SynaptiX - A Biotech Codeathon

SynaptiX is a dynamic codeathon where biology meets technology. It brings together biologists and computer science engineers to tackle real-world challenges and build impactful solutions. By fostering interdisciplinary collaboration, Synaptix drives innovation at the intersection of computer science and diverse fields of biology, empowering participants to create technology that can shape the future of life sciences. Domains: The two main domains are: AI in Bioinformatics: Pathogenic Variant Classification: An AI-based machine learning model that analyzes genetic variants using publicly available genomic databases to predict whether a mutation is pathogenic or benign, supporting early diagnosis and precision medicine. Antibiotic resistance predictor: A machine learning system that uses bacterial whole-genome sequencing data to predict whether a bacterial isolate is resistant or susceptible to a specific antibiotic, enabling faster and more accurate treatment decisions. AI-Based Toxicity & Adverse Reaction Prediction System: An AI model that predicts toxicity profiles and potential adverse drug reactions of pharmaceuticals, biologics, or nutraceuticals using chemical, molecular, and clinical datasets to enhance drug safety assessment. AI-Based Model for Predicting Host–Pathogen Interactions: An AI-driven computational framework that predicts molecular interactions between host and pathogen proteins using genomic and proteomic data, helping identify virulence mechanisms and potential therapeutic targets. AI-Based Antimicrobial Resistance (AMR) forecasting system: An intelligent system that analyzes microbial genomic mutations, epidemiological trends, and antibiotic usage data to predict emerging resistance patterns and support antimicrobial stewardship strategies. AI-Enhanced Drug Repurposing:  An AI model that scans medical literature and genomic data to suggest existing drugs that could treat new conditions. HealthTech: AI-Driven Clinical Trial Matcher: An AI system that analyzes patient records and trial criteria to match individuals with the most suitable clinical trials, improving recruitment efficiency. Automated Medical Scribe: An AI assistant that listens to doctor-patient conversations, transcribes medical notes, and generates structured EHR entries in real time. AI-Powered Remote Wound Assessment: A smartphone-based AI that assesses wound healing using image analysis, helping doctors remotely monitor infection risks. AI-Powered Personalized Nutrition Advisor: An AI tool that provides personalized dietary recommendations based on health data and pregnancy needs, ensuring optimal nutrition for maternal and overall health. Real-Time NeuroCardiac Digital Twin: An AI-powered system that integrates ECG & EEG data for continuous health monitoring, anomaly detection, and personalized insights, bridging cardiac and neural health for precision medicine. Cognitive Decline Detection AI: An AI tool that detects early signs of Alzheimer’s and dementia by analyzing speech patterns, eye movements, and reaction times. Participants may choose one of the above problem statements or come up with their own in the domain of their choosing Who Can Participate? Undergraduate engineering students are eligible.  Cross-year and cross-college participation is encouraged.  Team Structure: Team Size: upto 4 members Cross-disciplinary teams encouraged. Each participant can be part of only one team No changes in team composition after registration closes Rewards & Prizes: Prizepool upto Rs. 10,000/- Certificates will be provided for participants and winners.