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Metabolic Networks and Pathways

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Essential gene prediction using limited gene essentiality information-An integrative semi-supervised machine learning strategy.

PloS one
Essential gene prediction helps to find minimal genes indispensable for the survival of any organism. Machine learning (ML) algorithms have been useful for the prediction of gene essentiality. However, currently available ML pipelines perform poorly ...

Pathway information extracted from 25 years of pathway figures.

Genome biology
Thousands of pathway diagrams are published each year as static figures inaccessible to computational queries and analyses. Using a combination of machine learning, optical character recognition, and manual curation, we identified 64,643 pathway figu...

Cognitive analysis of metabolomics data for systems biology.

Nature protocols
Cognitive computing is revolutionizing the way big data are processed and integrated, with artificial intelligence (AI) natural language processing (NLP) platforms helping researchers to efficiently search and digest the vast scientific literature. M...

Hybrid modelling of biological systems using fuzzy continuous Petri nets.

Briefings in bioinformatics
Integrated modelling of biological systems is challenged by composing components with sufficient kinetic data and components with insufficient kinetic data or components built only using experts' experience and knowledge. Fuzzy continuous Petri nets ...

[Construction of Escherichia coli cell factories].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology
As an important model industrial microorganism, Escherichia coli has been widely used in pharmaceutical, chemical industry and agriculture. In the past 30 years, a variety of new strategies and techniques, including artificial intelligence, gene edit...

Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance.

Nature communications
Resistance to ionizing radiation, a first-line therapy for many cancers, is a major clinical challenge. Personalized prediction of tumor radiosensitivity is not currently implemented clinically due to insufficient accuracy of existing machine learnin...

Ensemble learning application to discover new trypanothione synthetase inhibitors.

Molecular diversity
Trypanosomatid-caused diseases are among the neglected infectious diseases with the highest disease burden, affecting about 27 million people worldwide and, in particular, socio-economically vulnerable populations. Trypanothione synthetase (TryS) is ...

SCOUR: a stepwise machine learning framework for predicting metabolite-dependent regulatory interactions.

BMC bioinformatics
BACKGROUND: The topology of metabolic networks is both well-studied and remarkably well-conserved across many species. The regulation of these networks, however, is much more poorly characterized, though it is known to be divergent across organisms-t...