Papers

Comprehensive review and assessment of multi-species splicing variant prediction: task-specific deep learning models and genomic foundation models.

Artificial Intelligence in genomics: a comprehensive survey of methods, resources, challenges, and prospects.

Evolving computational paradigms for noncoding variant pathogenicity prediction.

ITCNET: a visual and language pretraining model based on multimodal fusion in predicting the breast cancer subtypes on DCE-MRI.

Automated Morphological Profiling via Deep Learning-Based Segmentation for High-Throughput Phenotypic Screening.

Pre-training genomic language model with variants for better modeling functional genomics.

Crosstalk between RNA secondary and three-dimensional structure prediction: a comprehensive study.

Progress and new challenges in image-based profiling.

Illuminating cell states by a comprehensive and interpretable single cell foundation model.

Machine learning for RNA secondary structure prediction: a review of current methods and challenges.

imAgeScore, a Cell Painting-Based Predictor of Cellular Age for High-throughput Drug Screening Applications

rbio1 - training scientific reasoning LLMs with biological world models as soft verifiers

RiNALMo: general-purpose RNA language models can generalize well on structure prediction tasks.

Predicting cellular responses to perturbation across diverse contexts with State

Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction

Virtual Cell Challenge: Toward a Turing test for the virtual cell.

Scaling Large Language Models for Next-Generation Single-Cell Analysis

Scupa: single-cell unified polarization assessment of immune cells using the single-cell foundation model.

Genome modeling and design across all domains of life with Evo 2

Simulating 500 million years of evolution with a language model.