. We show that our top performing tuned models were able to achieve greater prediction accuracies than the default models for both datasets and that the performance of the default parameterization is inconsistent. This emphasizes the value of per-dataset tuning of RF models.

Results

Experimental design

To evaluate the sensitivity of RF models to parameterization, we selected two datasets representative of those commonly used in computational biology. The first studies quality-control metrics in next-generation sequencing [6] and comprises 15 features (sequencing quality metrics) with 720 training samples and 576 validation samples, and thus reflects low p/n ratio studies. Each sample was classified as “good library” or “bad library” based on information external to the 15 features, and our models aimed to predict this binary response variable.

The second dataset reflected high p/n studies and comprises three categorical clinical variables and 12,135 continuous mRNA abundances for Non-Small Cell Lung Cancer (NSCLC) patients [13]. We trained models to predict patient outcome, “no death” or “death”. There were 255 samples in the training cohort.

For both datasets, we performed two model-fitting steps (Fig. 1). First, we selected a broad and comprehensive range of parameters (Additional file 1), and trained a RF classification model for each combination, including the default parameters. Models were trained on the training dataset and validated on a fully independent dataset. Performance was scored using the Area Under the Receiver Operating Characteristic Curve (AUC) [38]. Second, we fit an RF regression model using the data from the previous step: parameters were set as the covariates and AUC as the response. This allows us to characterize the association between prediction accuracy and parameterization. We randomly sampled 2/3 of parameter sets for training and reserved the remainder for validation. We aimed to predict the withheld AUC scores and assessed performance using Spearman's Rank Correlation Coefficient (ρ) and Lin's Concordance Correlation Coefficient (ρ c ).
Fig. 1

Experimental Design. Classification-based model fitting began with a unique combination of n tree , m try , and sampsize parameters in conjunction with training data, illustrated by the gray boxes. Each learned random forest model was used to predict the class of the validation data. Subsequently, AUC scores were calculated using the true class labels and these values were randomly subsetted into training and validation groups using 2/3 and 1/3 of the samples, respectively. In the second model fitting step, we evaluated whether AUC could be predicted from parameter sets alone. A RF regression model was fit using the parameters n tree , m try , and sampsize as variables and AUC as the response, illustrated by the blue boxes. Default settings were selected to train the RF regression models and AUC scores were predicted for the validation data. We evaluated the results using Spearman's and Lin’s correlation and determined the relative importance of each variable

Prediction accuracy is a strong function of parameterization in low p/n studies

We first evaluated the parameter sensitivity of RF prediction accuracy in the low p/n dataset. We created 1,500 different sets of parameters and evaluated the performance of each. Most models succeeded at this task (Fig. 2), with a median AUC of 0.893 and 96 % of models exceeding 0.80 AUC. However, the performance varied dramatically, with a range of 0.6113–0.9996, suggesting that some parameterizations greatly improve or hinder prediction accuracy. The default parameterization (n tree  = 500, m try  = 3, sampsize = 720 with replacement) performed well, with an AUC of 0.9726 and ranked in the top 12 % of all models (174/1,500; Additional file 2). This clearly demonstrates that the default settings are reasonable, but not optimal.
Fig. 2

Prediction accuracy is a strong function of parameterization in low p/n studies. Summary of low p/n predicted votes for each fitted random forest model (n = 1500). An AUC plot is provided at the top indicating the relative performance of each model, represented by each column. Each model was fitted from a unique combination of n tree (n = 10), m try (n = 15) and sampsize parameters (n = 10) and their respectively outcomes (votes) for each sample or row (n = 576). Votes are provided in values from 0–1 with 0 representing a “bad library” and 1 representing a “good library”. All columns are ordered in descending order of AUC scores and rows are ordered in descending order of the fraction of correct votes for a given sample (total votes for the true sample class/all votes). All samples were subsetted according to the true class labels “good library” and “bad library”, though the votes may not be reflective of this. Barplots for vote fractions are provided on the right of the main heatmaps and the values for each parameter are provided at the bottom of the figure. The n tree parameter is illustrated in blue, m try in magenta and sampsize in orange. Lighter hues represent lower values with darker hues indicating higher values. A scatterplot in the bottom right corner illustrates a strong negative correlation between the m try parameter with AUC scores (ρ = -0.89, p = 0)

We asked if models were consistently struggling with the same samples. We looked for samples in the validation dataset where at least 50 % of models trained with different parameter sets made incorrect predictions. In total 73/576 (12.7 %) of validation samples were difficult to classify. These were strongly asymmetrically distributed between the classes with 72/432 (17 %) “good library” validation samples difficult to classify relative to only 1/144 (1 %) “bad library” validation samples (p = 1.27 × 10−6; proportion-test). Interestingly though, the global error rate was not dramatically different between these two groups (20 % for “good library” vs. 14 % for “bad library” samples).

Parameterization was strongly correlated to AUC score (Fig. 2) in this dataset, but tightly focused on specific parameters. The number of variables sampled per node (m try ) was strongly negatively correlated with AUC (ρ mtry  = -0.895) and m try  ≤ 3 resulted in higher classification accuracy (mean AUC for m try  ≤ 3 = 0.97; mean AUC for m try  > 3 = 0.88; Welch Two Sample t-test). In contrast, models were relatively robust to changes in the n tree and sampsize parameters (ρ ntree  = 0.053 and ρ sampsize  = 0.096; Spearman's ρ).

To further explore the relationship between parameterization and performance, we univariately compared performance within each parameter (Additional file 1), with Benjamini-Hochberg adjustment for multiple-testing [39]. While sampsize values did not differ significantly from each other, however, n tree of 10 had significantly lower AUCs (q < 0.05) than other setting (Additional files 3, 4 and 5). Similarly, as noted above there was a near-linear relationship between increasing m try and decreasing AUC in the validation cohort (Additional file 6). These findings illustrated the strong influence of parameter selection on classification accuracy, and that both linear and threshold effects can be observed.

