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Metabolic Networks and Pathways

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DeepRF: A deep learning method for predicting metabolic pathways in organisms based on annotated genomes.

Computers in biology and medicine
The rapid increase of metabolomics has led to an increasing focus on metabolic pathway modeling and reconstruction. In particular, reconstructing an organism's metabolic network based on its genome sequence is a key challenge in systems biology. The ...

A novel hybrid framework for metabolic pathways prediction based on the graph attention network.

BMC bioinformatics
BACKGROUND: Making clear what kinds of metabolic pathways a drug compound involves in can help researchers understand how the drug is absorbed, distributed, metabolized, and excreted. The characteristics of a compound such as structure, composition a...

Multi-omic integration by machine learning (MIMaL).

Bioinformatics (Oxford, England)
MOTIVATION: Cells respond to environments by regulating gene expression to exploit resources optimally. Recent advances in technologies allow for measuring the abundances of RNA, proteins, lipids and metabolites. These highly complex datasets reflect...

Exploring the expressiveness of abstract metabolic networks.

PloS one
Metabolism is characterised by chemical reactions linked to each other, creating a complex network structure. The whole metabolic network is divided into pathways of chemical reactions, such that every pathway is a metabolic function. A simplified re...

Metabolic engineering for sustainability and health.

Trends in biotechnology
Bio-based production of chemicals and materials has attracted much attention due to the urgent need to establish sustainability and enhance human health. Metabolic engineering (ME) allows purposeful modification of cellular metabolic, regulatory, and...

A Machine Learning-Based Approach Using Multi-omics Data to Predict Metabolic Pathways.

Methods in molecular biology (Clifton, N.J.)
The integrative method approaches are continuously evolving to provide accurate insights from the data that is received through experimentation on various biological systems. Multi-omics data can be integrated with predictive machine learning algorit...

Synthetic Biology Meets Machine Learning.

Methods in molecular biology (Clifton, N.J.)
This chapter outlines the myriad applications of machine learning (ML) in synthetic biology, specifically in engineering cell and protein activity, and metabolic pathways. Though by no means comprehensive, the chapter highlights several prominent com...

Message Passing Neural Networks Improve Prediction of Metabolite Authenticity.

Journal of chemical information and modeling
Cytochrome P450 enzymes aid in the elimination of a preponderance of small molecule drugs, but can generate reactive metabolites that may adversely react with protein and DNA and prompt drug candidate attrition or market withdrawal. Previously develo...

An integrated deep learning framework for the interpretation of untargeted metabolomics data.

Briefings in bioinformatics
Untargeted metabolomics is gaining widespread applications. The key aspects of the data analysis include modeling complex activities of the metabolic network, selecting metabolites associated with clinical outcome and finding critical metabolic pathw...

MPI-VGAE: protein-metabolite enzymatic reaction link learning by variational graph autoencoders.

Briefings in bioinformatics
Enzymatic reactions are crucial to explore the mechanistic function of metabolites and proteins in cellular processes and to understand the etiology of diseases. The increasing number of interconnected metabolic reactions allows the development of in...