1 citations · 1 across the 8 of their papers we have counts for
9 papers
DoAtlas-2: A Foundation for Self-Evolving Causal Biomedical Discovery
Yulong Li, Rong Xia, Yuxuan Zhang +12
We introduce DoAtlas-2, a foundation for self-evolving causal biomedical discovery that organizes knowledge around causal mechanisms and advances through external evidence from hum…
Automatic Ordinary Differential Equations Discovery For Biological Systems Using Large Language Model Powered Agentic System
David Krongauz, Arad Zulti, Eran Segal +1
Automatic scientific discovery has long been a goal of computational scholars - a machine that can discover nature's secrets on its own, moving computational systems beyond data-fi…
MOLAR: Learning Multimodal Molecular Representations from Noisy Labels
Yingxu Wang, Kunyu Zhang, Nan Yin +2
Motivation: Noisy labels are a common challenge in molecular property prediction because molecular annotations are often obtained from assays, curated databases, or weak annotation…
Simulating clinical interventions with a generative multimodal model of human physiology
Guy Lutsker, Gal Sapir, Jordi Merino +7
Understanding how human health changes over time, and why responses to interventions vary between individuals, remains a central challenge in medicine. Here we present HealthFormer…
DoAtlas-1: A Causal Compilation Paradigm for Clinical AI
Yulong Li, Jianxu Chen, Xiwei Liu +9
Medical foundation models generate narrative explanations but cannot quantify intervention effects, detect evidence conflicts, or validate literature claims, limiting clinical audi…
HPP-Voice: A Large-Scale Evaluation of Speech Embeddings for Multi-Phenotypic Classification
David Krongauz, Hido Pinto, Sarah Kohn +2
Human speech contains paralinguistic cues that reflect a speaker's physiological and neurological state, potentially enabling non-invasive detection of various medical phenotypes.…
Improving Diseases Predictions Utilizing External Bio-Banks
Hido Pinto, Eran Segal
Machine learning has been successfully used in critical domains, such as medicine. However, extracting meaningful insights from biomedical data is often constrained by the lack of…
Toward AI-Driven Digital Organism: Multiscale Foundation Models for Predicting, Simulating and Programming Biology at All Levels
Le Song, Eran Segal, Eric Xing
We present an approach of using AI to model and simulate biology and life. Why is it important? Because at the core of medicine, pharmacy, public health, longevity, agriculture and…
Causal Representation Learning from Multimodal Biomedical Observations
Yuewen Sun, Lingjing Kong, Guangyi Chen +10
Prevalent in biomedical applications (e.g., human phenotype research), multimodal datasets can provide valuable insights into the underlying physiological mechanisms. However, curr…