AI Medical Compendium Topic:
Computational Biology

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Computational Insights into Reproductive Toxicity: Clustering, Mechanism Analysis, and Predictive Models.

International journal of molecular sciences
Reproductive toxicity poses significant risks to fertility and progeny health, making its identification in pharmaceutical compounds crucial. In this study, we conducted a comprehensive in silico investigation of reproductive toxic molecules, identif...

Quantifying massively parallel microbial growth with spatially mediated interactions.

PLoS computational biology
Quantitative understanding of microbial growth is an essential prerequisite for successful control of pathogens as well as various biotechnology applications. Even though the growth of cell populations has been extensively studied, microbial growth r...

TabDEG: Classifying differentially expressed genes from RNA-seq data based on feature extraction and deep learning framework.

PloS one
Traditional differential expression genes (DEGs) identification models have limitations in small sample size datasets because they require meeting distribution assumptions, otherwise resulting high false positive/negative rates due to sample variatio...

PMTPred: machine-learning-based prediction of protein methyltransferases using the composition of k-spaced amino acid pairs.

Molecular diversity
Protein methyltransferases (PMTs) are a group of enzymes that help catalyze the transfer of a methyl group to its substrates. These enzymes play an important role in epigenetic regulation and can methylate various substrates with DNA, RNA, protein, a...

Abnormal genes and pathways that drive muscle contracture from brachial plexus injuries: Towards machine learning approach.

SLAS technology
In order to clarify the pathways closely linked to denervated muscle contracture, this work uses IoMT-enabled healthcare stratergies to examine changes in gene expression patterns inside atrophic muscles following brachial plexus damage. The gene exp...

Gtie-Rt: A comprehensive graph learning model for predicting drugs targeting metabolic pathways in human.

Journal of bioinformatics and computational biology
Drugs often target specific metabolic pathways to produce a therapeutic effect. However, these pathways are complex and interconnected, making it challenging to predict a drug's potential effects on an organism's overall metabolism. The mapping of dr...

deepAMPNet: a novel antimicrobial peptide predictor employing AlphaFold2 predicted structures and a bi-directional long short-term memory protein language model.

PeerJ
BACKGROUND: Global public health is seriously threatened by the escalating issue of antimicrobial resistance (AMR). Antimicrobial peptides (AMPs), pivotal components of the innate immune system, have emerged as a potent solution to AMR due to their t...

Machine learning for catalysing the integration of noncoding RNA in research and clinical practice.

EBioMedicine
The human transcriptome predominantly consists of noncoding RNAs (ncRNAs), transcripts that do not encode proteins. The noncoding transcriptome governs a multitude of pathophysiological processes, offering a rich source of next-generation biomarkers....

Achieving Occam's razor: Deep learning for optimal model reduction.

PLoS computational biology
All fields of science depend on mathematical models. Occam's razor refers to the principle that good models should exclude parameters beyond those minimally required to describe the systems they represent. This is because redundancy can lead to incor...

Identifying potential targets for preventing cancer progression through the PLA2G1B recombinant protein using bioinformatics and machine learning methods.

International journal of biological macromolecules
Lung cancer is the deadliest and most aggressive malignancy in the world. Preventing cancer is crucial. Therefore, the new molecular targets have laid the foundation for molecular diagnosis and targeted therapy of lung cancer. PLA2G1B plays a key rol...