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Artificial Intelligence in Medical Data Science and Precision Medicine
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Artificial Intelligence in Medical Data Science and Precision Medicine

A special issue of Data (ISSN 2306-5729). This special issue belongs to the section "Computational Biology, Bioinformatics, and Biomedical Data Science".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 3603

Special Issue Editor

Dr. Zeeshan Ahmed

E-Mail Website
Guest Editor
Institute for Health, and Robert Wood Johnson Medical School, at Rutgers, The State University of New Jersey, New Brunswick, NJ, USA
Interests: artificial intelligence; precision medicine; healthcare; genomics; metabolomics; bioinformatics; software engineering

Special Issue Information

Dear Colleagues,

The quest to understand what causes chronic, acute, and infectious diseases has been a central focus of humankind since the beginning of scientific discovery. Our evolving understanding of their complex nature has led us to realize that to effectively diagnose and treat patients with these conditions, it is essential to utilize precision medicine. By identifying novel risk factors and disease biomarkers, precision medicine has the potential to translate scientific discovery into clinically actionable personal healthcare. Advancements in Artificial Intelligence (AI), and its application in precision medicine can improve the traditional symptom-driven practice of medicine, allowing earlier interventions using advanced diagnostics and tailoring better and economically personalized treatments.

This Special Issue of Data features Artificial Intelligence, medical data science, and precision medicine, and invites relevant high-quality original articles, data descriptors, reviews, and case reports for transparent peer review and publication.

Dr. Zeeshan Ahmed
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. Data 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 1600 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

  • Artificial intelligence
  • Biomedical informatics
  • Bioinformatics
  • Big Data
  • Data science
  • Database
  • Healthcare
  • Machine Learning
  • Multi-OMICS
  • Precision medicine

Published Papers (1 paper)

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7 pages, 561 KiB  
Data Descriptor
Lipid Profiles of Human Brain Tumors Obtained by High-Resolution Negative Mode Ambient Mass Spectrometry
by Denis S. Zavorotnyuk, Stanislav I. Pekov, Anatoly A. Sorokin, Denis S. Bormotov, Nikita Levin, Evgeny Zhvansky, Savva Semenov, Polina Strelnikova, Konstantin V. Bocharov, Alexander Vorobiev, Alexey Kononikhin, Vsevolod Shurkhay, Eugene N. Nikolaev and Igor A. Popov
Data 2021, 6(12), 132; https://0-doi-org.brum.beds.ac.uk/10.3390/data6120132 - 12 Dec 2021
Cited by 4 | Viewed by 2866
Abstract
Alterations in cell metabolism, including changes in lipid composition occurring during malignancy, are well characterized for various tumor types. However, a significant part of studies that deal with brain tumors have been performed using cell cultures and animal models. Here, we present a [...] Read more.
Alterations in cell metabolism, including changes in lipid composition occurring during malignancy, are well characterized for various tumor types. However, a significant part of studies that deal with brain tumors have been performed using cell cultures and animal models. Here, we present a dataset of 124 high-resolution negative ionization mode lipid profiles of human brain tumors resected during neurosurgery. The dataset is supplemented with 38 non-tumor pathological brain tissue samples resected during elective surgery. The change in lipid composition alterations of brain tumors enables the possibility of discriminating between malignant and healthy tissues with the implementation of ambient mass spectrometry. On the other hand, the collection of clinical samples allows the comparison of the metabolism alteration patterns in animal models or in vitro models with natural tumor samples ex vivo. The presented dataset is intended to be a data sample for bioinformaticians to test various data analysis techniques with ambient mass spectrometry profiles, or to be a source of clinically relevant data for lipidomic research in oncology. Full article
(This article belongs to the Special Issue Artificial Intelligence in Medical Data Science and Precision Medicine)
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