Filho, U.L.; Pais, T.A.; Pais, R.J. Facilitating “Omics” for Phenotype Classification Using a User-Friendly AI-Driven Platform: Application in Cancer Prognostics. BioMedInformatics2023, 3, 1071-1082.
Filho, U.L.; Pais, T.A.; Pais, R.J. Facilitating “Omics” for Phenotype Classification Using a User-Friendly AI-Driven Platform: Application in Cancer Prognostics. BioMedInformatics 2023, 3, 1071-1082.
Filho, U.L.; Pais, T.A.; Pais, R.J. Facilitating “Omics” for Phenotype Classification Using a User-Friendly AI-Driven Platform: Application in Cancer Prognostics. BioMedInformatics2023, 3, 1071-1082.
Filho, U.L.; Pais, T.A.; Pais, R.J. Facilitating “Omics” for Phenotype Classification Using a User-Friendly AI-Driven Platform: Application in Cancer Prognostics. BioMedInformatics 2023, 3, 1071-1082.
Abstract
Precision medicine approaches often relies on complex and integrative analysis of multiple biomarkers from “omics” data to generate insights that can help either diagnostics, prognostics or therapeutical decisions. Such insights are often made using Machine learning (ML) models that make sample classification for a particular phenotype (yes/no). Building such models is a challenge and time-consuming, requiring advanced coding skills and mathematical modelling expertise. Artificial intelligence (AI) is a methodological solution that has the potential to facilitate, optimize and scale model development. In this work, we developed an AI-based, user-friendly and code-free platform (https://digitalphenomics.com) that fully automates the development of predictive models from quantitative “omics” data. Here, we show the application of this tool with the development of cancer survival prognostics models using real-life data from breast, lung and renal cancer transcriptomes. We report and compare their sensitivity, specificity, accuracy and Receiver Operating Characteristic (ROC) curve Area Under the Curve (AUC). Further, we report the associated sets of genes (biomarkers) and their expression pattern that are predictive of cancer survival. Moreover, we made our models available as online tools to generate prognostic predictions based on the gene expression of the biomarkers. In conclusion, we demonstrated that our tool is a robust user-friendly solution to develop bespoke predictive tools from “omics” data which facilitate precision medicine introduction to the point-of-care.
Keywords
software tools; bioinformatics; cancer prognostics; predictive modelling
Subject
Medicine and Pharmacology, Oncology and Oncogenics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
