AIMC Topic: Computational Biology

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DeePhafier: a phage lifestyle classifier using a multilayer self-attention neural network combining protein information.

Briefings in bioinformatics
Bacteriophages are the viruses that infect bacterial cells. They are the most diverse biological entities on earth and play important roles in microbiome. According to the phage lifestyle, phages can be divided into the virulent phages and the temper...

BPP: a platform for automatic biochemical pathway prediction.

Briefings in bioinformatics
A biochemical pathway consists of a series of interconnected biochemical reactions to accomplish specific life activities. The participating reactants and resultant products of a pathway, including gene fragments, proteins, and small molecules, coale...

Integrated multi-omics with machine learning to uncover the intricacies of kidney disease.

Briefings in bioinformatics
The development of omics technologies has driven a profound expansion in the scale of biological data and the increased complexity in internal dimensions, prompting the utilization of machine learning (ML) as a powerful toolkit for extracting knowled...

MetaPredictor: in silico prediction of drug metabolites based on deep language models with prompt engineering.

Briefings in bioinformatics
Metabolic processes can transform a drug into metabolites with different properties that may affect its efficacy and safety. Therefore, investigation of the metabolic fate of a drug candidate is of great significance for drug discovery. Computational...

From data to discovery: AI-guided analysis of disease-relevant molecules in spinal muscular atrophy (SMA).

Human molecular genetics
Spinal Muscular Atrophy is caused by partial loss of survival of motoneuron (SMN) protein expression. The numerous interaction partners and mechanisms influenced by SMN loss result in a complex disease. Current treatments restore SMN protein levels t...

Identification and validation of potential genes for the diagnosis of sepsis by bioinformatics and 2-sample Mendelian randomization study.

Medicine
This integrated study combines bioinformatics, machine learning, and Mendelian randomization (MR) to discover and validate molecular biomarkers for sepsis diagnosis. Methods include differential expression analysis, weighted gene co-expression networ...

A comprehensive survey on deep learning-based identification and predicting the interaction mechanism of long non-coding RNAs.

Briefings in functional genomics
Long noncoding RNAs (lncRNAs) have been discovered to be extensively involved in eukaryotic epigenetic, transcriptional, and post-transcriptional regulatory processes with the advancements in sequencing technology and genomics research. Therefore, th...

DeepPRMS: advanced deep learning model to predict protein arginine methylation sites.

Briefings in functional genomics
Protein methylation is a form of post-translational modifications of protein, which is crucial for various cellular processes, including transcription activity and DNA repair. Correctly predicting protein methylation sites is fundamental for research...

THGNCDA: circRNA-disease association prediction based on triple heterogeneous graph network.

Briefings in functional genomics
Circular RNAs (circRNAs) are a class of noncoding RNA molecules featuring a closed circular structure. They have been proved to play a significant role in the reduction of many diseases. Besides, many researches in clinical diagnosis and treatment of...

Deep learning for the PSIPRED Protein Analysis Workbench.

Nucleic acids research
The PSIRED Workbench is a long established and popular bioinformatics web service offering a wide range of machine learning based analyses for characterizing protein structure and function. In this paper we provide an update of the recent additions a...