AIMC Topic: Computational Biology
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Integrated analysis of gene expressions and targeted mirnas for explaining crosstalk between oral and esophageal squamous cell carcinomas through an interpretable machine learning approach.
Medical & biological engineering & computing
Oct 10, 2024
This study explores the bidirectional relation of esophageal squamous cell carcinoma (ESCC) and oral squamous cell carcinoma (OSCC), examining shared risk factors and underlying molecular mechanisms. By employing random forest (RF) classifier, enhanc...
High-throughput and computational techniques for aptamer design.
Expert opinion on drug discovery
Oct 10, 2024
INTRODUCTION: Aptamers refer to short ssDNA/RNA sequences that target small molecules, proteins, or cells. Aptamers have significantly advanced diagnostic applications, including biosensors for detecting specific biomarkers, state-of-the-art imaging,...
From Noise to Knowledge: Diffusion Probabilistic Model-Based Neural Inference of Gene Regulatory Networks.
Journal of computational biology : a journal of computational molecular cell biology
Oct 10, 2024
Understanding gene regulatory networks (GRNs) is crucial for elucidating cellular mechanisms and advancing therapeutic interventions. Original methods for GRN inference from bulk expression data often struggled with the high dimensionality and inhere...
Plant lncRNA-miRNA Interaction Prediction Based on Counterfactual Heterogeneous Graph Attention Network.
Interdisciplinary sciences, computational life sciences
Oct 9, 2024
Identifying interactions between long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) provides a new perspective for understanding regulatory relationships in plant life processes. Recently, computational methods based on graph neural networks (GNNs...
A Molecular Fragment Representation Learning Framework for Drug-Drug Interaction Prediction.
Interdisciplinary sciences, computational life sciences
Oct 9, 2024
The concurrent use of multiple drugs may result in drug-drug interactions, increasing the risk of adverse reactions. Hence, it is particularly crucial to propose computational methods for precisely identifying unknown drug-drug interactions, which is...
Screening core genes for minimal change disease based on bioinformatics and machine learning approaches.
International urology and nephrology
Oct 9, 2024
Based on bioinformatics and machine learning methods, we conducted a study to screen the core genes of minimal change disease (MCD) and further explore its pathogenesis. First, we obtained the chip data sets GSE108113 and GSE200828 from the Gene Expr...
Identification of endocrine-disrupting chemicals targeting key OP-associated genes via bioinformatics and machine learning.
Ecotoxicology and environmental safety
Oct 9, 2024
Osteoporosis (OP), a metabolic disorder predominantly impacting postmenopausal women, has seen considerable progress in diagnosis and treatment over the past few decades. However, the intricate interplay between genetic factors and endocrine disrupto...
A Knowledge-Driven Self-Supervised Approach for Molecular Generation.
IEEE/ACM transactions on computational biology and bioinformatics
Oct 9, 2024
Due to the great successes of Graph Neural Networks (GNN) in numerous fields, growing research interests have been devoted to applying GNN to molecular learning tasks. The molecule structure can be naturally represented as graphs where atoms and bond...
RDGAN: Prediction of circRNA-Disease Associations via Resistance Distance and Graph Attention Network.
IEEE/ACM transactions on computational biology and bioinformatics
Oct 9, 2024
As a series of single-stranded RNAs, circRNAs have been implicated in numerous diseases and can serve as valuable biomarkers for disease therapy and prevention. However, traditional biological experiments demand significant time and effort. Therefore...
Molecular Design Based on Integer Programming and Splitting Data Sets by Hyperplanes.
IEEE/ACM transactions on computational biology and bioinformatics
Oct 9, 2024
A novel framework for designing the molecular structure of chemical compounds with a desired chemical property has recently been proposed. The framework infers a desired chemical graph by solving a mixed integer linear program (MILP) that simulates t...
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