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Machine Learning and Artificial Intelligence in Bioinformatics

Section edited by Jean-Philippe Vert

This section covers recent advances in machine learning and artificial intelligence methods, including their applications to problems in bioinformatics. It considers manuscripts describing novel computational techniques to analyse high throughput data such as sequences and gene/protein expressions, as well as machine learning techniques such as graphical models, neural networks or kernel methods.

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  1. Machine learning models for repeated measurements are limited. Using topological data analysis (TDA), we present a classifier for repeated measurements which samples from the data space and builds a network gr...

    Authors: Henri Riihimäki, Wojciech Chachólski, Jakob Theorell, Jan Hillert and Ryan Ramanujam

    Citation: BMC Bioinformatics 2020 21:336

    Content type: Research article

    Published on:

  2. Gene expression signatures for the prediction of differential survival of patients undergoing anti-cancer therapies are of great interest because they can be used to prospectively stratify patients entering ne...

    Authors: Joachim Theilhaber, Marielle Chiron, Jennifer Dreymann, Donald Bergstrom and Jack Pollard

    Citation: BMC Bioinformatics 2020 21:333

    Content type: Methodology article

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  3. Drug repurposing aims to detect the new therapeutic benefits of the existing drugs and reduce the spent time and cost of the drug development projects. The synthetic repurposing of drugs may prove to be more u...

    Authors: Yosef Masoudi-Sobhanzadeh and Ali Masoudi-Nejad

    Citation: BMC Bioinformatics 2020 21:313

    Content type: Methodology article

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  4. Most biomedical information extraction focuses on binary relations within single sentences. However, extracting n-ary relations that span multiple sentences is in huge demand. At present, in the cross-sentence...

    Authors: Di Zhao, Jian Wang, Yijia Zhang, Xin Wang, Hongfei Lin and Zhihao Yang

    Citation: BMC Bioinformatics 2020 21:312

    Content type: Methodology article

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  5. Random forest based variable importance measures have become popular tools for assessing the contributions of the predictor variables in a fitted random forest. In this article we reconsider a frequently used ...

    Authors: Dries Debeer and Carolin Strobl

    Citation: BMC Bioinformatics 2020 21:307

    Content type: Methodology article

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  6. A common yet still manual task in basic biology research, high-throughput drug screening and digital pathology is identifying the number, location, and type of individual cells in images. Object detection meth...

    Authors: Jane Hung, Allen Goodman, Deepali Ravel, Stefanie C. P. Lopes, Gabriel W. Rangel, Odailton A. Nery, Benoit Malleret, Francois Nosten, Marcus V. G. Lacerda, Marcelo U. Ferreira, Laurent Rénia, Manoj T. Duraisingh, Fabio T. M. Costa, Matthias Marti and Anne E. Carpenter

    Citation: BMC Bioinformatics 2020 21:300

    Content type: Software

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  7. Even though we have established a few risk factors for metastatic breast cancer (MBC) through epidemiologic studies, these risk factors have not proven to be effective in predicting an individual’s risk of develo...

    Authors: Xia Jiang, Alan Wells, Adam Brufsky, Darshan Shetty, Kahmil Shajihan and Richard E. Neapolitan

    Citation: BMC Bioinformatics 2020 21:298

    Content type: Methodology article

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  8. Phytochemicals and other molecules in foods elicit positive health benefits, often by poorly established or unknown mechanisms. While there is a wealth of data on the biological and biophysical properties of d...

    Authors: Kenneth E. Westerman, Sean Harrington, Jose M. Ordovas and Laurence D. Parnell

    Citation: BMC Bioinformatics 2020 21:238

    Content type: Software

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  9. The interactions between proteins and aptamers are prevalent in organisms and play an important role in various life activities. Thanks to the rapid accumulation of protein-aptamer interaction data, it is nece...

    Authors: Jianwei Li, Xiaoyu Ma, Xichuan Li and Junhua Gu

    Citation: BMC Bioinformatics 2020 21:236

    Content type: Methodology article

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  10. The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are avai...

