Classify the given genetic variations/mutations based on evidence from text-based clinical literature. Theory for Cancer Diagnosis Project (#cancer-diagnosis) In this project, genetic variations/mutations are classified based on clinical literature and text-based evidence. The core idea is to automate the classification of genetic mutations that play a critical role in cancer development. These mutations are extracted and classified by examining relevant clinical literature to predict their impact, allowing better diagnosis and personalized treatment plans for cancer patients.
Objective: The objective of this project is to build a machine learning-based system that can:
Extract Genetic Mutations: Identify and extract genetic variations from raw clinical text using natural language processing (NLP) techniques. Classify Mutations: Classify these variations based on their relevance to cancer, using evidence from text-based clinical reports and literature. Predict Clinical Significance: Assess whether these mutations are benign, pathogenic, or uncertain, and provide clinical insights. Steps: Data Extraction: Clinical texts, publications, and medical reports are processed using NLP to extract key genetic variations. Text Classification Models: Build models using natural language processing to understand and classify the clinical significance of the mutation mentioned in the literature. Supervised Learning: Train the model using pre-labeled data, where genetic variations are associated with known outcomes (benign, pathogenic). Evaluation: Assess the model's accuracy and effectiveness using metrics like accuracy, precision, and F1 score. Applications: Clinical Diagnosis: Helps oncologists identify actionable genetic mutations in patients. Personalized Treatment: Supports personalized cancer treatment by predicting the significance of mutations. Automated Literature Review: Automates the process of reviewing clinical research related to specific genetic mutations.