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Computational drug repositioning, which aims to find new applications for existing drugs, is gaining more attention from the pharmaceutical companies due to its low attrition rate, reduced cost, and shorter ti...
One of the main issues in the automated protein function prediction (AFP) problem is the integration of multiple networked data sources. The UNIPred algorithm was thereby proposed to efficiently integrate —in ...
Ultra-fast pseudo-alignment approaches are the tool of choice in transcript-level RNA sequencing (RNA-seq) analyses. Unfortunately, these methods couple the tasks of pseudo-alignment and transcript quantificat...
Lineage rate heterogeneity can be a major source of bias, especially in multi-gene phylogeny inference. We had previously tackled this issue by developing LS3, a data subselection algorithm that, by removing fast...
Alignment of sequence families described by profiles provides a sensitive means for establishing homology between proteins and is important in protein evolutionary, structural, and functional studies. In the c...
Standard RNAseq methods using bulk RNA and recent single-cell RNAseq methods use DNA barcodes to identify samples and cells, and the barcoded cDNAs are pooled into a library pool before high throughput sequenc...
The development of high throughput sequencing techniques provides us with the possibilities to obtain large data sets, which capture the effect of dynamic perturbations on cellular processes. However, because ...
Predicting the effect of drug-drug interactions (DDIs) precisely is important for safer and more effective drug co-prescription. Many computational approaches to predict the effect of DDIs have been proposed, ...
R-loops are three-stranded nucleic acid structures that usually form during transcription and that may lead to gene regulation or genome instability. DRIP (DNA:RNA Immunoprecipitation)-seq techniques are widel...
Cartilage damage is a crucial feature involved in several pathological conditions characterized by joint disorders, such as osteoarthritis and rheumatoid arthritis. Accumulated evidences showed that Wnt/β-cate...
Linear mixed-effects models (LMM) are a leading method in conducting genome-wide association studies (GWAS) but require residual maximum likelihood (REML) estimation of variance components, which is computatio...
Antiretroviral drugs are a very effective therapy against HIV infection. However, the high mutation rate of HIV permits the emergence of variants that can be resistant to the drug treatment. Predicting drug re...
Internal ribosome entry sites (IRES) are segments of mRNA found in untranslated regions that can recruit the ribosome and initiate translation independently of the 5′ cap-dependent translation initiation mecha...
Understanding the phenotypic drug response on cancer cell lines plays a vital role in anti-cancer drug discovery and re-purposing. The Genomics of Drug Sensitivity in Cancer (GDSC) database provides open data ...
Ontologies are invaluable in the life sciences, but building and maintaining ontologies often requires a challenging number of distinct tasks such as running automated reasoners and quality control checks, ext...
Next-generation sequencing technologies can produce tens of millions of reads, often paired-end, from transcripts or genomes. But few programs can align RNA on the genome and accurately discover introns, espec...
It has been shown that the deregulation of miRNAs is associated with the development and progression of many human diseases. To reduce time and cost of biological experiments, a number of algorithms have been ...
The development of next generation sequencer (NGS) and the analytical methods allowed the researchers to profile their samples more precisely and easier than before. Especially for agriculture, the certificati...
This article is part of a Supplement: Volume 20 Supplement 13
Drug repurposing has been motivated to ameliorate low probability of success in drug discovery. For the recent decade, many in silico attempts have received primary attention as a first step to alleviate the h...
This article is part of a Supplement: Volume 20 Supplement 13
Pathogenic protist membrane transporter proteins play important roles not only in exchanging molecules into and out of cells but also in acquiring nutrients and biosynthetic compounds from their hosts. Current...
This article is part of a Supplement: Volume 20 Supplement 13
How can we obtain fast and high-quality clusters in genome scale bio-networks? Graph clustering is a powerful tool applied on bio-networks to solve various biological problems such as protein complexes detecti...
This article is part of a Supplement: Volume 20 Supplement 13
Automatically understanding chemical-disease relations (CDRs) is crucial in various areas of biomedical research and health care. Supervised machine learning provides a feasible solution to automatically extra...
Today a variety of phylogenetic file formats exists, some of which are well-established but limited in their data model, while other more recently introduced ones offer advanced features for metadata represent...
Visualization tools for deep learning models typically focus on discovering key input features without considering how such low level features are combined in intermediate layers to make decisions. Moreover, m...