    Authors: Hesham ElAbd, Yana Bromberg, Adrienne Hoarfrost, Tobias Lenz, Andre Franke and Mareike Wendorff

    Citation: BMC Bioinformatics 2020 21:235

    Content type: Methodology article

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  11. Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma and accounts for cancer-related deaths. Survival rates are very low when the tumor is discovered in the late-stage. Th...

    Authors: Fangjun Li, Mu Yang, Yunhe Li, Mingqiang Zhang, Wenjuan Wang, Dongfeng Yuan and Dongqi Tang

    Citation: BMC Bioinformatics 2020 21:232

    Content type: Research article

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  12. Inferring diseases related to the patient’s electronic medical records (EMRs) is of great significance for assisting doctor diagnosis. Several recent prediction methods have shown that deep learning-based meth...

    Authors: Tong Wang, Ping Xuan, Zonglin Liu and Tiangang Zhang

    Citation: BMC Bioinformatics 2020 21:230

    Content type: Methodology article

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  13. The latest works on CRISPR genome editing tools mainly employs deep learning techniques. However, deep learning models lack explainability and they are harder to reproduce. We were motivated to build an accura...

    Authors: Ali Haisam Muhammad Rafid, Md. Toufikuzzaman, Mohammad Saifur Rahman and M. Sohel Rahman

    Citation: BMC Bioinformatics 2020 21:223

    Content type: Methodology Article

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  14. Enzymatic and chemical reactions are key for understanding biological processes in cells. Curated databases of chemical reactions exist but these databases struggle to keep up with the exponential growth of th...

    Authors: Emily K. Mallory, Matthieu de Rochemonteix, Alex Ratner, Ambika Acharya, Chris Re, Roselie A. Bright and Russ B. Altman

    Citation: BMC Bioinformatics 2020 21:217

    Content type: Research article

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  15. Semantic resources such as knowledge bases contains high-quality-structured knowledge and therefore require significant effort from domain experts. Using the resources to reinforce the information retrieval fr...

    Authors: Zhijing Li, Yuchen Lian, Xiaoyong Ma, Xiangrong Zhang and Chen Li

    Citation: BMC Bioinformatics 2020 21:213

    Content type: Methodology article

    Published on:

  16. Apoptosis, also called programmed cell death, refers to the spontaneous and orderly death of cells controlled by genes in order to maintain a stable internal environment. Identifying the subcellular location o...

    Authors: Lei Du, Qingfang Meng, Yuehui Chen and Peng Wu

    Citation: BMC Bioinformatics 2020 21:212

    Content type: Methodology Article

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  17. The aim of gene expression-based clinical modelling in tumorigenesis is not only to accurately predict the clinical endpoints, but also to reveal the genome characteristics for downstream analysis for the purp...

    Authors: Yiru Zhao, Yifan Zhou, Yuan Liu, Yinyi Hao, Menglong Li, Xuemei Pu, Chuan Li and Zhining Wen

    Citation: BMC Bioinformatics 2020 21:195

    Content type: Methodology article

    Published on:

  18. In addition to causing the pandemic influenza outbreaks of 1918 and 2009, subtype H1N1 influenza A viruses (IAVs) have caused seasonal epidemics since 1977. Antigenic property of influenza viruses are determin...

    Authors: Lei Li, Deborah Chang, Lei Han, Xiaojian Zhang, Joseph Zaia and Xiu-Feng Wan

    Citation: BMC Bioinformatics 2020 21:182

    Content type: Methodology article

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  19. Recently, DNA methylation has drawn great attention due to its strong correlation with abnormal gene activities and informative representation of the cancer status. As a number of studies focus on DNA methylat...

    Authors: Joungmin Choi and Heejoon Chae

    Citation: BMC Bioinformatics 2020 21:181

    Content type: Research Article

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  20. While clinical trials are considered the gold standard for detecting adverse events, often these trials are not sufficiently powered to detect difficult to observe adverse events. We developed a preliminary ap...

    Authors: Chathuri Daluwatte, Peter Schotland, David G. Strauss, Keith K. Burkhart and Rebecca Racz

    Citation: BMC Bioinformatics 2020 21:163

    Content type: Research article

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  21. Recent years have witnessed an increasing interest in multi-omics data, because these data allow for better understanding complex diseases such as cancer on a molecular system level. In addition, multi-omics d...

