A framework for state-of-the-art pre-trained bio foundation models on genomics and transcriptomics modalities.

A Full Stack Variant Effect Predictor leveraging the Evo2 LLM for Single Nucleotide Variant(SNV) pathogenicity analysis, powered by a Modal‑deployed FastAPI GPU backend and a Next.js + Shadcn UI frontend

A deep learning model (EVO2-500M) for predicting host specificity of eukaryote-infecting viruses CDNA sequence

a web app that can classify how likely specific mutations in DNA are to cause diseases (variant effect prediction). We will deploy and use the state-of-the-art Evo2 large language model, and use it to predict the pathogenicity of single nucleotide variants (SNVs)

Reintegrate sd2iec evo2 support

This project critically analyzes the data pipeline of EVO 2, a cutting-edge AI model for genomic research developed by the Arc Institute and collaborators. The focus lies in evaluating its risks, ethical challenges, and governance structures in the context of data privacy, genome editing, and AI-driven biomedical innovation.

AI-powered variant pathogenicity prediction for cancer genes using Evo2-7B evolutionary foundation model. Combines deep learning, VEP annotation, gnomAD frequencies, and explainable AI for precision oncology. Built with Next.js + Modal + PostgreSQL.

Fine-tuning pipeline for NVIDIA BioNeMo Evo2 1B on genomics sequences, including preprocessing, training, inference, embeddings, and evaluation.

Aligning multi-hybrid foundation models for genome generation with domain adaptation to human genetics

An Analysis application that allows single nucleotide variations in genes to help predict "pathogenic" or not using the Evo2 LLM Model