AIMC Topic: Computational Biology

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DEMO2: Assemble multi-domain protein structures by coupling analogous template alignments with deep-learning inter-domain restraint prediction.

Nucleic acids research
Most proteins in nature contain multiple folding units (or domains). The revolutionary success of AlphaFold2 in single-domain structure prediction showed potential to extend deep-learning techniques for multi-domain structure modeling. This work pres...

Scoring protein sequence alignments using deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: A high-quality sequence alignment (SA) is the most important input feature for accurate protein structure prediction. For a protein sequence, there are many methods to generate a SA. However, when given a choice of more than one SA for a ...

Robust Random Forest-Based All-Relevant Feature Ranks for Trustworthy AI.

Studies in health technology and informatics
Feature selection is a fundamental challenge in machine learning. For instance in bioinformatics, it is essential when one wishes to detect biomarkers. Tree-based methods are predominantly used for this purpose. In this paper, we study the stability ...

Deep learning tools are top performers in long non-coding RNA prediction.

Briefings in functional genomics
The increasing amount of transcriptomic data has brought to light vast numbers of potential novel RNA transcripts. Accurately distinguishing novel long non-coding RNAs (lncRNAs) from protein-coding messenger RNAs (mRNAs) has challenged bioinformatic ...

A review of machine learning approaches for drug synergy prediction in cancer.

Briefings in bioinformatics
Combinational pharmacotherapy with the synergistic/additive effect is a powerful treatment strategy for complex diseases such as malignancies. Identifying synergistic combinations with various compounds and structures requires testing a large number ...

SynPathy: Predicting Drug Synergy through Drug-Associated Pathways Using Deep Learning.

Molecular cancer research : MCR
UNLABELLED: Drug combination therapy has become a promising therapeutic strategy for cancer treatment. While high-throughput drug combination screening is effective for identifying synergistic drug combinations, measuring all possible combinations is...

Integration of machine learning with computational structural biology of plants.

The Biochemical journal
Computational structural biology of proteins has developed rapidly in recent decades with the development of new computational tools and the advancement of computing hardware. However, while these techniques have widely been used to make advancements...

TransformerGO: predicting protein-protein interactions by modelling the attention between sets of gene ontology terms.

Bioinformatics (Oxford, England)
MOTIVATION: Protein-protein interactions (PPIs) play a key role in diverse biological processes but only a small subset of the interactions has been experimentally identified. Additionally, high-throughput experimental techniques that detect PPIs are...

GMNN2CD: identification of circRNA-disease associations based on variational inference and graph Markov neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: With the analysis of the characteristic and function of circular RNAs (circRNAs), people have realized that they play a critical role in the diseases. Exploring the relationship between circRNAs and diseases is of far-reaching significanc...

PST-PRNA: prediction of RNA-binding sites using protein surface topography and deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: Protein-RNA interactions play essential roles in many biological processes, including pre-mRNA processing, post-transcriptional gene regulation and RNA degradation. Accurate identification of binding sites on RNA-binding proteins (RBPs) i...