QuanAnts Machine: A Quantum Algorithm for Biomarker Discovery

TL;DR

The paper introduces QuantAnts Machine, a quantum algorithm inspired by ant colony optimization, designed to efficiently discover biomarkers in complex genomic data for biomedical applications.

Contribution

It presents a novel quantum algorithm that extends classical ACO to multi-domain genomic data, enabling efficient biomarker discovery in large search spaces.

Findings

Discovered new biomarkers for RAS-activation pathway

Identified potential therapeutic targets

Demonstrated effectiveness in clinical-associated genomic data

Abstract

The discovery of biomarker sets for a targeted pathway is a challenging problem in biomedical medicine, which is computationally prohibited on classical algorithms due to the massive search space. Here, I present a quantum algorithm named QuantAnts Machine to address the task. The proposed algorithm is a quantum analog of the classical Ant Colony Optimization (ACO). We create the mixture of multi-domain from genetic networks by representation theory, enabling the search of biomarkers from the multi-modality of the human genome. Although the proposed model can be generalized, we investigate the RAS-mutational activation in this work. To the end, QuantAnts Machine discovers rarely-known biomarkers in clinical-associated domain for RAS-activation pathway, including COL5A1, COL5A2, CCT5, MTSS1 and NCAPD2. Besides, the model also suggests several therapeutic-targets such as JUP, CD9, CD34 and CD74.