AIMC Topic: Metabolic Networks and Pathways

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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...

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...

Metabolic pathway inference using multi-label classification with rich pathway features.

PLoS computational biology
Metabolic inference from genomic sequence information is a necessary step in determining the capacity of cells to make a living in the world at different levels of biological organization. A common method for determining the metabolic potential encod...

Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism.

Nature communications
Through advanced mechanistic modeling and the generation of large high-quality datasets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can be c...

Moonlighting Proteins in the Fuzzy Logic of Cellular Metabolism.

Molecules (Basel, Switzerland)
The numerous interconnected biochemical pathways that make up the metabolism of a living cell comprise a fuzzy logic system because of its high level of complexity and our inability to fully understand, predict, and model the many activities, how the...

A Support Vector Machine Model Predicting the Risk of Duodenal Cancer in Patients with Familial Adenomatous Polyposis at the Transcript Levels.

BioMed research international
OBJECTIVE: Familial adenomatous polyposis (FAP) is one major type of inherited duodenal cancer. The estimate of duodenal cancer risk in patients with FAP is critical for selecting the optimal treatment strategy.

SemanticGO: a tool for gene functional similarity analysis in Arabidopsis thaliana and rice.

Plant science : an international journal of experimental plant biology
Gene or pathway functional similarities are important information for researchers. However, these similarities are often described sparsely and qualitatively. The latent semantic analysis of Arabidopsis thaliana (Arabidopsis) Gene Ontology (GO) data ...

Improving lipid mapping in Genome Scale Metabolic Networks using ontologies.

Metabolomics : Official journal of the Metabolomic Society
INTRODUCTION: To interpret metabolomic and lipidomic profiles, it is necessary to identify the metabolic reactions that connect the measured molecules. This can be achieved by putting them in the context of genome-scale metabolic network reconstructi...