AIMC Topic: Computational Biology

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ESAE-SDA: ensemble sparse autoencoder framework for epigenomics-informed snoRNA-disease associations prediction.

BMC bioinformatics
Small nucleolar RNAs (snoRNAs), a class of non-coding RNAs broadly distributed in eukaryotes, are emerging as pivotal regulators in the field of epigenomics. In addition to guiding 2'-O-methylation and pseudouridylation modifications at specific rRNA...

ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling.

Nature communications
Accurate detection of somatic variants in tumors is of critical importance and remains challenging. Current methods typically require matched normal samples for reliable detection, which are often unavailable in real-world research and clinical scena...

Identification of potential biomarkers for Lyme disease using bioinformatics and machine learning.

Clinical and experimental medicine
Lyme disease (LD) presents significant diagnostic challenges due to the absence of a reliable screening method for initial detection. This study aimed to identify potential biomarkers using bioinformatics and machine learning algorithms, which may co...

Natural language processing of gene descriptions for overrepresentation analysis with GeneTEA.

Genome biology
Overrepresentation analysis is used to identify biological enrichment in a list of genes. Here, we introduce GeneTEA, a model that ingests free-text gene descriptions and incorporates natural language processing methods to learn a sparse gene-by-term...

A novel prediction method for protein-DNA binding sites based on protein language model fusion features with SE-connection pyramidal network and ensemble learning.

BMC genomics
Protein-DNA interactions are crucial in life processes such as gene expression and regulation. Therefore, the accurate prediction of DNA-binding sites on proteins is highly important for the advancement of scientific understanding in the field of bio...

MDG-DDI: multi-feature drug graph for drug-drug interaction prediction.

BMC bioinformatics
BACKGROUND: Drug-drug interactions (DDIs) frequently occur in combination therapy and may cause adverse effects or reduced efficacy. Existing computational approaches often fail to capture both the semantic information in drug sequences and the struc...

Prediction of peptide cleavage sites using protein language models and graph neural networks.

Scientific reports
The growing interest in using peptide molecules as therapeutic agents, driven by their high selectivity and efficacy, has become a significant trend in the pharmaceutical industry. However, their oral administration remains challenging due to their l...

OpenSpliceAI provides an efficient modular implementation of SpliceAI enabling easy retraining across nonhuman species.

eLife
The SpliceAI deep learning system is currently one of the most accurate methods for identifying splicing signals directly from DNA sequences. However, its utility is limited by its reliance on older software frameworks and human-centric training data...

Nine quick tips for trustworthy machine learning in the biomedical sciences.

PLoS computational biology
As machine learning (ML) becomes increasingly central to biomedical research, the need for trustworthy models is more pressing than ever. In this paper, we present nine concise and actionable tips to help researchers build ML systems that are technic...

DeepBPred: blood-brain barrier peptide predictor using stacked BiGRU model with novel features.

BMC biology
BACKGROUND: The blood-brain barrier (B) acts as a membrane that is a major concern in treating central nervous system (CNS) disorders. The B penetrating peptides (BPPs) play a significant role in delivering therapeutic drugs to a wide range of disord...