AIMC Topic: Computational Biology

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Deep-learning models reveal how context and listener attention shape electrophysiological correlates of speech-to-language transformation.

PLoS computational biology
To transform continuous speech into words, the human brain must resolve variability across utterances in intonation, speech rate, volume, accents and so on. A promising approach to explaining this process has been to model electroencephalogram (EEG) ...

Teaching deep networks to see shape: Lessons from a simplified visual world.

PLoS computational biology
Deep neural networks have been remarkably successful as models of the primate visual system. One crucial problem is that they fail to account for the strong shape-dependence of primate vision. Whereas humans base their judgements of category membersh...

Improving drug-target interaction prediction through dual-modality fusion with InteractNet.

Journal of bioinformatics and computational biology
In the drug discovery process, accurate prediction of drug-target interactions is crucial to accelerate the development of new drugs. However, existing methods still face many challenges in dealing with complex biomolecular interactions. To this end,...

LGS-PPIS: A Local-Global Structural Information Aggregation Framework for Predicting Protein-Protein Interaction Sites.

Proteins
Exploring protein-protein interaction sites (PPIS) is of significance to elucidating the intrinsic mechanisms of diverse biological processes. On this basis, recent studies have applied deep learning-based technologies to overcome the high cost of we...

Interpretation knowledge extraction for genetic testing via question-answer model.

BMC genomics
BACKGROUND: Sequencing-based genetic testing is widely used in biomedical research, including pathogenic microorganism detection with metagenomic next-generation sequencing (mNGS). The application of sequencing results to clinical diagnosis and treat...

Predicting viral proteins that evade the innate immune system: a machine learning-based immunoinformatics tool.

BMC bioinformatics
Viral proteins that evade the host's innate immune response play a crucial role in pathogenesis, significantly impacting viral infections and potential therapeutic strategies. Identifying these proteins through traditional methods is challenging and ...

Immunoinformatics investigation on pathogenic Escherichia coli proteome to develop an epitope-based peptide vaccine candidate.

Molecular diversity
Escherichia coli (E. coli), a gram-negative bacterium, quickly colonizes in the human gastrointestinal tract after birth and typically sustains a long-term, symbiotic relationship with the host. However, certain virulent strains of E. coli can cause ...

Deep-GB: A novel deep learning model for globular protein prediction using CNN-BiLSTM architecture and enhanced PSSM with trisection strategy.

IET systems biology
Globular proteins (GPs) play vital roles in a wide range of biological processes, encompassing enzymatic catalysis and immune responses. Enzymes, among these globular proteins, facilitate biochemical reactions, while others, such as haemoglobin, cont...

Graph-based machine learning model for weight prediction in protein-protein networks.

BMC bioinformatics
Proteins interact with each other in complex ways to perform significant biological functions. These interactions, known as protein-protein interactions (PPIs), can be depicted as a graph where proteins are nodes and their interactions are edges. The...

A deep learning model for prediction of autism status using whole-exome sequencing data.

PLoS computational biology
Autism is a developmental disability. Research demonstrated that children with autism benefit from early diagnosis and early intervention. Genetic factors are considered major contributors to the development of autism. Machine learning (ML), includin...