Machine Learning in Metabolic Diseases

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Bioinformatics and Data Analysis".

Deadline for manuscript submissions: 15 November 2024 | Viewed by 2437

Special Issue Editor


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Guest Editor
1. Research Unit of Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, Germany
2. Research Center for Environmental Health, 85764 Neuherberg, Germany
Interests: type 2 diabetes and complications; bioinformatics; machine learning; omics data integration; translational research

Special Issue Information

Dear Colleagues,

Machine learning (ML) concerns computer algorithms that improve their performance by learning from large sets of data. As a subdiscipline of artificial intelligence, ML has been developed and applied in analyzing complex data such as metabolomics to predict, identify and validate biomarkers / risk factors of metabolic diseases. The key steps of ML includes 1) data gathering and pre-processing; 2) model selection, training and testing; and 3) prediction, inference and applications. Large and high quality data enable good performance for predicting disease risk to develop efficient personalized diagnosis and therapy.

This Special Issue focuses on ML in metabolic diseases. Topics include studies aimed at developing and / or using ML in the following areas:

  • Collection of data (e.g., human cohort studies, clinical studies, biobanks), and data pre-processing (e.g., harmonization / normalization of individuals molecular profiles or clinical phenotypes);
  • Techniques for optimized ML model selection. ML methods may include supervised (e.g., regression and classification analysis, support vector machine and random forest) and unsupervised (e.g., clustering, principal component analysis, autoencoders and generative adversarial networks);
  • Application of ML for improved prediction, identification and validation of risk factors, modifiers and / or biomarkers of metabolic diseases.

Dr. Rui Wang-Sattler
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metabolites is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • supervised
  • unsupervised
  • model selection
  • training and testing
  • data pre-processing
  • prediction
  • identification
  • validation risk factors/biomarkers
  • metabolic disease

Published Papers (1 paper)

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Research

23 pages, 4246 KiB  
Article
Identification of Cancer Driver Genes by Integrating Multiomics Data with Graph Neural Networks
by Hongzhi Song, Chaoyi Yin, Zhuopeng Li, Ke Feng, Yangkun Cao, Yujie Gu and Huiyan Sun
Metabolites 2023, 13(3), 339; https://doi.org/10.3390/metabo13030339 - 24 Feb 2023
Cited by 3 | Viewed by 1835
Abstract
Cancer is a heterogeneous disease that is driven by the accumulation of both genetic and nongenetic alterations, so integrating multiomics data and extracting effective information from them is expected to be an effective way to predict cancer driver genes. In this paper, we [...] Read more.
Cancer is a heterogeneous disease that is driven by the accumulation of both genetic and nongenetic alterations, so integrating multiomics data and extracting effective information from them is expected to be an effective way to predict cancer driver genes. In this paper, we first generate comprehensive instructive features for each gene from genomic, epigenomic, transcriptomic levels together with protein–protein interaction (PPI)-networks-derived attributes and then propose a novel semisupervised deep graph learning framework GGraphSAGE to predict cancer driver genes according to the impact of the alterations on a biological system. When applied to eight tumor types, experimental results suggest that GGraphSAGE outperforms several state-of-the-art computational methods for driver genes identification. Moreover, it broadens our current understanding of cancer driver genes from multiomics level and identifies driver genes specific to the tumor type rather than pan-cancer. We expect GGraphSAGE to open new avenues in precision medicine and even further predict drivers for other complex diseases. Full article
(This article belongs to the Special Issue Machine Learning in Metabolic Diseases)
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