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A foundation model for clinician-centered drug repurposing

Kexin Huang, Payal Chandak, Qianwen Wang, Shreyas Havaldar, Akhil Vaid, Jure Leskovec, Girish N. Nadkarni, Benjamin Scott Glicksberg et al.

Nature Medicine 2024 cited by 239

Medicine & Health Computational Drug Discovery Methods partially solved importance 7/10 scale stated

Drug repurposing AI models focus narrowly on diseases that already have drugs, leaving diseases with limited or no treatment options unaddressed.

Details Source Open-access PDF model: TxGNN (graph neural network foundation mod...

Matching patients to clinical trials with large language models

Qiao Jin, Zifeng Wang, Charalampos S. Floudas, Fangyuan Chen, Changlin Gong, Dara Mark Bracken-Clarke, Elisabetta Xue, Yifan Yang et al.

Nature Communications 2024 cited by 283

Medicine & Health Artificial Intelligence in Healthcare and Education solved importance 7/10 scale stated

Patient recruitment for clinical trials is challenging; matching patients to eligible trials manually does not scale.

Details Source Open-access PDF model: TrialGPT (LLM-based framework)

Cross-protein transfer learning substantially improves disease variant prediction

Milind Jagota, Chengzhong Ye, Carlos Albors, Ruchir Rastogi, Antoine Koehl, Nilah M. Ioannidis, Yun S. Song

Genome biology 2023 cited by 67

Biology & Life Sciences Genomics and Rare Diseases ai assisted importance 7/10 scale stated

Most human missense genetic variants have unknown disease effects, leaving clinicians unable to interpret them.

Details Source Open-access PDF model: CPT-1 (cross-protein transfer model)

Large language models encode clinical knowledge

Karan Singhal, Shekoofeh Azizi, Tao Tu, S. Sara Mahdavi, JASON KAI WEI LEE, Hyung Won Chung, Nathan Scales, Ajay Kumar Tanwani et al.

Nature 2023 cited by 3,955

Medicine & Health Artificial Intelligence in Healthcare and Education partially solved importance 7/10 scale stated

Reliably assessing whether large language models possess real clinical knowledge, beyond limited automated benchmarks.

Details Source Open-access PDF model: PaLM (540B parameter) and Flan-PaLM

Deep learning-based survival prediction for multiple cancer types using histopathology images

Ellery Wulczyn, David F. Steiner, Zhaoyang Xu, Apaar Sadhwani, Hongwu Wang, Isabelle Flament-Auvigne, Craig H. Mermel, Po-Hsuan Cameron Chen et al.

PLoS ONE 2020 cited by 265

Medicine & Health AI in cancer detection ai assisted importance 7/10 scale stated

Predicting disease-specific survival risk for cancer patients at diagnosis from histopathology images, where existing staging and clinical variables leave room for better risk stratification.

Details Source Open-access PDF model: weakly-supervised deep learning system; sp...

Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure

Jan Zrimec, Christoph S Börlin, Filip Buric, Azam Sheikh Muhammad, Rhongzen Chen, Verena Siewers, Vilhelm Verendel, JENS KVIST NIELSEN et al.

Nature Communications 2020 cited by 172

Biology & Life Sciences Genomics and Chromatin Dynamics ai assisted importance 7/10 scale stated

It was unclear how coding and non-coding regions of the gene regulatory structure interact to determine mRNA expression levels.

Details Source Open-access PDF model: not stated in source

Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning

Michael D. Abràmoff, Yiyue Lou, Ali Erginay, Warren Clarida, Ryan E. Amelon, James C. Folk, Meindert Niemeijer

Investigative Ophthalmology & Visual Science 2016 cited by 1,095

Medicine & Health Retinal Imaging and Analysis solved importance 7/10 scale stated

Automated detection of diabetic retinopathy from fundus images needed improvement; a deep-learning-enhanced algorithm was benchmarked against a consensus reference standard from three US board-certified retinal specialists.

Details Source Open-access PDF model: IDx-DR version X2.1 (deep-learning-enhance...

Target sequence-conditioned design of peptide binders using masked language modeling

Tianlai Chen, Zachary Quinn, Madeleine Dumas, Christina Yi Peng, Lauren Hong, Moises Lopez-Gonzalez, Alexander A. Mestre, Rio Watson et al.

Nature Biotechnology 2025 cited by 59

Biology & Life Sciences Chemical Synthesis and Analysis partially solved importance 7/10 scale stated

Designing de novo linear peptide binders for disease targets, including 'undruggable' ones, is difficult without stable structural information.

Details Source Open-access PDF model: PepMLM (finetuned ESM-2)

Machine learning-based meta-analysis reveals gut microbiome alterations associated with Parkinson’s disease

Stefano Romano, Jakob Wirbel, Rebecca Ansorge, Christian Schudoma, Quinten R. Ducarmon, Arjan Narbad, Georg Zeller

Nature Communications 2025 cited by 61

Medicine & Health Gut microbiota and health partially solved importance 7/10 scale stated

There was no consensus on which gut microbiome features are associated with Parkinson's disease or how well they can diagnose it across multiple studies.

