In this issue:
Welcome back to your weekly dose of AI news for Life Science!
This week, we have some exciting new models lined up for you
BiomedParse: AI-driven unified biomedical image analysis across modalities 🩻
MolPipeline: A python package for processing molecules with RDKit in scikit-learn 🤖
Dive into these game-changing innovations and explore how they are transforming the biotech and healthcare landscapes!
BiomedParse: AI-driven unified biomedical image analysis across modalities 🩻
Biomedical imaging plays a critical role in scientific discovery, enabling detailed insights into structures ranging from cellular components to organ systems. However, traditional approaches to image analysis often treat segmentation, detection, and recognition as separate tasks, limiting their potential. Introducing BiomedParse, a foundation model designed to address these challenges by integrating all three tasks into a unified framework across nine imaging modalities.
📌 Key Insights:
Joint Learning by integrating segmentation, detection, and recognition tasks together across 9 data modalities, like MRI, CT, and X-rays and Ultrasound
Trained on large data containing 3.4 million image–label triples and 6.8 million image–description triples
Substantial improvements over previous models (MedSAM and SAM)
MolPipeline: A python package for processing molecules with RDKit in scikit-learn 🤖
In cheminformatics, RDKit and scikit-learn are essential for molecular data processing and predictive modeling. Combining these tools enables us to build sophisticated models, leveraging both chemical and statistical insights. MolPipeline enhances this integration by offering an automated, scalable pipeline with consistent error handling and customizable processing steps, addressing limitations in handling diverse cheminformatics tasks and large datasets. Unlike previous tools, MolPipeline provides a unique end-to-end workflow that automatically manages processing errors and allows flexible adaptation to various project requirements, making it a more robust and versatile solution for molecular machine learning.
📌 Key Insights:
New pipeline, implementing standard cheminformatics tasks using RDKit while complying with scikit-learn’s pipeline API.
Consistent handling of processing errors.
Proof of concept by building Random Forest models with hyperparameter optimization on the BBBP dataset.
MassiveFold: Parallelising protein structure prediction 🧬
Massive sampling in AlphaFold enables access to increased structural diversity. In combination with its efficient confidence ranking, this unlocks elevated modeling capabilities for monomeric structures and foremost for protein assemblies. However, the approach struggles with GPU cost and data storage. Here we introduce MassiveFold, an optimised and customisable version of AlphaFold that runs predictions in parallel, reducing the computing time from several months to hours. Introducing MassiveFold, a scalable framework able to run on anything from a single computer to a large GPU infrastructure, where it can fully benefit from all the computing nodes.
📌 Key Insights:
Parallelised predictions, reducing computing time from several months to hours
Combines the framework of AlphaFold with the enhanced sampling of AFsample and the added functionality from ColabFold.
Easy to install and use and highly customisable
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