While the results to this point demonstrate both that parameterization powerfully influences prediction accuracy and that the default parameter settings are sub-optimal. However they do not demonstrate if it is possible to improve upon the defaults via parameter-optimization studies. We therefore implemented 10-fold and stratified 10-fold cross-validation using the parameters in Additional file 1. The data was randomly divided into 10 even folds, using 9/10 folds for training and the last fold for validation. This step was repeated so that each fold was used for validation once, so that the number of samples in validation was equal to the number of samples in the original training set (n = 720). All validation folds were pooled to evaluate AUC and cross-validated models were compared to non-cross-validated models using Spearman's ρ and Lin's ρ c (Additional file 2).

Predicted classes for both 10-fold cross-validation and stratified 10-fold cross-validation were weakly, but statistically-significantly correlated to the predicted classes for non-cross-validated results (Additional file 7a-b), and strongly correlated to one another (Additional file 7c).

We found that cross-validation and stratified cross-validation resulted in 97 % of models having an AUC of 1, including the defaults. We used an additional metric, root mean squared error (RMSE) to break ties. The optimal model in 10-fold cross-validation (rank = 1, n tree  = 500000, m try  = 10, sampsize = 720) had a RMSE of 0.00203, whereas the default model (rank = 579) had a RMSE of 0.0273. The optimal model in stratified 10-fold cross-validation (rank = 1, n tree  = 50, m try  = 14, sampsize = 648) had a RMSE of 0.0119, whereas the default model (rank = 319) had a RMSE of 0.0229. Overall, we found that 39 % (578/1500) and 21 % (318/1500) of models outperformed the untuned model (n tree  = 500, m try  = 3, sampsize = 720), respectively. Twenty one percent (310/1500) of these models shared the same parameter values and were found to perform better than the default settings in both cross-validated and non-cross-validated results. We found the addition of a second metric, RMSE useful in breaking ties and assessing model performance for low p/n data.

Prediction accuracy can be a strong function of parameterization in high p/n studies

To contrast these data, we examined the effects of parameterization on prediction accuracy for high p/n data [13] (Additional file 1). We created 1,000 different sets of parameters and evaluated the performance of each (Additional file 8). Again, we saw that model performance varied greatly with parameterization with a median AUC of 0.533 and 2 % of models exceeding an AUC of 0.60 (Fig. 3). However, the performance varied dramatically, with a range of 0.4254–0.6337, suggesting that some parameterizations could greatly improve or hinder prediction accuracy. The default parameterization (n tree  = 500, m try  = 110, sampsize = 255) performed well relative to other models, with an AUC of 0.6098 and ranked 10th. This demonstrates the near optimal performance of the default settings.
Fig. 3

Prediction accuracy is a strong function of parameterization in high p/n studies. Summary of the predicted votes for the combined validation data for each fitted random forest model (n = 1000). A barplot for AUC scores is provided at the top indicating the relative performance of each model, represented by each column. Each model was fitted from a unique combination of n tree (n = 10), m try (n = 10) and sampsize parameters (n = 10) and their respectively outcomes (votes) for each sample or row (n = 186). Votes are provided in values from 0–1 with 0 representing a “no death” event and 1 representing a “death” event. All columns are ordered in descending order of AUC scores and rows are ordered in descending order of the fraction of correct votes for a given sample (total votes for the true sample class/all votes). All samples were subsetted according to the true class labels “death” and “no death”, though the votes may not be reflective of this. On the right of the main heatmaps are respective barplots for vote fractions and a heatmap of parameter values is present at the bottom of the figure. The n tree parameter is illustrated in blue, m try in magenta and sampsize in orange. Lighter hues represent lower values with darker hues indicating higher values. To the right of this is a scatterplot illustrating Spearman's correlations of each parameter with the AUC scores; positive correlations were observed for the parameters n tree , m try , and sampsize (ρ = 0.222, p < 10−10; ρ = 0.238, p < 10−12; ρ = 0.207, p < 10−9, respectively)

We asked if models were consistently struggling with the same samples. We looked for samples in the validation dataset where at least 50 % of models trained with different parameter sets made incorrect predictions. In total 89/186 (48 %) of validation samples were difficult to classify. These were symmetrically distributed between the classes with 37/74 (50 %) “death” events difficult to classify compared to 52/112 (46 %) “no death” samples (p = 0.74; proportion-test). The error rate was significantly different between these two groups (for “no death” samples; p = 0; proportion-test).

Parameterization was strongly correlated to AUC in this dataset, with contribution from all parameters. We observed that m try (ρ = 0.238, p = 2.12 × 10−14; Spearman’s correlation) was the most correlated, followed by n tree (ρ = 0.222, p = 1.39 × 10−12; Spearman’s correlation) and sampsize (ρ = 0.207, p = 3.73 × 10−11; Spearman’s correlation).

To further explore the relationship between parameterization and performance, we univariately compared performance within each parameter (Additional file 1), with Benjamini-Hochberg adjustment for multiple-testing [39]. We observed that larger n tree values resulted in higher prediction accuracy and reduced performance variability compared to lower values (q < 10−8), with no significant difference observed between values n tree  ≥ 10,000 (Additional files 9 and 10). Similar results were observed for sampsize and m try (Additional files 11 and 12) where there was a near-linear relationship between increasing parameter values and AUC in the validation cohort. Additionally, no significant differences were observed in AUC for sampsize ≥ 153 and m try  ≥ 110. The m try value here is notable since it was used as the default, providing some support to previous claims that the default performs well. These findings illustrated the strong influence of parameter selection on classification accuracy, and that both linear and threshold effects can be observed.

Parameters can be used to predict performance

Having shown that model performance is strongly influenced by n tree , m try , and sampsize, we next asked how strongly these three parameters could predict AUC directly. We assessed variable importance using the Gini VIM, where larger values indicate a variable is more important for accurate classification. We were able to predict AUCs using this metric that closely reflects those of the true data for low p/n data (Additional file 13a; ρ = 0.92, p = 1.29 × 10−209, ρ c  = 0.89; Spearman's ρ and Lin's ρ c ). We observed that m try demonstrated the highest Gini VIM for low p/n data (Additional file 13b).

