IntelliGenes: a novel machine learning pipeline for biomarker discovery and predictive analysis using multi-genomic profiles

Author:

DeGroat William1,Mendhe Dinesh1,Bhusari Atharva1,Abdelhalim Habiba1,Zeeshan Saman2,Ahmed Zeeshan13ORCID

Affiliation:

1. Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers, The State University of New Jersey , New Brunswick, NJ 08901, United States

2. Rutgers Cancer Institute of New Jersey, Rutgers University , New Brunswick, NJ 08901, United States

3. Department of Medicine, Robert Wood Johnson Medical School, Rutgers Health , New Brunswick, NJ 08901, United States

Abstract

Abstract Summary In this article, we present IntelliGenes, a novel machine learning (ML) pipeline for the multi-genomics exploration to discover biomarkers significant in disease prediction with high accuracy. IntelliGenes is based on a novel approach, which consists of nexus of conventional statistical techniques and cutting-edge ML algorithms using multi-genomic, clinical, and demographic data. IntelliGenes introduces a new metric, i.e. Intelligent Gene (I-Gene) score to measure the importance of individual biomarkers for prediction of complex traits. I-Gene scores can be utilized to generate I-Gene profiles of individuals to comprehend the intricacies of ML used in disease prediction. IntelliGenes is user-friendly, portable, and a cross-platform application, compatible with Microsoft Windows, macOS, and UNIX operating systems. IntelliGenes not only holds the potential for personalized early detection of common and rare diseases in individuals, but also opens avenues for broader research using novel ML methodologies, ultimately leading to personalized interventions and novel treatment targets. Availability and implementation The source code of IntelliGenes is available on GitHub (https://github.com/drzeeshanahmed/intelligenes) and Code Ocean (https://codeocean.com/capsule/8638596/tree/v1).

Funder

Department of Medicine, Robert Wood Johnson Medical School

Rutgers Institute for Health, Health Care Policy

State University of New Jersey

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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