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Investigating Novel Biomarkers for Predicting Disease Progression in Amyotrophic Lateral Sclerosis using Machine Learning Approaches.

  • Writer: RPIHEF  NGO
    RPIHEF NGO
  • May 29
  • 1 min read

Updated: Jun 20

Dr. Abrahim Sharma, Assistant Professor, Department of Neurology, KLE University, Karnataka


Abstract

Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disease characterized by progressive motor neuron loss, leading to muscle weakness, paralysis, and ultimately death. Predicting disease progression in ALS remains a significant clinical challenge, hindering personalized treatment strategies and clinical trial design. This study proposes to investigate novel biomarkers for predicting disease progression in ALS using advanced machine learning approaches. We hypothesize that a multi-modal integration of genetic, proteomic, metabolomic, and neuroimaging data will yield robust predictive models. Our research will involve collecting longitudinal data from a well-characterized cohort of ALS patients. Genetic analyses will focus on identifying risk alleles and disease-modifying variants. Proteomic and metabolomic profiling of cerebrospinal fluid and blood samples will aim to identify dysregulated pathways associated with disease severity and progression rates. Neuroimaging techniques, including diffusion tensor imaging (DTI) and functional MRI (fMRI), will assess changes in white matter integrity and functional connectivity. Machine learning algorithms, such as random forests, support vector machines, and deep neural networks, will be employed to analyze this complex, high-dimensional dataset. The models will be trained to predict key progression metrics, including forced vital capacity decline, ALS Functional Rating Scale-Revised (ALSFRS-R) scores, and survival time. Cross-validation and external validation will be used to ensure model robustness and generalizability. This research aims to identify a panel of highly predictive biomarkers, offering clinicians a powerful tool for prognostication and enabling the development of more targeted and effective therapeutic interventions for ALS patients.

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