AIMC Topic: Computational Biology

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POSSUM: a bioinformatics toolkit for generating numerical sequence feature descriptors based on PSSM profiles.

Bioinformatics (Oxford, England)
SUMMARY: Evolutionary information in the form of a Position-Specific Scoring Matrix (PSSM) is a widely used and highly informative representation of protein sequences. Accordingly, PSSM-based feature descriptors have been successfully applied to impr...

HPSLPred: An Ensemble Multi-Label Classifier for Human Protein Subcellular Location Prediction with Imbalanced Source.

Proteomics
Predicting the subcellular localization of proteins is an important and challenging problem. Traditional experimental approaches are often expensive and time-consuming. Consequently, a growing number of research efforts employ a series of machine lea...

An efficient graph kernel method for non-coding RNA functional prediction.

Bioinformatics (Oxford, England)
MOTIVATION: The importance of RNA protein-coding gene regulation is by now well appreciated. Non-coding RNAs (ncRNAs) are known to regulate gene expression at practically every stage, ranging from chromatin packaging to mRNA translation. However the ...

modlAMP: Python for antimicrobial peptides.

Bioinformatics (Oxford, England)
SUMMARY: We have implemented the lecular esign aboratory's nti icrobial eptides package ( ), a Python-based software package for the design, classification and visual representation of peptide data. modlAMP offers functions for molecular descriptor c...

A deep learning framework for improving long-range residue-residue contact prediction using a hierarchical strategy.

Bioinformatics (Oxford, England)
MOTIVATION: Residue-residue contacts are of great value for protein structure prediction, since contact information, especially from those long-range residue pairs, can significantly reduce the complexity of conformational sampling for protein struct...

Neuro-symbolic representation learning on biological knowledge graphs.

Bioinformatics (Oxford, England)
MOTIVATION: Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries. In the past years, feature learning methods that are applicable to g...

Deep learning in bioinformatics.

Briefings in bioinformatics
In the era of big data, transformation of biomedical big data into valuable knowledge has been one of the most important challenges in bioinformatics. Deep learning has advanced rapidly since the early 2000s and now demonstrates state-of-the-art perf...

A deep boosting based approach for capturing the sequence binding preferences of RNA-binding proteins from high-throughput CLIP-seq data.

Nucleic acids research
Characterizing the binding behaviors of RNA-binding proteins (RBPs) is important for understanding their functional roles in gene expression regulation. However, current high-throughput experimental methods for identifying RBP targets, such as CLIP-s...

SVMQA: support-vector-machine-based protein single-model quality assessment.

Bioinformatics (Oxford, England)
MOTIVATION: The accurate ranking of predicted structural models and selecting the best model from a given candidate pool remain as open problems in the field of structural bioinformatics. The quality assessment (QA) methods used to address these prob...

Removal of batch effects using distribution-matching residual networks.

Bioinformatics (Oxford, England)
MOTIVATION: Sources of variability in experimentally derived data include measurement error in addition to the physical phenomena of interest. This measurement error is a combination of systematic components, originating from the measuring instrument...