Similar results were observed for the high p/n data, where prediction accuracy was a strong function of parameter selection across all validation sets (Additional file 14a; ρ = 0.48, p = 5.42 × 10−21, ρ c  = 0.33; Spearman's ρ and Lin's ρ c ). Interestingly, the parameters demonstrated relatively balanced importance measures with sampsize demonstrating the highest Gini VIM and n tree with the lowest (Additional file 14b).

Importance ranks can be sensitive to parameter changes

Finally, we asked if parameterization change could alter the identification of importance variables (which are frequently used in feature-selection approaches, for example) [23, 36]. We focused on the low p/n data, and trained models using the settings in Additional file 1 and ranked permutation VIM for each quality metric from 1–15, with 1 representing the most important variable. Permutation VIM is the mean decrease in classification accuracy after a random variable is removed from model fitting. Larger values suggest a variable has more discriminative power [40, 41].

Variables differed in their sensitivity to parameter changes when evaluating variable importance (Fig. 4). The variable “Average reads/starts” was robust against parameter changes and was considered the most important in 94 % of all samples, whereas “Clusters” exemplified strong parameter sensitivity and was positively correlated to m try . On the other hand, “% bases ≥ 50 %” was found to have higher VIMs with lower m try values.
Fig. 4

Importance ranks can be sensitive to parameter changes in low p/n studies. Summary of the variable importance ranks for each sequencing metric (n = 15). An AUC plot is provided at the top indicating the relative performance of each model, represented by each column. Each model was fitted from a unique combination of n tree (n = 10), m try (n = 15) and sampsize parameters (n = 10) and their respectively outcomes (importance value) for each metric. Each column of the main heatmap corresponds to a model's importance values, and were ranked from 1–15, where 1 represented the most important feature and 15 the least. The importance values were ordered according to previously calculated AUC scores using predicted vote and true class labels. Each row represents a metric and are ordered according to the mean rank of its importance values. The importance values were simplified in the main heatmap and illustrate four groups only. Blue indicates a rank of 1, green a rank of 2, gold a rank of 3, and beige a rank of 4 and greater. A summary of overall rank groups for a particular metric are illustrated in a barplot on the right of the main heatmap and a covariate heatmap with all parameter combinations is illustrated at the bottom of the plot. The n tree parameter is illustrated in blue, m try in pink and orange for sampsize in orange. Some parameters demonstrate robust behaviour to parameter changes such as “Uncollapsed coverage” and “% bases ≥ 50 quality”, which were ranked between 11–15 inclusive in 96 % and 95 % of all samples, respectively. These variables possessed VIMs that suggested they were less influential on classification accuracy. Yet, “Average reads/starts” was insensitive to parameter changes and was considered the most important variable. Another variable “Clusters” was parameter sensitive, illustrating that variables vary in their sensitivity to parameter changes which can ultimately influence classification accuracy

Our order for variable importance deviated from that of the original study [6], where “% bases ≥ 8×” was reported as the most discriminative variable. We examined how variable importance changed with differing n tree values (n = 10) while holding m try and sampsize constant (m try  = 3, sampsize = 720; Additional file 15) and observed that larger n tree values led to more stable VIMs.

Discussion

There are two common assumptions regarding RF models. The first is that the default parameters lead to good performance [37, 38] and the second is that the algorithm is robust to parameter changes [19, 21, 42]. To help quantify the wide-spread nature of these assumptions we manually reviewed all papers published in BMC Bioinformatics between January 1, 2015 and November 21, 2015 (Additional file 16). We looked for papers that referenced the canonical RF paper [18] during this ~11 month period. Of the 16 papers that implemented RFs, exactly half performed a parameterization study to optimize parameters, and only 5/16 papers reported the final parameter setting used. That is, about half of RF-studies could benefit from improved parameterization and another third from improved reporting. This highlights clearly the gap between machine learning theory and practice, and gaps in methods reporting that are not being caught by peer-review.

Parameterization is difficult and its absence from the model fitting process may be due to limited experience, a lack of readily available heuristics or limited resources [43]. Consequently, these factors lead to the inappropriate selection of parameters or lack thereof, directly influencing learning [44]. We sought to determine the effects of parameterization on classification accuracy and variable importance measures. Our findings suggested data-dependent parameter sensitivities ultimately influence classification accuracy and VIMs for binary classification problems. Our findings may not extend to regression analyses or multi-class problems, where the relationship between the variables and response is much more complex.

We observed that the default parameters have the potential to perform well, however results across all tests indicated that parameter tuning enabled higher model performance. The majority of high performing parameter combinations did not coincide with general patterns observed in the pattern selection process i.e., in most samples higher parameter values led to greater classification accuracy and the top performing parameters had lower values. Such models may have performed well due to random chance or were over-fit. These results emphasize the importance of parameter tuning and how one cannot rely on any arbitrary parameter set to perform well. This also suggests that existing publications implementing untuned models may improve classification accuracy through model tuning. To reduce computation time and work for parameter selection, we applied a RF regression model, which predicted model performance more accurately than the more expensive 10-fold cross-validation and stratified 10-fold cross-validation. The RF regression model was also better at discriminating poor performing parameter sets from high performing parameter sets.

To our knowledge, this is the first computational genomic study that addresses parameter sensitivities using a comprehensive range of values for two unique biological data types. In particular, we observed that the low p/n data was sensitive to changes in m try and the high p/n data demonstrated a synergism between all three parameters. Additionally, not all variables exhibited robust behaviour towards parameter changes when determining VIMs (e.g., “Clusters” and “% bases ≥ 50×”). These findings challenge the assumption that RFs are relatively robust. Parameters that did not a play key role independently had an observable and significant synergism when constructing RF regression models with interaction terms (from section Parameters can be used to predict performance).

We also noted that our variable importance ranks did not coincide with [6]. This was largely explained by the bias in feature importance for the RF algorithm. Variables that were highly correlated to truly influential variables or have more categories will be over-selected by the algorithm and do not reflect the true relative contribution of a variable in a classification or regression problem [20]. Chong et al. [6] implemented an alternate algorithm, “cforest”, from the R package “party” to generate unbiased VIMs. One area for future research is to investigate the sensitivity of parameter changes in the “cforest” algorithm.

