Papers

Artificial intelligence in the diagnosis of deep vein thrombosis: A scoping review.

Trends in the use of adult-specific preference-weighted health-related quality of life instruments in clinical trials over the past 50 years: a protocol for a meta-research study using deep learning-based natural language processing and large language models.

GARN3: A coarse-grained helix centered technique for RNA 3D structures prediction.

A multi-modal co-attention model for accurate drug-target interaction prediction.

Artificial Intelligence in Selected Domains of Drug Discovery: A Critical Narrative Review.

Evaluation of Five Large Language Models for Parental Education in Pediatric Anesthesia: Reliability and Readability Study.

Comparison of local large language models for extraction of signs and symptoms data from electronic health records.

Research progress of artificial intelligence in high-throughput drug screening.

Artificial intelligence applications in automated dental report generation - a scoping review.

FourierDrug: a domain generalization framework for robust drug response prediction via frequency-space asymmetric attention.

Protein Design Enters the Artificial Intelligence Era: Foundations, Tools, and Emerging Paradigms.

Causal effect estimation from trans-regulatory single-cell CRISPR screens.

Machine Learning-Based Prediction of Polymer Properties Using Structure-Property Relationship Modeling.

Application of Machine Learning in Predicting the Properties of Two-Dimensional Semiconductor Materials.

Accuracy of Deep Learning Models in Detecting Mandibular Furcation Defects on Panoramic Radiographs.

In vitro profiling of Trypanosoma cruzi inhibitors identified from High throughput Screening and application to parasite painting.

Machine Learning in Non-fullerene Organic Solar Cells: Accelerating Discovery, Design, and Understanding.

How artificial intelligence is reshaping citation impact in oral and maxillofacial radiology journals: an 8-year analysis with editorial and clinical implications (JCR 2017-2024).

Machine Learning-Driven QSAR Modeling for pK<sub>a</sub> Prediction of Ionizable Lipids in Lipid Nanoparticles for Hepatic Gene Silencing.

Inferring the qualities of protein-RNA models with graph transformers.

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.

CZ CELLxGENE Discover: a single-cell data platform for scalable exploration, analysis and modeling of aggregated data.

Nucleotide Transformer: building and evaluating robust foundation models for human genomics.

A cell atlas foundation model for scalable search of similar human cells.

Boltz-1 Democratizing Biomolecular Interaction Modeling

Sequence modeling and design from molecular to genome scale with Evo.

Protein language models learn evolutionary statistics of interacting sequence motifs.

Chai-1: Decoding the molecular interactions of life

Large-scale foundation model on single-cell transcriptomics.

scGPT: toward building a foundation model for single-cell multi-omics using generative AI.

AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences.

PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences.

Universal Cell Embeddings: A Foundation Model for Cell Biology

Predicting transcriptional outcomes of novel multigene perturbations with GEARS.

De novo design of protein structure and function with RFdiffusion.

Transfer learning enables predictions in network biology.

Predicting cellular responses to complex perturbations in high-throughput screens.

Genomic benchmarks: a collection of datasets for genomic sequence classification.

JUMP Cell Painting dataset: morphological impact of 136,000 chemical and genetic perturbations

Evolutionary-scale prediction of atomic-level protein structure with a language model.

Robust deep learning-based protein sequence design using ProteinMPNN.

ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning.

ProtGPT2 is a deep unsupervised language model for protein design.

Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq.

The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans.

AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models.

FLIP: Benchmark tasks in fitness landscape inference for proteins

Highly accurate protein structure prediction with AlphaFold.

Cellpose: a generalist algorithm for cellular segmentation.

The mutational constraint spectrum quantified from variation in 141,456 humans.

Evaluating Protein Transfer Learning with TAPE.

scGen predicts single-cell perturbation responses.

Predicting Splicing from Primary Sequence with Deep Learning.

Deep generative modeling for single-cell transcriptomics.

The Encyclopedia of DNA elements (ENCODE): data portal update.

Continuous Automated Model EvaluatiOn (CAMEO) complementing the critical assessment of structure prediction in CASP12.

The Human Cell Atlas.

A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles.

The Image Data Resource: A Bioimage Data Integration and Publication Platform.

A global reference for human genetic variation.

PDB-wide collection of binding data: current status of the PDBbind database.