AP22686112 "Investigation of somatic mutations based on single-cell RNA using machine learning methods in patients with peripheral artery disease"

Relevance

Peripheral arterial disease (PD), as part of cardiovascular diseases (CVD), remains one of the leading causes of disability and mortality. Modern diagnostic methods do not always make it possible to identify individual genetic predispositions. The development of an effective pipeline for detecting somatic mutations using AI and genetic tools will improve diagnostic accuracy, speed up the choice of therapy and reduce the burden on the healthcare system.

Purpose

The main goal of this study is to create an inclusive pipeline for detecting somatic mutations in patients with peripheral artery disease (PA) using tools such as Gemini, Cosmic and Monocle, as well as various machine learning methods. The goal of the first year is to review and write an overview article about precomputation tools and gene expression clustering tools.

Expected and achieved results

At the current stage of research, optimal methods for preparing genetic data for cluster analysis have been loaded (performed on ultra-high-performance computing equipment, documents on the lease of high-performance computing equipment. Using these tools, clusters were obtained using gene expression and various clustering algorithms) and a review article was written about this (on December 25, 2024, it will be published in the Bulletin of KBTU, since it is very difficult to queue, and the article became possible only after funding was received). As a result of this work, we were able to analyze the pathways of gene development and identify mutations at the next stage.

Research team members with their identifiers (Scopus Author ID, Researcher ID, ORCID, if available) and links to relevant profiles