    Authors: Amina Lemsara, Salima Ouadfel and Holger Fröhlich

    Citation: BMC Bioinformatics 2020 21:146

    Content type: Methodology article

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  22. Feature selection in class-imbalance learning has gained increasing attention in recent years due to the massive growth of high-dimensional class-imbalanced data across many scientific fields. In addition to r...

    Authors: Guang-Hui Fu, Yuan-Jiao Wu, Min-Jie Zong and Jianxin Pan

    Citation: BMC Bioinformatics 2020 21:121

    Content type: Research Article

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  23. The ability to confidently predict health outcomes from gene expression would catalyze a revolution in molecular diagnostics. Yet, the goal of developing actionable, robust, and reproducible predictive signatu...

    Authors: Aaron M. Smith, Jonathan R. Walsh, John Long, Craig B. Davis, Peter Henstock, Martin R. Hodge, Mateusz Maciejewski, Xinmeng Jasmine Mu, Stephen Ra, Shanrong Zhao, Daniel Ziemek and Charles K. Fisher

    Citation: BMC Bioinformatics 2020 21:119

    Content type: Research Article

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  24. MicroRNA (miRNA) regulation is associated with several diseases, including neurodegenerative diseases. Several approaches can be used for modeling miRNA regulation. However, their precision may be limited for ...

    Authors: Lucile Mégret, Satish Sasidharan Nair, Julia Dancourt, Jeff Aaronson, Jim Rosinski and Christian Neri

    Citation: BMC Bioinformatics 2020 21:75

    Content type: Research article

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  25. The study of functional associations between ncRNAs and human diseases is a pivotal task of modern research to develop new and more effective therapeutic approaches. Nevertheless, it is not a trivial task sinc...

    Authors: Emanuele Pio Barracchia, Gianvito Pio, Domenica D’Elia and Michelangelo Ceci

    Citation: BMC Bioinformatics 2020 21:70

    Content type: Methodology Article

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  26. Genome-wide association studies (GWAS) provide a powerful means to identify associations between genetic variants and phenotypes. However, GWAS techniques for detecting epistasis, the interactions between gene...

    Authors: Yu-Chuan Chang, June-Tai Wu, Ming-Yi Hong, Yi-An Tung, Ping-Han Hsieh, Sook Wah Yee, Kathleen M. Giacomini, Yen-Jen Oyang and Chien-Yu Chen

    Citation: BMC Bioinformatics 2020 21:68

    Content type: Software

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  27. Single-cell RNA sequencing (scRNA-seq) is an emerging technology that can assess the function of an individual cell and cell-to-cell variability at the single cell level in an unbiased manner. Dimensionality r...

    Authors: Eugene Lin, Sudipto Mukherjee and Sreeram Kannan

    Citation: BMC Bioinformatics 2020 21:64

    Content type: Methodology article

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  28. Feature selection is a crucial step in machine learning analysis. Currently, many feature selection approaches do not ensure satisfying results, in terms of accuracy and computational time, when the amount of ...

    Authors: Mattia Chiesa, Giada Maioli, Gualtiero I. Colombo and Luca Piacentini

    Citation: BMC Bioinformatics 2020 21:54

    Content type: Software

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  29. Various methods for differential expression analysis have been widely used to identify features which best distinguish between different categories of samples. Multiple hypothesis testing may leave out explana...

    Authors: Xudong Zhao, Qing Jiao, Hangyu Li, Yiming Wu, Hanxu Wang, Shan Huang and Guohua Wang

    Citation: BMC Bioinformatics 2020 21:43

    Content type: Software

    Published on:

  30. Automated biomedical named entity recognition and normalization serves as the basis for many downstream applications in information management. However, this task is challenging due to name variations and enti...

    Authors: Huiwei Zhou, Shixian Ning, Zhe Liu, Chengkun Lang, Zhuang Liu and Bizun Lei

    Citation: BMC Bioinformatics 2020 21:35

    Content type: Research article

    Published on:

2019 Journal Metrics

  • Citation Impact
    3.242 - 2-year Impact Factor
    3.213 - 5-year Impact Factor
    1.156 - Source Normalized Impact per Paper (SNIP)
    1.626 - SCImago Journal Rank (SJR)

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