AIMC Topic: Computational Biology

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A fast (CNN + MCWS-transformer) based architecture for protein function prediction.

Statistical applications in genetics and molecular biology
The transformer model for sequence mining has brought a paradigmatic shift to many domains, including biological sequence mining. However, transformers suffer from quadratic complexity, i.e., O( ), where is the sequence length, which affects the tra...

Identification of novel biomarkers linked to M1 macrophage infiltration in the diagnosis of inflammatory bowel diseases.

International immunopharmacology
Inflammatory bowel disease (IBD) often lacks a definitive diagnostic standard, leading to diagnoses through exclusion. This study aimed to create a predictive model for IBD using bioinformatics and deep learning while identifying potential biomarkers...

GMFOLD: Subgraph matching for high-throughput DNA-aptamer secondary structure classification and machine learning interpretability.

Mathematical biosciences
Aptamers are oligonucleotide receptors that bind to their targets with high affinity. Here, we consider aptamers comprised of single-stranded DNA that undergo target-binding-induced conformational changes, giving rise to unique secondary and tertiary...

A Innovative Strategy for Identifying Subtypes Through the Analysis of Multi-Omics Data with Adversarial Autoencoders.

Journal of computational biology : a journal of computational molecular cell biology
Cancer is a disease that is both complex and diverse, and effective diagnosis and treatment require an accurate depiction of tumor subtypes. Traditional methods of cancer identification, which rely on clinical and histopathological criteria, have lim...

rbpTransformer: A novel deep learning model for prediction of piRNA and mRNA bindings.

PloS one
An important issue in biotechnology is predicting whether a piRNA and an mRNA will or will not bind. Research and treatment of diseases, drug discovery, and the silencing and regulation of genes, transposons, and genomic stability may all benefit fro...

Sequence and Structure-based Prediction of Allosteric Sites.

Journal of molecular biology
Allosteric regulation in proteins is a critical aspect of cellular function, influencing various biological processes through conformational or dynamic changes induced by effector molecules. Allosteric drugs possess significant therapeutic value due ...

MLWNNR: LncRNA-Disease Association Prediction with Multi-Kernel Learning-Driven Weighted Nuclear Norm Regularization.

Interdisciplinary sciences, computational life sciences
Emerging evidence highlights long non-coding RNAs (lncRNAs) as pivotal regulators demonstrating significant linkages with diverse human pathologies through expression dynamics and regulatory cascades. This research endeavors to establish an algorithm...

CPPpred-En: Ensemble framework integrating a protein language model and conventional features for highly accurate cell-penetrating peptide prediction.

Computers in biology and medicine
Cell-penetrating peptides (CPPs) have gained significant attention for biomedical applications, including drug delivery and therapeutic development, due to their ability to penetrate cell membranes. The accurate prediction of CPPs is critical for acc...

Transcriptomic bioinformatics analysis proposes a novel BCL2-MAPK14-TXN oxidative stress diagnostic model of sepsis and identifies TXN as an oxidative stress-related signature gene in sepsis.

International immunopharmacology
BACKGROUND: Oxidative stress was one of key factors driving the septic development by the uncontrolled accumulation of free radicals, thus oxidative stress-related biomarkers provide a novel diagnostic option. This study focused on screening oxidativ...

CD63 as a critical biomarker associated with immune infiltration in depression through bioinformatics analysis and experimental validation.

Journal of affective disorders
BACKGROUND: Major depressive disorder (MDD) exhibits a multifactorial etiopathogenesis, yet current diagnosis still relies on clinician-rated symptom scales and phenomenological interviews, lacking validated biomarkers.