Moreover, characteristics of the data, such as, p > > n and minor class imbalances were observed. The numerous variables in the high p/n data constrained the selection range of m try parameters, potentially confounding the results. In such samples, m try  ≠ p. This was not the sample for the low p/n data, where we were able to test all possible values of m try . This limitation may also be viewed as beneficial since the number of randomly selected variables at each split is constrained and therefore, limits tree correlation within a forest.

An additional data characteristic limiting the classification accuracy in RF could be class imbalance [45, 46]. The unequal number of classes in a dataset is technically considered class imbalance, however, in the scientific community, class imbalance corresponds to data with significant to extreme disproportional class numbers, such as, 100:1 or 10,000:1 [47]. These types of “imbalanced data” were not considered here. Furthermore, the minor classes “bad library” and “death” in the small p/n data and high p/n data respectively, had a higher classification accuracy suggesting, in some instances, the heterogeneity of a sample is more influential on classification accuracy. We also aimed to mitigate class imbalance effects through stratified sampling and by using the AUC performance metric. Alternate methods such as, cost sensitive learning [48] and artificially balancing the data through down sampling the majority class [49], over sampling the minority class [50], or both [51] have been shown to deal with class imbalance effectively. Artificial balancing ensures that class priors are equal in tree classifiers and that the minority class is included in the bootstrap sample. On the other hand, cost sensitive learning incurs a greater cost for misclassified minority samples over majority samples. Minor class imbalances were not observed to be an issue in this study, however, data should be analysed with caution in highly imbalanced studies.

Conclusions

We analysed the effects of parameterization using exhaustive selection methods and showed that tuning can be successfully applied to a non-parametric machine learning algorithm to improve prediction accuracy. Although we only examined two different genomic datasets, we observed that parameter sensitivities are data-specific, necessitating per-dataset tuning. Our findings illustrate this through discordant correlations between parameters and performance scores for low p/n and high p/n data. The model fitting process is a fundamental step in machine learning and careless parameter selection can lead to sub-optimal models and potentially missed findings.

Methods

Datasets

We explored parameterization of RFs on two datasets. The first was a sequencing-derived dataset (low p/n data) [6] and the second was a microarray-derived dataset (high p/n data) [13], reflecting low and high p/n data, respectively.

The low p/n data (15 variables with 1,296 samples) contained 15 quality metrics describing overall coverage, coverage distribution, basewise coverage and basewise quality of 53 whole genomes. The data was derived for the International Cancer Genome Consortium (ICGC) project to predict the amount of sequencing that is required to reach a given coverage depth for 1/8 lane samples [6]. The outcome column was a list of binary values (0 for “bad library” or 1 for “good library”) indicating whether the target coverage depth was reached (30× for normal, 50× for tumour). The data was split into training and validation sets, as described by the low p/n paper [6] and contained 720 and 576 samples, respectively.

The high p/n data contained gene expression data for 442 lung adenocarcinomas and basic clinical covariates (stage, age and sex) to predict lung cancer patient outcome (0 for “no death” or 1 for “death”). The data were collected from six contributing institutions and grouped into four subsets based on the laboratory where processed (University of Michigan Cancer Center (UM), Moffitt Cancer Center (HLM), Memorial Sloan-Kettering Cancer Center (MSKCC), and Dana-Farber Cancer Institute (DFCI)). All facilities processed the data using the same robust and reproducible protocol.

The first two datasets, UM and HLM, were grouped together to form the training set (12,138 variables with 255 samples), while the MSKCC data (104 samples) and DFCI data (82 samples) formed the validation set (186 samples).

Parameter selection

The m try parameter values were selected using factor levels of the default value. Since the nature of this supervised learning problem is that of classification and not regression, the default value of m try is the square root of the number of variables or features in the data 18 √p, whereas, in regression the default is p/3. The study by [21] reported m try as the most sensitive parameter with values of m try factor = 1/2 (1/2• 18 √p), m try factor = 1 (18 √p) and m try factor = 2 (2•18√p) showing good performance. Given this information and the number of variables in the data, one to all variables were selected as m try values for the SeqControl dataset (p = 1-15), the m try values 1, 5, 11, 22, 55, 110, 220, 550, 1100, 2200 were selected for the NSCLC data (p = 12,138). The NSCLC values were obtained by selecting factor levels (1/100, 1/20, 1/10, 1/2, 1, 2, 5, 10, 20), multiplying them with p and taking the largest integer preceding a specified number i.e., for a value of 3.4, 3 was used.

The values for n tree were selected similarly to those for m try . We imposed factor levels to the default value and took the product to create the n tree values. The factor levels were 1/50, 1/10, 1/5, 4/10, 1, 2, 20, 100, 200 and 1000. The final n tree values were 10, 50, 100, 200, 500, 1000, 1e4, 5e4, 1e5, 5e5. The selected n tree values were the same for both datasets.

The final parameter sampsize, had the same factor levels for both datasets and was a sequence of values from 0.1–1, increasing by increments of 0.1. To obtain the final sampsize values, we multiplied the total number of samples in training by the sampsize factor levels and took the smallest integer proceeding a number i.e., for a value of 3.4, 4 was used.

Selected parameters were used to train models with the function “randomForest” using sampling with replacement. The data was partitioned according to the original papers, as described above. In the SeqControl data experiment, we aimed to predict whether the target of sequencing depth coverage was achieved using 1/8 lane (1 for “good library”, 0 for “bad library”). In the NSCLC data experiment, we aimed to predict patient outcome (1 for “death”, 0 for “no death”). A table of complete parameter settings for the SeqControl data and NSCLC data can be found in Additional file 1.

Model training

The data were trained using the function “randomForest” from the R package “randomForest” (v4.6-10) [21, 52]. A series of RFs were trained on each dataset using a unique combination of the three parameters: n tree , m try and sampsize. For the SeqControl data, we used 15 m try values, 10 n tree values, and 10 sampsize values. These values and numbers differed slightly in the NSCLC training: 10 m try values, 10 n tree values, and 10 sampsize values. A resulting total of 1500 and 1000 unique combination were obtained for model fitting on the SeqControl data and NSCLC data, respectively.

