AIMC Topic: Computational Biology

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Learning protein binding affinity using privileged information.

BMC bioinformatics
BACKGROUND: Determining protein-protein interactions and their binding affinity are important in understanding cellular biological processes, discovery and design of novel therapeutics, protein engineering, and mutagenesis studies. Due to the time an...

Machine Learning Distinguishes with High Accuracy between Pan-Assay Interference Compounds That Are Promiscuous or Represent Dark Chemical Matter.

Journal of medicinal chemistry
Assay interference compounds give rise to false-positives and cause substantial problems in medicinal chemistry. Nearly 500 compound classes have been designated as pan-assay interference compounds (PAINS), which typically occur as substructures in o...

Two New Heuristic Methods for Protein Model Quality Assessment.

IEEE/ACM transactions on computational biology and bioinformatics
Protein tertiary structure prediction is an important open challenge in bioinformatics and requires effective methods to accurately evaluate the quality of protein 3-D models generated computationally. Many quality assessment (QA) methods have been p...

Modeling and Predicting the Activities of Trans-Acting Splicing Factors with Machine Learning.

Cell systems
Alternative splicing (AS) is generally regulated by trans-splicing factors that specifically bind to cis-elements in pre-mRNAs. The human genome encodes ∼1,500 RNA binding proteins (RBPs) that potentially regulate AS, yet their functions remain large...

A Deep Neural Network Model using Random Forest to Extract Feature Representation for Gene Expression Data Classification.

Scientific reports
In predictive model development, gene expression data is associated with the unique challenge that the number of samples (n) is much smaller than the amount of features (p). This "n ≪ p" property has prevented classification of gene expression data f...

Prediction of LncRNA Subcellular Localization with Deep Learning from Sequence Features.

Scientific reports
Long non-coding RNAs are involved in biological processes throughout the cell including the nucleus, chromatin and cytosol. However, most lncRNAs remain unannotated and functional annotation of lncRNAs is difficult due to their low conservation and t...

Machine learning predicts individual cancer patient responses to therapeutic drugs with high accuracy.

Scientific reports
Precision or personalized cancer medicine is a clinical approach that strives to customize therapies based upon the genomic profiles of individual patient tumors. Machine learning (ML) is a computational method particularly suited to the establishmen...

Introducing Heuristic Information Into Ant Colony Optimization Algorithm for Identifying Epistasis.

IEEE/ACM transactions on computational biology and bioinformatics
Epistasis learning, which is aimed at detecting associations between multiple Single Nucleotide Polymorphisms (SNPs) and complex diseases, has gained increasing attention in genome wide association studies. Although much work has been done on mapping...

Using Unlabeled Data to Discover Bivariate Causality with Deep Restricted Boltzmann Machines.

IEEE/ACM transactions on computational biology and bioinformatics
An important question in microbiology is whether treatment causes changes in gut flora, and whether it also affects metabolism. The reconstruction of causal relations purely from non-temporal observational data is challenging. We address the problem ...