Scientist Stories: Eran Segal, Unraveling the functional impact of thousands of p53 mutations

Eran Segal heads the Human Phenotype Project, a large-scale (more than 10,000 participants) deep-phenotype prospective longitudinal cohort and biobank that his lab established, aimed at identifying novel molecular markers with diagnostic, prognostic and therapeutic value, and at developing prediction models for disease onset and progression. The deep profiling includes medical history, lifestyle and nutritional habits, vital signs, anthropometrics, blood tests, continuous glucose and sleep monitoring, and molecular profiling of the transcriptome, genetics, gut and oral microbiome, metabolome and immune system. Segal’s analysis of this data provides novel insights into potential drivers of obesity, diabetes, and heart disease, and identifies hundreds of novel markers at the microbiome, metabolite, and immune system level. The predictive models developed can be translated into personalized disease prevention and treatment plans, and to the development of new therapeutic modalities based on metabolites and the microbiome. Segal’s research focuses on developing and fine-tuning robust foundation models via Self-supervised Learning (SSL) techniques based on the data of the Human Phenotype Project. The models draw inspiration from large language models (LLMs) like OpenAI’s GPT-series models, but extend them by handling diverse clinical and multi-omics data modalities, including imaging, time-series, tabular, and sequencing-based data. While LLMs can understand and generate human-like text, here the focus is on creating multi-modal models that can effectively analyze heterogeneous types of medical data and be tailored to the biomedical domain. The multi-modal models developed excel at extracting meaningful features and patterns from each data modality while capturing the complex interplay between different modalities. For example, when analyzing a patient's health status, our models can simultaneously consider their genetic information, imaging data, time-series data from wearables, and tabular data like blood tests. By learning the relationships between these modalities, the models can generate holistic insights into an individual's health, predict disease risks, stratify patients, and identify optimal treatment strategies. By combining different data modalities, the research can unlock new avenues for predictive modeling, disease diagnosis, and personalized medicine. This multi-modal approach has the potential to revolutionize healthcare by harnessing the power of AI to analyze and interpret complex medical data across different modalities. Segal’s lab includes a multi-disciplinary team of computational biologists and experimental scientists in the area of computational and systems biology. The group has extensive experience in machine learning, computational biology, and analysis of heterogeneous high-throughput genomic data. Segal published over 200 peer-reviewed publications, which have been cited over 60,000 times. He received several awards and honors for his work, including the Overton prize, awarded annually by the International Society for Bioinformatics (ICSB) to one scientist for outstanding accomplishments in computational biology, and the Michael Bruno award. He was also elected as an EMBO member and as a member of the young Israeli academy of science. During the COVID-19 pandemic, Prof. Segal developed models for analyzing the dynamics of the pandemic and served as a senior advisor to the government of Israel. Segal was awarded a B.Sc. in Computer Science summa cum laude from Tel-Aviv University, and a Ph.D. in Computer Science and Genetics, from Stanford University. Before joining the Weizmann Institute, Segal held an independent research position at Rockefeller University, New York.
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