Details Source Open-access PDF model: not stated in source

Predicting Short-Term Intensity Change in Tropical Cyclones Using a Convolutional Neural Network

Sarah M. Griffin, Anthony Wimmers, Christopher S. Velden

Weather and Forecasting 2024 cited by 17

Climate & Environment Tropical and Extratropical Cyclones Research partially solved importance 7/10 scale stated

Predicting current intensity and short-term intensity change of tropical cyclones more skillfully than existing operational methods.

Details Source model: D-MINT and D-PRINT (convolutional neural n...

Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors

Joosep Pata, Eric Wulff, Farouk Mokhtar, David Southwick, Mengke Zhang, Maria Girone, Javier Mauricio Duarte

Communications Physics 2024 cited by 9

Physics & Astronomy Particle Detector Development and Performance ai assisted importance 7/10 scale stated

Reconstructing particles efficiently and accurately in the highly granular detectors expected at the High-Luminosity LHC and Future Circular Collider is computationally challenging.

Details Source Open-access PDF model: Graph neural network and kernel-based tran...

U-Plume: automated algorithm for plume detection and source quantification by satellite point-source imagers

Jack Bruno, Dylan Jervis, Daniel J. Varon, Daniel J. Jacob

Atmospheric measurement techniques 2024 cited by 39

Climate & Environment Atmospheric and Environmental Gas Dynamics partially solved importance 7/10 scale stated

Detecting atmospheric methane plumes and quantifying point-source rates from growing high-resolution satellite imagery is labor-intensive and not scalable.

Details Source Open-access PDF model: U-Net; convolutional neural network (CNN)

Simultaneous discovery of quantum error correction codes and encoders with a noise-aware reinforcement learning agent

Jan Ollé, Remmy A. M. Zen, Matteo Ballerini Puviani, Florian Marquardt

npj Quantum Information 2024 cited by 23

Physics & Astronomy Quantum Computing Algorithms and Architecture partially solved importance 7/10 scale stated

Discovering quantum error correction codes and encoding circuits tailored to specific qubit hardware is critical and hard to do manually; prior reinforcement learning approaches were severely limited in scalability.

Details Source Open-access PDF model: Reinforcement learning agent (noise-aware...

Early detection of dementia through retinal imaging and trustworthy AI

Jinkui Hao, William Robert Kwapong, Ting Shen, Huazhu Fu, Yanwu Xu, Qinkang Lu, Shouyue Liu, Jiong Zhang et al.

npj Digital Medicine 2024 cited by 58

Medicine & Health Retinal Imaging and Analysis ai assisted importance 7/10 scale stated

Alzheimer's disease lacks a simple, affordable early detection method, leaving early-onset AD and mild cognitive impairment hard to identify.

Details Source Open-access PDF model: Eye-AD (deep learning, multilevel graph re...

Congenital heart disease detection by pediatric electrocardiogram based deep learning integrated with human concepts

Jintai Chen, Shuai Huang, Ying Zhang, Qing Hua Chang, Yixiao Zhang, Dantong Li, Jia Qiu, Lianting Hu et al.

Nature Communications 2024 cited by 76

Medicine & Health ECG Monitoring and Analysis solved importance 7/10 scale stated

Detecting congenital heart disease in children early using low-cost, non-invasive pediatric ECG instead of more invasive or expensive methods.

Details Source Open-access PDF model: CHDdECG (deep learning)

Improving deep learning protein monomer and complex structure prediction using DeepMSA2 with huge metagenomics data

Wei Hui Zheng, Qiqige Wuyun, Yang Li, Chengxin Zhang, Lydia Freddolino, Yang Zhang

Nature Methods 2024 cited by 130

Biology & Life Sciences Protein Structure and Dynamics partially solved importance 7/10 scale stated

Protein tertiary and quaternary structure prediction loses accuracy when multiple-sequence alignments (MSAs) are poorly constructed, especially for complexes.

Details Source Open-access PDF model: DeepMSA2 pipeline (with deep learning stru...

Learning high-accuracy error decoding for quantum processors

Johannes Bausch, Andrew Senior, Francisco J. H. Heras, Thomas Edlich, Alex Davies, Michael E. Newman, Cody Jones, Kevin J. Satzinger et al.

Nature 2024 cited by 90

Physics & Astronomy Quantum Computing Algorithms and Architecture partially solved importance 7/10 scale stated

Building large-scale quantum computers requires accurately decoding noisy syndrome information from quantum error-correction codes such as the surface code.

Details Source Open-access PDF model: Recurrent, transformer-based neural networ...