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The Role of Artificial Intelligence in Cancer Treatment: Molecular Perspective
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The Role of Artificial Intelligence in Cancer Treatment: Molecular Perspective

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 December 2024 | Viewed by 1190

Special Issue Editor

Dr. Andra Valentina Krauze

E-Mail Website
Guest Editor
Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD 20892, USA
Interests: rare tumors; data science and computational biology; brain and spine cancer; head and neck cancer; genitourinary tumors; re-irradiation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) in its various forms—machine learning (ML), deep learning (DL), and hybrid techniques—has seamlessly integrated into our daily lives. This integration is especially evident in the medical field, where the explosion of data across different modalities and settings offers us unprecedented opportunities to explore and analyze diverse data types. However, despite the vast amount of data available in cancer research, we still lack clinically actionable conclusions that can guide personalized treatments through AI-approved methods.

But there is hope on the horizon. The intersection of advanced computational methods and the multi-dimensional data in cancer research is paving the way for a theoretical shift. We now have the potential to translate AI findings into clinical trials and practical applications, bridging the gap from bench to bedside. The Special Issue brings together cutting-edge research that demonstrates how AI is enabling the analysis of massive multi-omics data, including genomics, transcriptomics, proteomics, and metabolomics, to uncover novel molecular mechanisms underlying cancer progression and the response to therapies. By harnessing AI’s molecular insights, we can develop cancer treatments that are not only effective but also interpretable for clinical use. Your participation and interest can help drive this exciting evolution in cancer care.

Dr. Andra Valentina Krauze
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.

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Keywords

  • cancer treatment
  • artificial intelligence
  • machine learning
  • precision medicine
  • molecular insights

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Published Papers (1 paper)

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Research

24 pages, 5480 KiB  
Article
MetaWise: Combined Feature Selection and Weighting Method to Link the Serum Metabolome to Treatment Response and Survival in Glioblastoma
by Erdal Tasci, Michael Popa, Ying Zhuge, Shreya Chappidi, Longze Zhang, Theresa Cooley Zgela, Mary Sproull, Megan Mackey, Heather R. Kates, Timothy J. Garrett, Kevin Camphausen and Andra V. Krauze
Int. J. Mol. Sci. 2024, 25(20), 10965; https://doi.org/10.3390/ijms252010965 - 11 Oct 2024
Viewed by 1022
Abstract
Glioblastoma (GBM) is a highly malignant and devastating brain cancer characterized by its ability to rapidly and aggressively grow, infiltrating brain tissue, with nearly universal recurrence after the standard of care (SOC), which comprises maximal safe resection followed by chemoirradiation (CRT). The metabolic [...] Read more.
Glioblastoma (GBM) is a highly malignant and devastating brain cancer characterized by its ability to rapidly and aggressively grow, infiltrating brain tissue, with nearly universal recurrence after the standard of care (SOC), which comprises maximal safe resection followed by chemoirradiation (CRT). The metabolic triggers leading to the reprogramming of tumor behavior and resistance are an area increasingly studied in relation to the tumor molecular features associated with outcome. There are currently no metabolomic biomarkers for GBM. Studying the metabolomic alterations in GBM patients undergoing CRT could uncover the biochemical pathways involved in tumor response and resistance, leading to the identification of novel biomarkers and the optimization of the treatment response. The feature selection process identifies key factors to improve the model’s accuracy and interpretability. This study utilizes a combined feature selection approach, incorporating both Least Absolute Shrinkage and Selection Operator (LASSO) and Minimum Redundancy–Maximum Relevance (mRMR), alongside a rank-based weighting method (i.e., MetaWise) to link metabolomic biomarkers to CRT and the 12-month and 20-month overall survival (OS) status in patients with GBM. Our method shows promising results, reducing feature dimensionality when employed on serum-based large-scale metabolomic datasets (University of Florida) for all our analyses. The proposed method successfully identified a set of eleven serum biomarkers shared among three datasets. The computational results show that the utilized method achieves 96.711%, 92.093%, and 86.910% accuracy rates with 48, 46, and 33 selected features for the CRT, 12-month, and 20-month OS-based metabolomic datasets, respectively. This discovery has implications for developing personalized treatment plans and improving patient outcomes. Full article
(This article belongs to the Special Issue The Role of Artificial Intelligence in Cancer Treatment: Molecular Perspective)
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