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Using Biomedical Semi-Supervised Machine Learning to Accelerate the Drug Discovery Process
This research proposal explores the implementation of Biomedical Semi-Supervised Machine Learning (ML) in accelerating the drug discovery process, specifically targeting Huntington's Disease (HD). The goal is to develop new, effective treatments for HD by leveraging advanced ML techniques.
Why I Chose This Project
- Better Treatments: Aiming to discover better treatments for HD and other diseases.
- Current Utilization: ML is currently utilized in cancer research and can be adapted for HD.
- HD Specific Focus: HD is a rare, progressive neurodegenerative disease characterized by uncontrolled movements and loss of cognition, necessitating innovative treatment approaches.
- Efficiency: ML has the potential to shorten and reduce the cost of the drug discovery process.
Project Details
Understanding Huntington’s Disease (HD)
Characteristics: HD is marked by uncontrolled movements and cognitive decline.
Current Treatments: Includes drugs for movement control and antipsychotic medications.
Drug Discovery Process
Involves four stages, including FDA approval.
ML Algorithms: Use data inputs to predict potential drug candidates by analyzing current treatment drugs
Machine Learning Implementation
Semi-Supervised ML
Combines labeled and unlabeled data to improve learning accuracy.
Utilizes algorithms such as Divisive Hierarchical Clustering, Naïve Bayes Tree Hybrid, Naïve Bayes, and Decision Tree.
Parameters for ML
Combating HD: Focus on inhibiting aggregation and lowering HD levels.
Treatment Options
Atypical antipsychotic drugs (e.g., olanzapine, risperidone, quetiapine).
DA-depleting agents (e.g., tetrabenazine, deutetrabenazine).
Limitations
Requires additional computational models for calculating binding affinity.
Predicts drugs for the general population, not individual cases.
The machine predicts potential drug candidates, best dosages, administration methods, side effects, and interactions.
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