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Found 8 papers, 4 papers with code
SGAC: A Graph Neural Network Framework for Imbalanced and Structure-Aware AMP Classification
Classifying antimicrobial peptides(AMPs) from the vast array of peptides mined from metagenomic sequencing data is a significant approach to addressing the issue of antibiotic resistance.
Toward AI-Driven Digital Organism: Multiscale Foundation Models for Predicting, Simulating and Programming Biology at All Levels
We present an approach of using AI to model and simulate biology and life.
Causal Representation Learning from Multimodal Biomedical Observations
Theoretically, we consider a nonparametric latent distribution (c. f., parametric assumptions in previous work) that allows for causal relationships across potentially different modalities.
Generative AI Enables Medical Image Segmentation in Ultra Low-Data Regimes
While deep learning has excelled in automating this task, a major hurdle is the need for numerous annotated segmentation masks, which are resource-intensive to produce due to the required expertise and time.
From Glucose Patterns to Health Outcomes: A Generalizable Foundation Model for Continuous Glucose Monitor Data Analysis
Here, we present GluFormer, a generative foundation model for CGM data that learns nuanced glycemic patterns and translates them into predictive representations of metabolic health.
FrackyFrac: A Standalone UniFrac Calculator
UniFrac is a family of distance metrics over microbial abundances, that take taxonomic relatedness into account.
A Multimodal Dataset of 21,412 Recorded Nights for Sleep and Respiratory Research
This study introduces a novel, rich dataset obtained from home sleep apnea tests using the FDA-approved WatchPAT-300 device, collected from 7, 077 participants over 21, 412 nights.
Regularization Learning Networks: Deep Learning for Tabular Datasets
Despite their impressive performance, Deep Neural Networks (DNNs) typically underperform Gradient Boosting Trees (GBTs) on many tabular-dataset learning tasks.
