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New Applications in the Diagnosis and Therapy of Diseases, 2nd Edition

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Pathology, Diagnostics, and Therapeutics".

Deadline for manuscript submissions: closed (30 August 2024) | Viewed by 1158

Special Issue Editors


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IDEAI_UPC Research Center, Universitat Politècnica de Catalunya (UPC BarcelonaTech), 08034 Barcelona, Spain
Interests: machine learning; data science; medical applications of artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Personalized medicine is an emerging field that promotes the use of novel diagnostic tests to offer the right treatment at the right time for each patient. Personalized medicine unavoidably leads us towards a data-centric view of health, as data from expensive multicenter, randomized, controlled clinical trials (evidence-based medicine) are required to demonstrate that a treatment is effective, but the results can be disappointing in population minorities. Therefore, an analysis of retrospective data collected in large databases (Big Data) can help to explore usefulness in population minorities and benefit patients and health systems. However, data must usually be obtained from heterogeneous sources and in different modalities (individual medical history, high-resolution images, omics data, diagnostic tests, etc.) and their analysis may become complex due to non-linear behaviour or changing dynamics. Therefore, modelling such complexity is a major challenge that requires powerful new tools and technologies. This is a natural challenge and pursuit for data science in the form of data collection, storage, curation of databases, processing, and analysis. We argue that the knowledge generated by data science approaches will have a positive impact on clinical practice through personalized treatments, and we would like to dedicate a Special Issue to collecting experiences of the diagnosis and personalized treatment of diseases from a data science and molecular perspective. Review papers and work in progress putting forward new and groundbreaking ideas are also welcomed.

Dr. Miguel Hueso
Dr. Alfredo Vellido
Guest Editors

Manuscript Submission Information

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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.

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Keywords

  • personalized medicine
  • systems biology
  • high-throughput technologies
  • molecular imaging
  • omics
  • pharmacogenomics
  • monitoring responses
  • data visualization
  • theranostics

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Published Papers (1 paper)

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Research

33 pages, 13307 KiB  
Article
A Study on the Robustness and Stability of Explainable Deep Learning in an Imbalanced Setting: The Exploration of the Conformational Space of G Protein-Coupled Receptors
by Mario A. Gutiérrez-Mondragón, Alfredo Vellido and Caroline König
Int. J. Mol. Sci. 2024, 25(12), 6572; https://doi.org/10.3390/ijms25126572 - 14 Jun 2024
Viewed by 725
Abstract
G-protein coupled receptors (GPCRs) are transmembrane proteins that transmit signals from the extracellular environment to the inside of the cells. Their ability to adopt various conformational states, which influence their function, makes them crucial in pharmacoproteomic studies. While many drugs target specific GPCR [...] Read more.
G-protein coupled receptors (GPCRs) are transmembrane proteins that transmit signals from the extracellular environment to the inside of the cells. Their ability to adopt various conformational states, which influence their function, makes them crucial in pharmacoproteomic studies. While many drugs target specific GPCR states to exert their effects—thereby regulating the protein’s activity—unraveling the activation pathway remains challenging due to the multitude of intermediate transformations occurring throughout this process, and intrinsically influencing the dynamics of the receptors. In this context, computational modeling, particularly molecular dynamics (MD) simulations, may offer valuable insights into the dynamics and energetics of GPCR transformations, especially when combined with machine learning (ML) methods and techniques for achieving model interpretability for knowledge generation. The current study builds upon previous work in which the layer relevance propagation (LRP) technique was employed to interpret the predictions in a multi-class classification problem concerning the conformational states of the β2-adrenergic (β2AR) receptor from MD simulations. Here, we address the challenges posed by class imbalance and extend previous analyses by evaluating the robustness and stability of deep learning (DL)-based predictions under different imbalance mitigation techniques. By meticulously evaluating explainability and imbalance strategies, we aim to produce reliable and robust insights. Full article
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