After training, each model was then validated on independent validation data to obtain class probabilities (votes). The votes and true class labels were then used to estimate model performance by calculating the AUC score.

Performance prediction using parameters as variables

In order to determine whether model performance could be predicted, we performed regression using RF, on a subset of parameters and their respective AUC scores. AUC scores were calculated by comparing the predicted votes from each model to the true classifications. We initially attempted this from a linear model approach, however, classification accuracy was low due to overfitting. After subsetting 2/3 of the data into training and 1/3 for validation, we performed model tuning and selected the model with the lowest mean squared error. Tuning was conducted using a grid of parameters (Additional file 17) and 5-fold cross validation. We then applied the optimal settings (n tree  = 200, m try  = 2, sampsize = 200) to train a RF model. The response for our model was AUC score and the variables were n tree , m try and sampsize. The expression for the model formula included the terms in an additive and interaction format i.e., sampsize + m try  + n tree  + sampsize*m try  + sampsize*n tree  + n tree * m try  + sampsize*n tree *m try . After training and validating the models, we were able to assess performance using the following metrics, Spearman's ρ, Spearman's p-value (P) and Lin's ρ c . Lastly, importance values were found for each variable (n tree , m try or sampsize) in the form of Gini VIM.

Model selection using 10-fold cross-validation and stratified 10-fold cross-validation

Ten-fold cross-validation was used to estimate the generalization error of each unique RF model (n = 1500) for the SeqControl data. This method of cross-validation has been suggested to perform better than the more expensive leave-one-out cross-validation [53]. The data was subsetted into 10 even folds, with nine groups selected for training and the last reserved for validation. This process was iterated until each fold was used in the validation stage once, so that the number of samples in validation was equal to the number of samples in the original training set (n = 720).

The above was repeated for stratified 10-fold cross-validation with an even distribution of the minority class among each fold. A total of 72 samples appeared in each fold with approximately 14 samples of the minority class and 58 of the majority class. AUC scores were used to estimate accuracy and correlations were calculated between non-cross-validation, 10-fold cross-validation and stratified 10-fold cross-validation results. A table comparing the above three methods is in Additional file 2.

Ranking variable importance

Additional information pertaining to variable importance was collected from training and validating the SeqControl models using permutation VIM [54]. Permutation VIM can be interpreted as the mean decrease in accuracy of a RF due to the removal of a variable. The magnitude of the value is directly proportional to the relative contribution of a particular variable in classifying samples, that is, the greater the decrease or drop in accuracy, the more a feature is correlated to the response.

The model for the SeqControl data had additional settings that were implemented, such as “importance”, “localImp”, “proximity” and “keep.inbag”. These arguments were all set to “TRUE” to keep results relatively consistent with the original paper [6].

Due to the exhaustive parameter selection method of grid searching, we parallelized jobs using Perl High Performance Computing Interface (HPCI) [55] and parallelized jobs further by using the R package, “foreach” (v1.4.2) [56].

Statistical model evaluation

We evaluated the performance of models using several statistical measures in the R statistical environment (v3.1.3) [57]. For classification accuracy, we calculated the AUC using the predicted votes and the true class labels with the function “auc” from the package pROC (v1.8) [58]. For non-parametric tests comparing the parameter performance in classification, we used the function “cor” from the base “stats” package (v3.2.0) [57] to calculate Spearman's ρ and to find the correlation coefficient between the AUC scores and the parameter of interest. Spearman's ρ, Spearman's p-value and the equation for Lin's ρ c from the paper [59] were used to determine the correlation between true and predicted AUC values in performance prediction. All p-values were adjusted using the function “p.adjust” from the base “stats” package (v3.2.0), using the Benjamini-Hochberg procedure.

Data visualization

Figures were generated in the programming language LaTeX and in the R statistical environment (v3.1.3) using custom R scripts for the “lattice” (v0.2-31) [60] and “latticeExtra” (v0.6-26) [61] packages.

Abbreviations

AUC: 

Area under the receiver operating characteristic curve

DFCI: 

Dana-Farber Cancer Institute

HLM: 

Moffitt Cancer Center

HPCI: 

High performance computing interface

ICGC: 

International Cancer Genome Consortium

ML: 

Machine learning

MSKCC: 

Memorial Sloan-Kettering Cancer Center

NSCLC: 

Non-small cell lung cancer

OOB: 

Out-of-bag

RF: 

Random forest

RMSE: 

Root mean squared error

UM: 

University of Michigan Cancer Center

VIM: 

Variable importance measure

Declarations

Acknowledgements

We are grateful to the reviewers who have contributed to the significant improvement of this paper, and to all members of the Boutros laboratory for their technical support and insightful comments.

Funding

This study was conducted with the support of the Ontario Institute for Cancer Research to PCB through funding provided by the government of Ontario. Dr. Boutros was supported by a Terry Fox Research Institute New Investigator Award and a CIHR New Investigator Award. This work was supported by Prostate Cancer Canada and is proudly funded by the Movember Foundation - Grant #RS2014-01.

Availability of data and material

Using public datasets that are already available. Data from the SeqControl experiment may be accessed through the original paper from Nature Methods (http://www.nature.com/nmeth/journal/v11/n10/full/nmeth.3094.html). The data used can be found in the Supplementary information as Supplementary table 10. Data from the lung adenocarcinoma study may be accessed through the original paper from Nature Medicine (http://www.nature.com/nm/journal/v14/n8/full/nm.1790.html). The microarray data can be found in the methods section of the paper and accessed at: https://www.ebi.ac.uk/arrayexpress/search.html?query=Affymetrix+lung+adenocarcinoma and accession number, E-GEOD-68571.

