Molecular Informatics and AI in Cancer Research
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 June 2026 | Viewed by 1249
Special Issue Editors
2. Phalcon, LLC, Manhasset, NY 11030, USA
Interests: medical informatics; artificial intelligence; personalized medicine; cancer; drug repositioning
Special Issue Information
Dear Colleagues,
The convergence of molecular informatics and artificial intelligence (AI) is catalyzing a revolution in cancer research and care. This synergy enables the translation of vast molecular data into actionable knowledge, driving advancements across the cancer continuum from early detection and personalized medicine to drug discovery and a deeper understanding of cancer biology.
The Evolving Landscape
Cancer's complexity, driven by inter- and intratumoral heterogeneity, demands sophisticated analytical approaches. High-throughput technologies generate vast amounts of molecular data, offering deep insights and posing significant analytical challenges. Understanding molecular aberrations is paramount for effective therapies. To unlock the ultimate potential of molecular data to understand cancer progression and treatment, the context-specific incorporation of vast amounts of patient-specific data from large, curated datasets can be leveraged.
Molecular Informatics in Cancer
In oncology, molecular informatics involves systematically generating, managing, analyzing, and interpreting diverse molecular datasets (such as genomics, epigenomics, and proteomics) to illuminate cancer biology. It bridges raw molecular data and biological understanding, aiming to identify biomarkers, understand disease mechanisms, and inform the development of new therapies.
Artificial Intelligence
AI creates machines for tasks requiring human intelligence, such as reasoning, learning, and decision-making. Key capabilities include advanced data analysis, pattern recognition, automation, prediction, image analysis (e.g., medical imaging), and natural language processing (NLP) for unstructured data, including clinical notes.
The Synergistic Imperative
Molecular informatics provides structured molecular data; AI offers tools to extract knowledge from it. AI can combine this knowledge with patient-specific data to generate unique patient and cancer scenarios that identify highly accurate prognostic and treatment options for clinical applications. This synergy transforms oncology from discovery to personalized treatment. AI's capacity to analyze vast amounts of data is indispensable for navigating the molecular complexity of cancer.
Highlights
Future progress hinges on addressing challenges, including data quality, algorithmic bias, interpretability, clinical integration, and the ethical, legal, and social implications. Ultimately, this fusion heralds a new era of predictive, personalized, and equitable oncology, requiring sustained multidisciplinary collaboration and robust ethical frameworks to translate technological innovations into meaningful improvements in patient outcomes globally.
This Special Issue is supervised by Prof. Dr. Nabil Adam, Prof. Dr. Robert Wieder, and assisted by Dr. Tarek Adam.
Prof. Dr. Nabil Adam
Prof. Dr. Robert Wieder
Guest Editors
Manuscript Submission Information
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Keywords
- molecular informatics
- artificial intelligence (AI)
- cancer research
- personalized medicine
- high-throughput data
- biomarkers
- clinical applications
- longitudinal data
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