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Machine Learning Applications in Bioinformatics and Biomedicine 2.0
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Machine Learning Applications in Bioinformatics and Biomedicine 2.0

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: 30 September 2024 | Viewed by 1982

Special Issue Editor

Dr. Hao Lv

E-Mail Website
Guest Editor
Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
Interests: bioinformatics; single-cell data analysis; epigenomics; 3D genomics

Special Issue Information

Dear Colleagues,

Machine learning has been developed for over 40 years. In recent years, with the rapid accumulation of data in the biological and medical fields, machine learning has been widely used in these fields. The purpose of organizing this Special Issue is to provide a platform for publishing the latest cutting-edge work related to the application of machine learning in the biological and medicine fields, and promote the development of related fields. This Special Issue will focus on various aspects of the development and application of computational methods and techniques in biological and medical data for discovering disease markers. The subtopics include, but are not limited to, the following:

  • Identification of disease markers from genome, transcriptome, proteome and metabolome;
  • Discovery of drug target using machine learning;
  • Drug design based on machine learning;
  • Using machine learning to analyze clinical data;
  • Research on big data of physical examination based on machine learning and artificial intelligence;
  • Prediction of drug side effects based on machine learning;
  • Epigenetics markers discovery for disease using artificial intelligence;
  • The discovery of molecular network marker for disease diagnosis and therapy;
  • Early screening of diseases based on artificial intelligence.

Dr. Hao Lv
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. 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

  • genome
  • transcriptome
  • proteome
  • metabolome
  • drug target
  • machine learning
  • prediction of drug side effects
  • epigenetics markers discovery for disease
  • molecular network marker
  • early screening of diseases

Published Papers (3 papers)