Authors’ contributions

BFH: Performed statistical and bioinformatics analyses. BFH: Wrote the first draft of the manuscript. BFH, PCB: Initiated the project. PCB: Supervised research. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Informatics and Bio-computing Program, Ontario Institute for Cancer Research
(2)
Department of Medical Biophysics, University of Toronto
(3)
Department of Pharmacology and Toxicology, University of Toronto
(4)
MaRS Centre

References

  1. Cruz JA, Wishart DS. Applications of Machine Learning in Cancer Prediction and Prognosis. Cancer Inform. 2006;2:59–77.Google Scholar
  2. Chen X, Liu M. Prediction of protein – protein interactions using random decision forest framework. Bioinformatics. 2005;21:4394–400.View ArticlePubMedGoogle Scholar
  3. Nielsen H, Brunak S, von Heijne G. Machine learning approaches for the prediction of signal peptides and other protein sorting signals. Protein Eng Des Sel. 1999;12:3–9.View ArticleGoogle Scholar
  4. Burbidge R, Trotter M, Buxton B, Holden S. Drug design by machine learning: support vector machines for pharmaceutical data analysis. Comput Chem. 2001;26:5–14.View ArticlePubMedGoogle Scholar
  5. Murphy RF. An active role for machine learning in drug development. Nat Chem Biol. 2014;7:327–30.View ArticleGoogle Scholar
  6. Chong LC, Albuquerque MA, Harding NJ, Caloian C, Chan-seng-yue M, De Borja R, Fraser M, Denroche RE, Beck TA, Van Der KT, Bristow RG, Mcpherson JD, Boutros PC. SeqControl: process control for DNA sequencing. Nat Methods. 2014;11:1071–8.View ArticlePubMedGoogle Scholar
  7. Ben-Hur A, Ong CS, Sonnenburg S, Schölkopf B, Rätsch G. Support vector machines and kernels for computational biology. PLoS Comput Biol. 2008;4, e1000173.View ArticlePubMedPubMed CentralGoogle Scholar
  8. Lafferty J, McCallum A, Pereira FCN. Conditional Random Fields : Probabilistic Models for Segmenting and Labeling Sequence Data. In: Proc 18th Int Conf Mach Learn. 2001. p. 282–9.Google Scholar
  9. Statnikov A, Wang L, Aliferis CF. A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification. BMC Bioinforma. 2008;9:1–10.View ArticleGoogle Scholar
  10. Guyon I, Weston J, Barnhill S. Gene Selection for Cancer Classification using Support Vector Machines. Mach Learn. 2002;46:389–422.View ArticleGoogle Scholar
  11. Hilario M, Kalousis A, Müller M, Pellegrini C. Machine learning approaches to lung cancer prediction from mass spectra. Proteomics. 2003;3:1716–9.View ArticleGoogle Scholar
  12. Tan AC, Gilbert D. Ensemble machine learning on gene expression data for cancer classification. Appl Bioinforma. 2003;2:1–10.Google Scholar
  13. Shedden K, Taylor JMG, Enkemann SA, Tsao MS, Yeatman TJ, Gerald WL, Eschrich S, Jurisica I, Giordano TJ, Misek DE, Chang AC, Zhu CQ, Strumpf D, Hanash S, Shepherd FA, Ding K, Seymour L, Naoki K, Pennell N, Weir B, Verhaak R, Ladd-Acosta C, Golub T, Gruidl M, Sharma A, Szoke J, Zakowski M, Rusch V, Kris M, Viale A, et al. Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study. Nat Med. 2008;14:822–7.View ArticlePubMedPubMed CentralGoogle Scholar
  14. Ayers M, Symmans WF, Stec J, Damokosh AI, Clark E, Hess K, Lecocke M, Metivier J, Booser D, Ibrahim N, Valero V, Royce M, Arun B, Whitman G, Ross J, Sneige N, Hortobagyi GN, Pusztai L. Gene expression profiles predict complete pathologic response to neoadjuvant paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide chemotherapy in breast cancer. J Clin Oncol. 2004;22:2284–93.View ArticlePubMedGoogle Scholar
  15. Shipp M, Ross KN, Tamayo P, Weng AP, Kutok JL, Aguiar RCT, Gaasenbeek M, Angelo M, Reich M, Pinkus GS, Ray TS, Koval MA, Last KW, Norton A, Lister A, Mesirov J, Neuberg D, Lander ES, Aster JC, Golub TR. Diffuse large B-cell lymphoma outcome prediction by gene- expression profiling and supervised machine learning. Nat Med. 2002;8:68–74.View ArticlePubMedGoogle Scholar
  16. Liu JJ, Cutler G, Li W, Pan Z, Peng S, Hoey T, Chen L, Ling XB. Multiclass cancer classification and biomarker discovery using GA-based algorithms. Bioinformatics. 2005;21:2691–7.View ArticlePubMedGoogle Scholar
  17. Yasui Y, Pepe M, Thompson ML, Adam B-L, Wright JR GL, Qu Y, Potter JD, Winget M, Thornquist M, Feng Z. A data-analytic strategy for protein biomarker discovery: profiling of high-dimensional proteomic data for cancer detection. Biostatistics. 2003;4:449–63.View ArticlePubMedGoogle Scholar
  18. Breiman L. Random Forests. Mach Learn. 2001;45:5–32.View ArticleGoogle Scholar
  19. Díaz-Uriarte R, De Andrés SA. Gene selection and classification of microarray data using random forest. BMC Bioinforma. 2006;7:1–13.View ArticleGoogle Scholar
  20. Strobl C, Boulesteix A-L, Zeileis A, Hothorn T. Bias in random forest variable importance measures: illustrations, sources and a solution. BMC Bioinforma. 2007;8:25.View ArticleGoogle Scholar
  21. Liaw A, Wiener M. Classification and Regression by randomForest. R News. 2002;2:18–22.Google Scholar
  22. Qi Y, Bar-Joseph Z, Klein-Seetharaman J. Evaluation of Different Biological Data and Computational Classification Methods for Use in Protein Interaction Prediction. Proteins. 2006;63:490–500.View ArticlePubMedPubMed CentralGoogle Scholar
  23. Criminisi A, Shotton J, Konukoglu E. Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning. Found Trends® Comput Graph Vis. 2011;7:81–227.View ArticleGoogle Scholar
