“ Machine Learning- Based Early Detection of Biomarkers from Omics & Brain Imaging Patterns: Revolutionizing Neurological Disorders Diagnosis and Treatment “
Abstract Biomarkers are chemical indicators used to assess disease risk. Identification and detection of biomarkers are critical for diagnosis. Currently, commonly used biomarkers for detecting neurological disorders include body fluid (cerebrospinal fluid and blood) and imaging markers. The early identification of biomarkers has the potential to completely alter how neurological disorders are diagnosed and treated. The goal of this project is to provide a machine learning-based method for identifying and analyzing biomarkers suggestive of neurological illnesses by fusing omics data with brain imaging patterns, by utilizing the robustness of machine learning algorithms, intended to enhance the precision and efficacy of biomarker detection, enabling early diagnosis and intervention.
This initiative will combine brain imaging patterns from methods like MRI and PET scans with multi-omics data, including genetic, transcriptomic, and proteomic details.
Modern machine learning methods, such as deep learning models and feature selection algorithms, will be used by the system to extract useful data from challenging omics and imaging datasets. The algorithm will learn to distinguish between disease-specific biomarkers and typical biological fluctuations through intensive training and validation using large-scale datasets that include both healthy individuals and patients with neurological disorders.
Researchers, physicians, and other healthcare professionals can input omics datasets and brain imaging data into the built-in app to get in-depth analysis and forecasts. It will give in-depth reporting on the discovered biomarkers, their importance, and any potential effects on diagnosis, prognosis, and treatment decision-making.
Early intervention, individualized treatment plans, and better patient outcomes can result from the timely discovery of biomarkers. The app's user-friendly interface and seamless connection with current medical systems will also promote broad adoption and influence.
Pl refer to the document titled - NeurOmicXPert attached for more insights.
If you came across this project and are interested in contributing/collaborating, PLEASE send an email to [email protected] with a detailed plan of action.