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Research

15 pages, 11541 KiB  
Article
Novel AT2 Cell Subpopulations and Diagnostic Biomarkers in IPF: Integrating Machine Learning with Single-Cell Analysis
by Zhuoying Yang, Yanru Yang, Xin Han and Jiwei Hou
Int. J. Mol. Sci. 2024, 25(14), 7754; https://doi.org/10.3390/ijms25147754 - 15 Jul 2024
Viewed by 255
Abstract
Idiopathic pulmonary fibrosis (IPF) is a long-term condition with an unidentified cause, and currently there are no specific treatment options available. Alveolar epithelial type II cells (AT2) constitute a heterogeneous population crucial for secreting and regenerative functions in the alveolus, essential for maintaining [...] Read more.
Idiopathic pulmonary fibrosis (IPF) is a long-term condition with an unidentified cause, and currently there are no specific treatment options available. Alveolar epithelial type II cells (AT2) constitute a heterogeneous population crucial for secreting and regenerative functions in the alveolus, essential for maintaining lung homeostasis. However, a comprehensive investigation into their cellular diversity, molecular features, and clinical implications is currently lacking. In this study, we conducted a comprehensive examination of single-cell RNA sequencing data from both normal and fibrotic lung tissues. We analyzed alterations in cellular composition between IPF and normal tissue and investigated differentially expressed genes across each cell population. This analysis revealed the presence of two distinct subpopulations of IPF-related alveolar epithelial type II cells (IR_AT2). Subsequently, three unique gene co-expression modules associated with the IR_AT2 subtype were identified through the use of hdWGCNA. Furthermore, we refined and identified IPF-related AT2-related gene (IARG) signatures using various machine learning algorithms. Our analysis demonstrated a significant association between high IARG scores in IPF patients and shorter survival times (p-value < 0.01). Additionally, we observed a negative correlation between the percent predicted diffusing capacity for lung carbon monoxide (% DLCO) and increased IARG scores (cor = −0.44, p-value < 0.05). The cross-validation findings demonstrated a high level of accuracy (AUC > 0.85, p-value < 0.01) in the prognostication of patients with IPF utilizing the identified IARG signatures. Our study has identified distinct molecular and biological features among AT2 subpopulations, specifically highlighting the unique characteristics of IPF-related AT2 cells. Importantly, our findings underscore the prognostic relevance of specific genes associated with IPF-related AT2 cells, offering valuable insights into the advancement of IPF. Full article
(This article belongs to the Special Issue Machine Learning Applications in Bioinformatics and Biomedicine 2.0)
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13 pages, 2603 KiB  
Article
Machine Learning Identifies Key Proteins in Primary Sclerosing Cholangitis Progression and Links High CCL24 to Cirrhosis
by Tom Snir, Raanan Greenman, Revital Aricha, Matthew Frankel, John Lawler, Francesca Saffioti, Massimo Pinzani, Douglas Thorburn, Adi Mor and Ilan Vaknin
Int. J. Mol. Sci. 2024, 25(11), 6042; https://doi.org/10.3390/ijms25116042 - 30 May 2024
Viewed by 619
Abstract
Primary sclerosing cholangitis (PSC) is a rare, progressive disease, characterized by inflammation and fibrosis of the bile ducts, lacking reliable prognostic biomarkers for disease activity. Machine learning applied to broad proteomic profiling of sera allowed for the discovery of markers of disease presence, [...] Read more.
Primary sclerosing cholangitis (PSC) is a rare, progressive disease, characterized by inflammation and fibrosis of the bile ducts, lacking reliable prognostic biomarkers for disease activity. Machine learning applied to broad proteomic profiling of sera allowed for the discovery of markers of disease presence, severity, and cirrhosis and the exploration of the involvement of CCL24, a chemokine with fibro-inflammatory activity. Sera from 30 healthy controls and 45 PSC patients were profiled with proximity extension assay, quantifying the expression of 2870 proteins, and used to train an elastic net model. Proteins that contributed most to the model were tested for correlation to enhanced liver fibrosis (ELF) score and used to perform pathway analysis. Statistical modeling for the presence of cirrhosis was performed with principal component analysis (PCA), and receiver operating characteristics (ROC) curves were used to assess the useability of potential biomarkers. The model successfully predicted the presence of PSC, where the top-ranked proteins were associated with cell adhesion, immune response, and inflammation, and each had an area under receiver operator characteristic (AUROC) curve greater than 0.9 for disease presence and greater than 0.8 for ELF score. Pathway analysis showed enrichment for functions associated with PSC, overlapping with pathways enriched in patients with high levels of CCL24. Patients with cirrhosis showed higher levels of CCL24. This data-driven approach to characterize PSC and its severity highlights potential serum protein biomarkers and the importance of CCL24 in the disease, implying its therapeutic potential in PSC. Full article
(This article belongs to the Special Issue Machine Learning Applications in Bioinformatics and Biomedicine 2.0)
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20 pages, 868 KiB  
Article
Prediction of Protein–Protein Interactions Based on Integrating Deep Learning and Feature Fusion
by Hoai-Nhan Tran, Phuc-Xuan-Quynh Nguyen, Fei Guo and Jianxin Wang
Int. J. Mol. Sci. 2024, 25(11), 5820; https://doi.org/10.3390/ijms25115820 - 27 May 2024
Viewed by 564
Abstract
Understanding protein–protein interactions (PPIs) helps to identify protein functions and develop other important applications such as drug preparation and protein–disease relationship identification. Deep-learning-based approaches are being intensely researched for PPI determination to reduce the cost and time of previous testing methods. In this [...] Read more.
Understanding protein–protein interactions (PPIs) helps to identify protein functions and develop other important applications such as drug preparation and protein–disease relationship identification. Deep-learning-based approaches are being intensely researched for PPI determination to reduce the cost and time of previous testing methods. In this work, we integrate deep learning with feature fusion, harnessing the strengths of both approaches, handcrafted features, and protein sequence embedding. The accuracies of the proposed model using five-fold cross-validation on Yeast core and Human datasets are 96.34% and 99.30%, respectively. In the task of predicting interactions in important PPI networks, our model correctly predicted all interactions in one-core, Wnt-related, and cancer-specific networks. The experimental results on cross-species datasets, including Caenorhabditis elegans, Helicobacter pylori, Homo sapiens, Mus musculus, and Escherichia coli, also show that our feature fusion method helps increase the generalization capability of the PPI prediction model. Full article
(This article belongs to the Special Issue Machine Learning Applications in Bioinformatics and Biomedicine 2.0)
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