  24. Efron B, Tibshirani R. Introduction to the Bootstrap. New York: Chapman & Hall; 1993.View ArticleGoogle Scholar
  25. Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP. Random forest: a classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci. 2003;43:1947–58.View ArticlePubMedGoogle Scholar
  26. Breiman L. Out-of-Bag Estimation. 1996. p. 1–13.Google Scholar
  27. Breiman L. Bagging Predictors. Mach Learn. 1996;24:123–40.Google Scholar
  28. Breiman L. Heuristics of Instability and Stabilization in Model Selection. Ann Stat. 1996;24:2350–83.View ArticleGoogle Scholar
  29. Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. New York: Springer; 2005.Google Scholar
  30. Segal MR. Machine Learning Benchmarks and Random Forest Regression. 2004.Google Scholar
  31. Bauer E, Kohavi R. An Empirical Comparison of Voting Classification Algorithms : Bagging, Boosting, and Variants. Mach Learn. 2011;38:1–38.Google Scholar
  32. Dietterich TG. An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization. Mach Learn. 2000;40:139–57.View ArticleGoogle Scholar
  33. Opitz D, Maclin R. Popular Ensemble Methods: An Emperical Study. J Artif Intell Res. 1999;11:169–98.Google Scholar
  34. Nagi S, Bhattacharyya DK. Classification of microarray cancer data using ensemble approach. Netw Model Anal Heal Informatics Bioinforma. 2013;2:159–73.View ArticleGoogle Scholar
  35. Snoek J, Larochelle H, Adams RP. Practical Bayesian Optimization of Machine Learning Algorithms. Adv Neural Inf Process Syst. 2012;1–9.Google Scholar
  36. Okun O, Priisalu H. Random Forest for Gene Expression Based Cancer Classification: Overlooked Issues. In: Proc 4th Int Meet Comput Intell Methods Bioinforma Biostat Portofino, Italy. 2007. p. 483–90.Google Scholar
  37. Sun YV, Bielak LF, Peyser PA, Turner ST, Sheedy PF, Boerwinkle E, Kardia SLR. Application of machine learning algorithms to predict coronary artery calcification with a sibship-based design. Genet Epidemiol. 2008;32:350–60.View ArticlePubMedPubMed CentralGoogle Scholar
  38. Sun YV. Multigenic Modeling of Complex Disease by Random Forest. Adv Genet. 2010;72:73–99.PubMedGoogle Scholar
  39. Benjamini Y, Hochberg Y. Benjamini and Y FDR.pdf. J R Stat Soc Ser B. 1995;57:289–300.Google Scholar
  40. Archer KJ, Kimes RV. Empirical characterization of random forest variable importance measures. Comput Stat Data Anal. 2008;52:2249–60.View ArticleGoogle Scholar
  41. Calle ML, Urrea V. Letter to the editor: Stability of Random Forest importance measures. Brief Bioinform. 2011;12:86–9.View ArticlePubMedGoogle Scholar
  42. Goldstein BA, Briggs FBS, Polley EC. Random Forests for Genetic Association Studies. Stat Appl Genet Mol Biol. 2011;10:1–34.Google Scholar
  43. Domingos P. A few useful things to know about machine learning. Commun ACM. 2012;55:78–87.View ArticleGoogle Scholar
  44. Li J-B, Chu S-C, Pan J-S. Kernel Learning Algorithms for Face Recognition. New York: Springer; 2013. p. 1–17.Google Scholar
  45. Dudoit S, Fridlyand J. Classification in microarray experiments. Stat Anal gene Expr microarray data. 2003;1:93–158.Google Scholar
  46. Sun Y, Kamel MS, Wong AKC, Wang Y. Cost-sensitive boosting for classification of imbalanced data. Pattern Recognit. 2007;40:3358–78.View ArticleGoogle Scholar
  47. He H, Garcia EA. Learning from Imbalanced Data. IEEE Trans Knowl Data Eng. 2009;21:1263–84.View ArticleGoogle Scholar
  48. Domingos P. MetaCost: A General Method for Making Classifiers. In: Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Diego: ACM Press; 1999. p. 155–64.Google Scholar
  49. Kubat M, Matwin S. Addressing the Curse of Imbalanced Training Sets: One-Sided Selection. In: Kaufmann M, editor. Proceedings of the 14th International conference on Machine Learning. 1997. p. 179–86.Google Scholar
  50. Ling CX, Li C. Data Mining for Direct Marketing : Problems and Solutions. In: Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining. New York: AAAI Press; 1998.Google Scholar
  51. Chawla NV, Bowyer KW, Hall LO. SMOTE: Synthetic Minority Over-sampling Technique. J Artif Intell Res. 2002;16:321–57.Google Scholar
  52. Breiman L, Cutler A, Liaw A, Wiener M. Breiman and Cutler’s random forests for classification and regression. 2015.Google Scholar
  53. Kohavi R. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. In: Kaufmann M, editor. International Joint Conference on Artificial Intelligence (IJCAI). 1995. p. 1137–43.Google Scholar
  54. Leo Breiman. Manual - Setting up, using, and udnerstanding random forests v4.0. https://www.stat.berkeley.edu/~breiman/Using_random_forests_v4.0.pdf.
  55. Boutros lab. HPCI. http://search.cpan.org/dist/HPCI/.
  56. Revolution Analytics. doMC: Foreach parallel adaptor for the multicore package. 2014.Google Scholar
  57. R Core Team. R: A language and environment for statistical computing. 2015.Google Scholar
  58. Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez J-C, Müller M. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinforma. 2011;18:77.View ArticleGoogle Scholar
  59. Lin LI. A Concordance Correlation Coefficient to Evaluate Reproducibility. Biometrics. 1989;45:255–68.View ArticlePubMedGoogle Scholar
  60. Sarkar D. Lattice: Multivariate Data Visualization with R. New York: Springer; 2008.View ArticleGoogle Scholar
