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Advances in AI and Machine Learning for the Analysis of -Omics and Complex Molecular Data

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".

Deadline for manuscript submissions: 20 October 2024 | Viewed by 1179

Special Issue Editors

Prof. Dr. David P Kreil

E-Mail Website
Guest Editor
1. Department für Biotechnologie, Universität für Bodenkultur Wien, (BOKU), Vienna, Austria
2. Institute of Advanced Research in Artificial Intelligence (IARAI), Vienna, Austria
Interests: machine learning; artificial intelligence; quantitative assays
Dr. Aleksandra Gruca

E-Mail Website
Guest Editor
Department of Computer Networks and Systems, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland
Interests: machine learning; computational biology; bioinformatics; protein function

Special Issue Information

Dear Colleagues,

Increasingly, AI and machine learning spearhead efforts in analyzing the complex datasets generated by high-throughput -omic technologies. Advances in AI and machine learning, on the one hand, and progress in their applications, on the other hand, are traditionally pursued by different scientific communities, which we aim to bring together in this Special Issue of the IJMS.

We thus invite you to share your best work in the following domains:

(1) Advancing AI and machine learning for the analysis of -omics and complex molecular data. We welcome methodological advances or insights that robustly generalize to different data sources. Where complex algorithms or pipelines are introduced, individual steps need to be justified, such as through ablation studies.

(2) Applying AI and machine learning for novel insights into the mechanisms of biological processes or systems at the molecular level. We welcome novel insights concerning molecular functions, regulation mechanisms, pathways (regulation, signaling, metabolic, etc.), or molecular pathology. The identification of biomarkers is of interest if robust across cohorts or linked to mechanisms.

Novel insights should be developed in the context of complex systems, including, but not limited to, studies on organism interactions, healthy cohorts, or heterogenous diseases, such as cardiovascular, autoimmune, or ageing-related diseases, and cancer.

We sincerely hope that this Special Issue can showcase your latest work!

This Special Issue is edited by members of COST Action AtheroNET CA21153 (Network for implementing multi-omics approaches in atherosclerotic cardiovascular disease prevention and research, www.atheronet.eu).

Prof. Dr. David P Kreil
Dr. Aleksandra Gruca
Guest Editors

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. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. 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

  • computational biology

  • bioinformatics
  • machine learning/AI
  • high-throughput data analysis
  • multi-omics
  • genomics
  • transcriptomics
  • proteomics
  • metabolomics
  • regulation mechanisms
  • pathway analysis (regulation, signaling, and metabolic)
  • molecular pathology
  • complex diseases (cancer, cardiovascular, autoimmune, ageing-related, etc.)
  • biomarkers
  • functional prediction/annotation
  • benchmarking

Published Papers (1 paper)

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Research

20 pages, 23188 KiB  
Article
Boosting Clear Cell Renal Carcinoma-Specific Drug Discovery Using a Deep Learning Algorithm and Single-Cell Analysis
by Yishu Wang, Xiaomin Chen, Ningjun Tang, Mengyao Guo and Dongmei Ai
Int. J. Mol. Sci. 2024, 25(7), 4134; https://doi.org/10.3390/ijms25074134 - 8 Apr 2024
Viewed by 904
Abstract
Clear cell renal carcinoma (ccRCC), the most common subtype of renal cell carcinoma, has the high heterogeneity of a highly complex tumor microenvironment. Existing clinical intervention strategies, such as target therapy and immunotherapy, have failed to achieve good therapeutic effects. In this article, [...] Read more.
Clear cell renal carcinoma (ccRCC), the most common subtype of renal cell carcinoma, has the high heterogeneity of a highly complex tumor microenvironment. Existing clinical intervention strategies, such as target therapy and immunotherapy, have failed to achieve good therapeutic effects. In this article, single-cell transcriptome sequencing (scRNA-seq) data from six patients downloaded from the GEO database were adopted to describe the tumor microenvironment (TME) of ccRCC, including its T cells, tumor-associated macrophages (TAMs), endothelial cells (ECs), and cancer-associated fibroblasts (CAFs). Based on the differential typing of the TME, we identified tumor cell-specific regulatory programs that are mediated by three key transcription factors (TFs), whilst the TF EPAS1/HIF-2α was identified via drug virtual screening through our analysis of ccRCC’s protein structure. Then, a combined deep graph neural network and machine learning algorithm were used to select anti-ccRCC compounds from bioactive compound libraries, including the FDA-approved drug library, natural product library, and human endogenous metabolite compound library. Finally, five compounds were obtained, including two FDA-approved drugs (flufenamic acid and fludarabine), one endogenous metabolite, one immunology/inflammation-related compound, and one inhibitor of DNA methyltransferase (N4-methylcytidine, a cytosine nucleoside analogue that, like zebularine, has the mechanism of inhibiting DNA methyltransferase). Based on the tumor microenvironment characteristics of ccRCC, five ccRCC-specific compounds were identified, which would give direction of the clinical treatment for ccRCC patients. Full article
(This article belongs to the Special Issue Advances in AI and Machine Learning for the Analysis of -Omics and Complex Molecular Data)
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