Preprint Hypothesis Version 1 This version is not peer-reviewed

Beyond Computational Genomics towards Systems Metabolomics for Precision Oncology

Version 1 : Received: 6 August 2018 / Approved: 6 August 2018 / Online: 6 August 2018 (15:09:15 CEST)

How to cite: Alberghina, L. Beyond Computational Genomics towards Systems Metabolomics for Precision Oncology. Preprints 2018, 2018080127 (doi: 10.20944/preprints201808.0127.v1). Alberghina, L. Beyond Computational Genomics towards Systems Metabolomics for Precision Oncology. Preprints 2018, 2018080127 (doi: 10.20944/preprints201808.0127.v1).

Abstract

Coordinated sets of extremely numerous digital data, on a given social or economic event, are treated by Artificial Intelligence tools to obtain reasonably accurate, valuable predictions. The same approach, applied to biomedical issues, as how to choose the right drug to completely cure a given cancer patient, does not reach satisfactory results. It is the “organized biological complexity”, which requires a different systems approach, to integrate, in an Augmented Intelligence strategy, statistical computations of digital data, network construction of “omics” findings, well-designed mathematical models and new experiments in an iterative pathway to reconstruct the “logic” beneath the “organized complexity”, as shown here for Systems Metabolomics of cancer. On this basis new diagnostic approaches, able to identify precision drug treatments, as well as new discovery strategy for more effective anti-cancer drugs are described.

Subject Areas

Big Data, Systems Models, Cancer metabolism, Cancer personalized treatment, Drug Discovery.

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