  61. Sarkar D, Andrews F. latticeExtra: Extra Graphical Utilities Based on Lattice. 2013.Google Scholar
  62. Sun J, Zhao H. The application of sparse estimation of covariance matrix to quadratic discriminant analysis. BMC Bioinforma. 2015;16:48.View ArticleGoogle Scholar
  63. Shankar J, Szpakowski S, Solis NV, Mounaud S, Liu H, Losada L, Nierman WC, Filler SG. A systematic evaluation of high-dimensional, ensemble-based regression for exploring large model spaces in microbiome analyses. BMC Bioinforma. 2015;16:31.View ArticleGoogle Scholar
  64. Wu AC-Y, Rifkin SA. Aro: a machine learning approach to identifying single molecules and estimating classification error in fluorescence microscopy images. BMC Bioinforma. 2015;16:102.View ArticleGoogle Scholar
  65. Lee J, Lee K, Joung I, Joo K, Brooks BR, Lee J. Sigma-RF: prediction of the variability of spatial restraints in template-based modeling by random forest. BMC Bioinforma. 2015;16:94.View ArticleGoogle Scholar
  66. Limongelli I, Marini S, Bellazzi R. PaPI: pseudo amino acid composition to score human protein-coding variants. BMC Bioinforma. 2015;16:123.View ArticleGoogle Scholar
  67. Hofner B, Boccuto L, Göker M. Controlling false discoveries in high-dimensional situations: boosting with stability selection. BMC Bioinforma. 2015;16:144.View ArticleGoogle Scholar
  68. Fratello M, Serra A, Fortino V, Raiconi G, Tagliaferri R, Greco D. A multi-view genomic data simulator. BMC Bioinforma. 2015;16:151.View ArticleGoogle Scholar
  69. Ruiz-Blanco YB, Paz W, Green J, Marrero-Ponce Y. ProtDCal: A program to compute general-purpose-numerical descriptors for sequences and 3D-structures of proteins. BMC Bioinforma. 2015;16:162.View ArticleGoogle Scholar
  70. Sanders J, Singh A, Sterne G, Ye B, Zhou J. Learning-guided automatic three dimensional synapse quantification for drosophila neurons. BMC Bioinforma. 2015;16:177.View ArticleGoogle Scholar
  71. Schönenberger F, Deutzmann A, Ferrando-May E, Merhof D. Discrimination of cell cycle phases in PCNA-immunolabeled cells. BMC Bioinforma. 2015;16:180.View ArticleGoogle Scholar
  72. Novianti PW, Jong VL, Roes KCB, Eijkemans MJC. Factors affecting the accuracy of a class prediction model in gene expression data. BMC Bioinforma. 2015;16:199.View ArticleGoogle Scholar
  73. Cheng X, Cai H, Zhang Y, Xu B, Su W. Optimal combination of feature selection and classification via local hyperplane based learning strategy. BMC Bioinforma. 2015;16:219.View ArticleGoogle Scholar
  74. Ogoe HA, Visweswaran S, Lu X, Gopalakrishnan V. Knowledge transfer via classification rules using functional mapping for integrative modeling of gene expression data. BMC Bioinforma. 2015;16:226.View ArticleGoogle Scholar
  75. Kuhring M, Dabrowski PW, Piro VC, Nitsche A, Renard BY. SuRankCo: supervised ranking of contigs in de novo assemblies. BMC Bioinforma. 2015;16:240.View ArticleGoogle Scholar
  76. Khurana JK, Reeder JE, Shrimpton AE, Thakar J. GESPA: classifying nsSNPs to predict disease association. BMC Bioinforma. 2015;16:228.View ArticleGoogle Scholar
  77. Ren H, Shen Y. RNA-binding residues prediction using structural features. BMC Bioinforma. 2015;16:249.View ArticleGoogle Scholar
  78. Serra A, Fratello M, Fortino V, Raiconi G, Tagliaferri R, Greco D. MVDA: a multi-view genomic data integration methodology. BMC Bioinforma. 2015;16:261.View ArticleGoogle Scholar
  79. Korir PK, Geeleher P, Seoighe C. Seq-ing improved gene expression estimates from microarrays using machine learning. BMC Bioinforma. 2015;16:286.View ArticleGoogle Scholar
  80. Sakellariou A, Spyrou G. mAPKL: R/ Bioconductor package for detecting gene exemplars and revealing their characteristics. BMC Bioinforma. 2015;16:291.View ArticleGoogle Scholar
  81. Huang H, Fava A, Guhr T, Cimbro R, Rosen A, Boin F, Ellis H. A methodology for exploring biomarker-phenotype associations: application to flow cytometry data and systemic sclerosis clinical manifestations. BMC Bioinforma. 2015;16:293.View ArticleGoogle Scholar
  82. Blagus R, Lusa L. Boosting for high-dimensional two-class prediction. BMC Bioinforma. 2015;16:300.View ArticleGoogle Scholar
  83. Bellot P, Olsen C, Salembier P, Oliveras-Vergés A, Meyer PE. NetBenchmark: a bioconductor package for reproducible benchmarks of gene regulatory network inference. BMC Bioinforma. 2015;16:312.View ArticleGoogle Scholar
  84. König C, Cárdenas MI, Giraldo J, Alquézar R, Vellido A. Label noise in subtype discrimination of class C G protein-coupled receptors: A systematic approach to the analysis of classification errors. BMC Bioinforma. 2015;16:314.View ArticleGoogle Scholar
  85. Cremona MA, Sangalli LM, Vantini S, Dellino GI, Pelicci PG, Secchi P, Riva L. Peak shape clustering reveals biological insights. BMC Bioinforma. 2015;16:349.View ArticleGoogle Scholar
  86. Ditzler G, Morrison JC, Lan Y, Rosen GL. Fizzy: feature subset selection for metagenomics. BMC Bioinforma. 2015;16:358.View ArticleGoogle Scholar
  87. Landoni E, Miceli R, Callari M, Tiberio P, Appierto V, Angeloni V, Mariani L, Daidone MG. Proposal of supervised data analysis strategy of plasma miRNAs from hybridisation array data with an application to assess hemolysis-related deregulation. BMC Bioinforma. 2015;16:388.View ArticleGoogle Scholar

Copyright

© The Author(s). 2016

Advertisement