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Metabolic Networks and Pathways

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Combined network analysis and machine learning allows the prediction of metabolic pathways from tomato metabolomics data.

Communications biology
The identification and understanding of metabolic pathways is a key aspect in crop improvement and drug design. The common approach for their detection is based on gene annotation and ontology. Correlation-based network analysis, where metabolites ar...

Recognition of early and late stages of bladder cancer using metabolites and machine learning.

Metabolomics : Official journal of the Metabolomic Society
INTRODUCTION: Bladder cancer (BCa) is one of the most common and aggressive cancers. It is the sixth most frequently occurring cancer in men and its rate of occurrence increases with age. The current method of BCa diagnosis includes a cystoscopy and ...

Machine and deep learning meet genome-scale metabolic modeling.

PLoS computational biology
Omic data analysis is steadily growing as a driver of basic and applied molecular biology research. Core to the interpretation of complex and heterogeneous biological phenotypes are computational approaches in the fields of statistics and machine lea...

HyperFoods: Machine intelligent mapping of cancer-beating molecules in foods.

Scientific reports
Recent data indicate that up-to 30-40% of cancers can be prevented by dietary and lifestyle measures alone. Herein, we introduce a unique network-based machine learning platform to identify putative food-based cancer-beating molecules. These have bee...

Dynamic Metabolomics for Engineering Biology: Accelerating Learning Cycles for Bioproduction.

Trends in biotechnology
Metabolomics is a powerful tool to rationally guide the metabolic engineering of synthetic bioproduction pathways. Current reports indicate great potential to further develop metabolomics-directed synthetic bioproduction. Advanced mass metabolomics m...

Multigene signatures of responses to chemotherapy derived by biochemically-inspired machine learning.

Molecular genetics and metabolism
Pharmacogenomic responses to chemotherapy drugs can be modeled by supervised machine learning of expression and copy number of relevant gene combinations. Such biochemical evidence can form the basis of derived gene signatures using cell line data, w...

Analysis of defective pathways and drug repositioning in Multiple Sclerosis via machine learning approaches.

Computers in biology and medicine
BACKGROUND: Although some studies show that there could be a genetic predisposition to develop Multiple Sclerosis (MS), attempts to find genetic signatures related to MS diagnosis and development are extremely rare.

Reinforcement Learning for Bioretrosynthesis.

ACS synthetic biology
Metabolic engineering aims to produce chemicals of interest from living organisms, to advance toward greener chemistry. Despite efforts, the research and development process is still long and costly, and efficient computational design tools are requi...

The reactome pathway knowledgebase.

Nucleic acids research
The Reactome Knowledgebase (https://reactome.org) provides molecular details of signal transduction, transport, DNA replication, metabolism and other cellular processes as an ordered network of molecular transformations in a single consistent data mo...