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Metabolic Networks and Pathways

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Machine learning methods for predicting essential metabolic genes from Plasmodium falciparum genome-scale metabolic network.

PloS one
Essential genes are those whose presence is vital for a cell's survival and growth. Detecting these genes in disease-causing organisms is critical for various biological studies, including understanding microbe metabolism, engineering genetically mod...

Identification of key metabolism-related genes and pathways in spontaneous preterm birth: combining bioinformatic analysis and machine learning.

Frontiers in endocrinology
BACKGROUND: Spontaneous preterm birth (sPTB) is a global disease that is a leading cause of death in neonates and children younger than 5 years of age. However, the etiology of sPTB remains poorly understood. Recent evidence has shown a strong associ...

A Multi-Omics, Machine Learning-Aware, Genome-Wide Metabolic Model of Bacillus Subtilis Refines the Gene Expression and Cell Growth Prediction.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Given the extensive heterogeneity and variability, understanding cellular functions and regulatory mechanisms through the analysis of multi-omics datasets becomes extremely challenging. Here, a comprehensive modeling framework of multi-omics machine ...

A machine learning framework for extracting information from biological pathway images in the literature.

Metabolic engineering
There have been significant advances in literature mining, allowing for the extraction of target information from the literature. However, biological literature often includes biological pathway images that are difficult to extract in an easily edita...

Metabolic Fluxes Using Deep Learning Based on Enzyme Variations: .

International journal of molecular sciences
Metabolic pathway modeling, essential for understanding organism metabolism, is pivotal in predicting genetic mutation effects, drug design, and biofuel development. Enhancing these modeling techniques is crucial for achieving greater prediction accu...

Identification of biomarkers in Alzheimer's disease and COVID-19 by bioinformatics combining single-cell data analysis and machine learning algorithms.

PloS one
BACKGROUND: Since its emergence in 2019, COVID-19 has become a global epidemic. Several studies have suggested a link between Alzheimer's disease (AD) and COVID-19. However, there is little research into the mechanisms underlying these phenomena. The...

Coupling flux balance analysis with reactive transport modeling through machine learning for rapid and stable simulation of microbial metabolic switching.

Scientific reports
Integrating genome-scale metabolic networks with reactive transport models (RTMs) provides a detailed description of the dynamic changes in microbial growth and metabolism. Despite promising demonstrations in the past, computational inefficiency has ...

Bootstrap inference and machine learning reveal core differential plasma metabolic connectome signatures in major depressive disorder.

Journal of affective disorders
BACKGROUND: Major depressive disorder (MDD) involves molecular alterations and pathway dysregulation. Metabolic interconnections are crucial for normal functioning, yet current analysis focuses on individual pathways or biomarkers, overlooking intric...

Integrative analysis of signaling and metabolic pathways, immune infiltration patterns, and machine learning-based diagnostic model construction in major depressive disorder.

Scientific reports
Major depressive disorder (MDD) is a multifactorial disorder involving genetic and environmental factors, with unclear pathogenesis. This study aims to explore the pathogenic pathway of MDD and its relationship with immune responses and to discover i...

An integrated AI knowledge graph framework of bacterial enzymology and metabolism.

Proceedings of the National Academy of Sciences of the United States of America
The study of bacterial metabolism holds immense significance for improving human health and advancing agricultural practices. The prospective applications of genomically encoded bacterial metabolism present a compelling opportunity, particularly in t...