The CATH database provides a hierarchical classification of protein domain structures including a sub-classification of superfamilies into functional families (FunFams). We analyzed the similarity of binding site...
High-throughput experiments can bring to light associations between genes, proteins and/or metabolites, many of which will be explainable by existing knowledge. Our aim is to speed elucidation of such explanat...
Utilization of quantitative proteomics data on the network level is still a challenge in proteomics data analysis. Currently existing models use sophisticated, sometimes hard to implement analysis techniques. ...
Tandem mass spectrometry (MS/MS)-based database searching is a widely acknowledged and widely used method for peptide identification in shotgun proteomics. However, due to the rapid growth of spectra data prod...
Since the number of known lncRNA-disease associations verified by biological experiments is quite limited, it has been a challenging task to uncover human disease-related lncRNAs in recent years. Moreover, con...
Ordinary differential equation systems are frequently utilized to model biological systems and to infer knowledge about underlying properties. For instance, the development of drugs requires the knowledge to w...
High-dimensional data of discrete and skewed nature is commonly encountered in high-throughput sequencing studies. Analyzing the network itself or the interplay between genes in this type of data continues to ...
MicroRNAs (miRNAs) are small RNAs that regulate gene expression at a post-transcriptional level and are emerging as potentially important biomarkers for various disease states, including pancreatic cancer. In ...
Clustering methods are essential to partitioning biological samples being useful to minimize the information complexity in large datasets. Tools in this context usually generates data with greed algorithms tha...
Asymmetry during cellular division, both in the uneven partitioning of damaged cellular components and of cell volume, is a cell biological phenomenon experienced by many unicellular organisms. Previous work b...
Human papillomavirus (HPV) is a common sexually transmitted infection associated with cervical cancer that frequently occurs as a coinfection of types and subtypes. Highly similar sublineages that show over 10...
Single-cell RNA-sequencing technologies provide a powerful tool for systematic dissection of cellular heterogeneity. However, the prevalence of dropout events imposes complications during data analysis and, de...
Bioinformatics methods are helpful to identify new molecules for diagnostic or therapeutic applications. For example, the use of peptides capable of mimicking binding sites has several benefits in replacing a ...
Mathematical models of biological networks can provide important predictions and insights into complex disease. Constraint-based models of cellular metabolism and probabilistic models of gene regulatory networ...
In cancer research, robustness of a complex biochemical network is one of the most relevant properties to investigate for the development of novel targeted therapies. In cancer systems biology, biological netw...
Alkaloids, a class of organic compounds that contain nitrogen bases, are mainly synthesized as secondary metabolites in plants and fungi, and they have a wide range of bioactivities. Although there are thousan...
Unsupervised machine learning methods (deep learning) have shown their usefulness with noisy single cell mRNA-sequencing data (scRNA-seq), where the models generalize well, despite the zero-inflation of the da...
The QuantiGene® Plex 2.0 platform (ThermoFisher Scientific) combines bDNA with the Luminex/xMAP magnetic bead capturing technology to assess differential gene expression in a compound exposure setting. This te...
Electron transport chain is a series of protein complexes embedded in the process of cellular respiration, which is an important process to transfer electrons and other macromolecules throughout the cell. It i...
Molecule identification is a crucial step in metabolomics and environmental sciences. Besides in silico fragmentation, as performed by MetFrag, also machine learning and statistical methods evolved, showing an...
Cells operate in an uncertain environment, where critical cell decisions must be enacted in the presence of biochemical noise. Information theory can measure the extent to which such noise perturbs normal cell...
One of the major challenges facing investigators in the microbiome field is turning large numbers of reads generated by next-generation sequencing (NGS) platforms into biological knowledge. Effective analytica...
RNA molecules play many crucial roles in living systems. The spatial complexity that exists in RNA structures determines their cellular functions. Therefore, understanding RNA folding conformations, in particu...
Predicting the effect of single point variations on protein stability constitutes a crucial step toward understanding the relationship between protein structure and function. To this end, several methods have ...
This article is part of a Supplement: Volume 20 Supplement 14
The studies of functions of circular RNAs (circRNAs) are heavily focused on the regulation of gene expression through interactions with multiple miRNAs. However, the number of predicted target genes is typical...
The falling cost of next-generation sequencing technology has allowed deep sequencing across related species and of individuals within species. Whole genome assemblies from these data remain high time- and res...
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