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Bio x ML Hackathon

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Morphologic AI
Morphologic AI

Using Cell Segmentation to improve explainability of Recursion rxrx.ai datasets and Phenoprint deep learning embeddings

Trevor D McKee Aru Singh
1 0
PhenoSeq
PhenoSeq

We want to model how a network of proteins contributes to organism-scale phenotypes, such as cancer growth, by using pLM embeddings from ESM and propagating them over PPIs using GNNs.

G D lpkoh Bomin Rahmani Edward Sun + 3
3 0
EvoCapsid
EvoCapsid

An end-to-end ML workflow capable of tailored protein design for open sourced development of a gene delivery agent, from cargo to carrier to targeting moieties

Joseph Alzagatiti
0 0
CHOFormance: Codon Optimization in Cricetulus griseus
CHOFormance: Codon Optimization in Cricetulus griseus

Improve the protein expression of your synthetic gene in the CHO expression system by over 200x, for the same amount of time, plasmid, and cost.

Rishab Jain Arun Moorthy Tyler Rose Alan Ma + 5
3 0
Automated Data Scientist (GeneEase)
Automated Data Scientist (GeneEase)

GeneEase is pioneering an AI-powered platform that revolutionizes genomic data exploration

Rob Blaine
0 1
Protein Design with Cellular Context
Protein Design with Cellular Context

We propose Protein Design with Cellular Context, a method based on protein and DNA language models to predict expression of a given protein for every cell type in the mouse brain.

Guillermo Herrera
0 0
de-MS-tifying dark Mols
de-MS-tifying dark Mols

We are developing an end-to-end solution for in silico fragmentation in mass spectrometry, using a BART-based model with adversarial training to predict spectra from molecular structures.

ChrisTho23 Thomassin
1 0
The Alchemists
The Alchemists

Our problem was to predict how a given molecule would fragment in tandem MS. We built a model that leverages the pretrained chemistry LLM called ChemBERTa to solve this problem.

Lars Mennen
0 0
Learning features in pLM using sparse autoencoders
Learning features in pLM using sparse autoencoders

What features are pLMs learning? Inspired mechanistic interpretability work on LLMs, we train sparse autoencoders on pLMs. We identify highly context specific features at the residue and motif scale.

Etowah Adams Steven Yu PranaySatya Liam Bai + 1
1 0
Naturals: Enveda Challenge 2
Naturals: Enveda Challenge 2

Predicting chemist-perceived molecular similarity

David Kubánek
0 0
Simulating 500 years of evolution in solution
Simulating 500 years of evolution in solution

ESM3 predicts protein 3D structures with high accuracy, yet proteins are flexible and undergo conformational changes in solution. Our project integrates this dynamic behavior into ESM3.

Till Siebenmorgen
0 0
Epitope prediction web platform
Epitope prediction web platform

Open-source web platform for AI powered B-cell and T-cell epitope prediction.

Zachary Pfizenmaier
0 0
De Novo Lead Generation & Ranking for Enzyme & Nanobody
De Novo Lead Generation & Ranking for Enzyme & Nanobody

Use assay data for an enzyme and nanobody to design enzymes with improved activity/stability and nanobodies with increased binding affinity. Apply SOTA and rigorous computational/biological methods.

Nikhil Haas
0 0
Adapter-guided controllable generation for proteins
Adapter-guided controllable generation for proteins

Want to control generation with bio LLMs, but have limited access to frontier models (prompting too much of an art, can't finetune for cost or security reasons)? We explore “adapter-guidance” to help!

Rachel Park Michael Zhang aunell Unell
0 0
Novel Organoid AI Network for Parkinson's Therapy Discovery
Novel Organoid AI Network for Parkinson's Therapy Discovery

Parkinson’s disease lacks effective disease-modifying therapies. We developed a cutting-edge patient-derived organoid platform combined with a Foundation Model-based network for PD therapy discovery.

Jun Yin
0 0
MSEffect
MSEffect

Accelerating scientific discovery with MS/MS prediction. High resolution, interpretable, incorporating unlabelled spectra.

Andreas Burger
0 0
Using EVO to predict phage-bacteria matching
Using EVO to predict phage-bacteria matching

Phage therapy is a hope in combating the antibiotics resistance crisis. In Phagebook, we accurately predict phage-bacteria host interactions, a crucial step toward designing effective phage therapies

Ha Vu Boyang Fu Cindy K. Pino Veronika Dubinkina + 2
6 0
Generative DNA design for plasmid vector engineering.
Generative DNA design for plasmid vector engineering.

Plasmid vectors are essential tools for research, syn bio, biomanufacturing, and gene therapies. We developed a generative plasmid design tool using in silico mutagenesis of genomic language models.

William Connell Himanshu Reddy Aaron Wenteler
1 0
LLM Agents to Cure Children's Cancer
LLM Agents to Cure Children's Cancer

A large language model (LLM) agentic framework for drug discovery in children's cancer.

Vidya Ganapati
0 0

1 – 19 of 19

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