AIMC Topic: Computational Biology

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Language models reveal a complex sequence basis for adaptive convergent evolution of protein functions.

Proceedings of the National Academy of Sciences of the United States of America
Convergent evolution, or convergence, refers to repeated, independent emergences of the same trait in two or more lineages of species during evolution, often indicating functional adaptation to specific environmental factors. Many computational metho...

Computational Pipeline for Targeted Integration and Variable Payload Expression in Bacteriophage Engineering.

ACS synthetic biology
Bacteriophages offer a promising alternative to conventional antimicrobials, especially when such treatments fail. While natural phages are viable for therapy, advances in synthetic biology allow precise genome modifications to enhance their therapeu...

MorphoITH: a framework for deconvolving intra-tumor heterogeneity using tissue morphology.

Genome medicine
BACKGROUND: Tumor evolution, driven by the emergence of genetically and epigenetically distinct subclones, enables cancers to adapt to selective pressures and become more aggressive, posing a major challenge in oncology. Multi-regional sequencing has...

iBitter-Stack: A multi-representation ensemble learning model for accurate bitter peptide identification.

Journal of molecular biology
The identification of bitter peptides is crucial in various domains, including food science, drug discovery, and biochemical research. These peptides not only contribute to the undesirable taste of hydrolyzed proteins but also play key roles in physi...

MCMFPP: A Multifunctional Peptides Prediction Method Based on Class Feature Enhancement and Classifier Fusion.

Journal of chemical information and modeling
With the increasing discovery of peptide sequences and the growing demand for peptide-targeted drugs, traditional wet-lab experiment methods have become inadequate for peptide function prediction due to their high cost and that they are time consumin...

SPP1 as a key modulator of M2 macrophage polarization promotes endometriosis progression via activation of the FAK/PI3K/AKT pathway: A bioinformatics and experimental study.

International immunopharmacology
Endometriosis (EMs) is a gynecological disorder characterized by chronic inflammation and an aberrant immune microenvironment. In this study, we integrated the GSE6364 dataset from the GEO database to identify differentially expressed genes, and appl...

A deep learning approach based on molecular graph features and residual blocks to predict interaction sites between CircRNA and RBP.

Biochemical and biophysical research communications
CircRNAs are ubiquitously expressed across diverse tissues and cells, playing a pivotal role not only in protein-mediated biological processes but also in disease prevention and therapeutics. RNA-RBP interactions are critical for deciphering gene reg...

Partner-RBR: Predicting Multitype RNA-Binding Residues Based on Mutual Learning.

Journal of chemical information and modeling
RNA molecules play diverse and critical roles in various biological processes, including gene expression, post-transcriptional regulation, and disease pathogenesis. Understanding the interaction between proteins and RNA necessitates the precise ident...

Multi-class machine learning-based classification of SCID-related genetic variants.

Immunologic research
BACKGROUND: Variants of uncertain significance (VUS) represent a major diagnostic challenge in the interpretation of genetic testing results, particularly in the context of inborn errors of immunity such as severe combined immunodeficiency (SCID). Th...

Genome-scale prediction of gene ontology from mass fingerprints reveals new metabolic gene functions.

Life science alliance
Mass-based fingerprinting can characterize microorganisms; however, expansion of these methods to predict specific gene functions is lacking. Therefore, mass fingerprinting was developed to functionally profile a yeast knockout library. Matrix-assist...