Friends and colleagues often ask, “What are the top 100 important "AI in Biology" papers that provide broad insights into the field?” 📚🔬 While narrowing it down to exactly 100 is no small feat, I’ve curated a list of foundational and impactful #BioAI papers. I'm sure the list would exceed more than 100 given the relentless expansion of this thrilling field! Please follow this thread for key insights, and please feel free to suggest any papers I may have missed! With my background and interests, I prioritize papers in #AgingBiology, #CellBiology, and #Neuroscience. I’ll keep this thread pinned to my profile and update it regularly. I encourage everyone to add more papers from their own expertise—let’s make this interactive and foster engaging discussions! Here is a collection of essential #AIbio papers. #100_AIBio_Papers #AIBio_papers #AIBio_Chat #AIneuro_papers #AI_AgingBio_papers

Neural network #journal_club of this week Summary: In this study, Obermeyer et al., have developed a deep-learning model to identify a novel ECG biomarker for sudden cardiac death, outperforming the current medical standard of left ventricular ejection fraction. By analyzing massive datasets from Sweden, the USA, and Taiwan, the study isolates a high-risk group that frequently goes undetected by traditional clinical screenings. This group experiences significantly higher rates of lethal arrhythmias, yet observational data suggests they may gain a substantial mortality benefit from implanted defibrillators. To understand the model's logic, the authors paired it with a generative model to visualize the specific waveform changes associated with high risk. These visualizations revealed a previously undescribed slurring of the QRS complex, potentially linked to diffuse myocardial fibrosis. This discovery offers a scalable tool for preventative cardiology and provides new mechanistic insights into a widespread medical problem. Summarized this paper with slides in the thread. #Bioelectricity #Cardiology #AIinHealthcare #DeepLearning #ECG #Electrophysiology #Electrocardiogram #SuddenCardiacDeath #MedicalAI #ClinicalAI #HealthcareInnovation #BioAI

1/12 I am excited to share our study published today @NeuroCellPress. In this study, we described nanoscopic and transcriptomic correlates that underlie synaptic diversity. Thanks to @JTroyLittleton and colleagues

Happy 4th of July! 🇺🇸🎆 As an immigrant scientist👨‍🔬and father (3X) 👨‍👩‍👧‍👦, I am grateful for the opportunity to build a career in research, raise my family, and contribute to a culture that values innovation and free inquiry 📚. The pursuit of knowledge, the exchange of ideas, and the belief that tomorrow can be better than today continue to inspire me. 🚀

From virtual experiments to biomedical insight with synthetic data | Nature Machine Intelligence nature.com/articles/s42256-0…

Conditional Monge Gap enables generalizable single-cell perturbation modelling | Nature Machine Intelligence nature.com/articles/s42256-0…