Michal Ozery-Flato
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View article: BioVERSE: Representation Alignment of Biomedical Modalities to LLMs for Multi-Modal Reasoning
BioVERSE: Representation Alignment of Biomedical Modalities to LLMs for Multi-Modal Reasoning Open
Recent advances in large language models (LLMs) and biomedical foundation models (BioFMs) have achieved strong results in biological text reasoning, molecular modeling, and single-cell analysis, yet they remain siloed in disjoint embedding…
View article: Preoperative kidney tumor risk estimation with AI: From logistic regression to transformer
Preoperative kidney tumor risk estimation with AI: From logistic regression to transformer Open
We consider the problem of renal mass risk classification to support doctors in adjuvant treatment decisions following nephrectomy. Recommendation of adjuvant therapy based on the mass appearance poses two major challenges: first, morpholo…
View article: Leveraging Large Language Models to Predict Antibody Biological Activity Against Influenza A Hemagglutinin
Leveraging Large Language Models to Predict Antibody Biological Activity Against Influenza A Hemagglutinin Open
Monoclonal antibodies (mAbs) represent one of the most prevalent FDA-approved modalities for treating autoimmune diseases, infectious diseases, and cancers. However, discovery and development of therapeutic antibodies remains a time-consum…
View article: Corrigendum to “Leveraging large language models to predict antibody biological activity against influenza A hemagglutinin” [Comput Struct Biotechnol J 27 (2025) 1286–1295]
Corrigendum to “Leveraging large language models to predict antibody biological activity against influenza A hemagglutinin” [Comput Struct Biotechnol J 27 (2025) 1286–1295] Open
[This corrects the article DOI: 10.1016/j.csbj.2025.03.038.].
View article: Leveraging large language models to predict antibody biological activity against influenza A hemagglutinin
Leveraging large language models to predict antibody biological activity against influenza A hemagglutinin Open
View article: MAMMAL -- Molecular Aligned Multi-Modal Architecture and Language
MAMMAL -- Molecular Aligned Multi-Modal Architecture and Language Open
Large language models applied to vast biological datasets have the potential to transform biology by uncovering disease mechanisms and accelerating drug development. However, current models are often siloed, trained separately on small-mol…
View article: AI Age Discrepancy: A Novel Parameter for Frailty Assessment in Kidney Tumor Patients
AI Age Discrepancy: A Novel Parameter for Frailty Assessment in Kidney Tumor Patients Open
Kidney cancer is a global health concern, and accurate assessment of patient frailty is crucial for optimizing surgical outcomes. This paper introduces AI Age Discrepancy, a novel metric derived from machine learning analysis of preoperati…
View article: A large dataset curation and benchmark for drug target interaction
A large dataset curation and benchmark for drug target interaction Open
Bioactivity data plays a key role in drug discovery and repurposing. The resource-demanding nature of \textit{in vitro} and \textit{in vivo} experiments, as well as the recent advances in data-driven computational biochemistry research, hi…
View article: Breast Multiparametric MRI for Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer: The BMMR2 Challenge
Breast Multiparametric MRI for Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer: The BMMR2 Challenge Open
Purpose To describe the design, conduct, and results of the Breast Multiparametric MRI for prediction of neoadjuvant chemotherapy Response (BMMR2) challenge. Materials and Methods The BMMR2 computational challenge opened on May 28, 2021, a…
View article: Leveraging Comprehensive Health Records for Breast Cancer Risk Prediction: A Binational Assessment
Leveraging Comprehensive Health Records for Breast Cancer Risk Prediction: A Binational Assessment Open
Breast cancer (BC) risk models based on electronic health records (EHR) can assist physicians in estimating the probability of an individual with certain risk factors to develop BC in the future. In this retrospective study, we used clinic…
View article: Leveraging Comprehensive Health Records for Breast Cancer Risk Prediction: A Binational Assessment
Leveraging Comprehensive Health Records for Breast Cancer Risk Prediction: A Binational Assessment Open
Breast cancer (BC) risk models based on electronic health records (EHR) can assist physicians in estimating the probability of an individual with certain risk factors to develop BC in the future. In this retrospective study, we used clinic…
View article: Impact of the COVID-19 Pandemic on Clinical Findings in Medical Imaging Exams in a Nationwide Israeli Health Organization: Observational Study
Impact of the COVID-19 Pandemic on Clinical Findings in Medical Imaging Exams in a Nationwide Israeli Health Organization: Observational Study Open
Background The outbreak of the COVID-19 pandemic had a major effect on the consumption of health care services. Changes in the use of routine diagnostic exams, increased incidences of postacute COVID-19 syndrome (PCS), and other pandemic-r…
View article: A Competition, Benchmark, Code, and Data for Using Artificial Intelligence to Detect Lesions in Digital Breast Tomosynthesis
A Competition, Benchmark, Code, and Data for Using Artificial Intelligence to Detect Lesions in Digital Breast Tomosynthesis Open
Importance An accurate and robust artificial intelligence (AI) algorithm for detecting cancer in digital breast tomosynthesis (DBT) could significantly improve detection accuracy and reduce health care costs worldwide. Objectives To make t…
View article: A causal inference approach for estimating effects of non-pharmaceutical interventions during Covid-19 pandemic
A causal inference approach for estimating effects of non-pharmaceutical interventions during Covid-19 pandemic Open
In response to the outbreak of the coronavirus disease 2019 (Covid-19), governments worldwide have introduced multiple restriction policies, known as non-pharmaceutical interventions (NPIs). However, the relative impact of control measures…
View article: Impact of the COVID-19 Pandemic on Clinical Findings in Medical Imaging Exams in a Nationwide Israeli Health Organization: Observational Study (Preprint)
Impact of the COVID-19 Pandemic on Clinical Findings in Medical Imaging Exams in a Nationwide Israeli Health Organization: Observational Study (Preprint) Open
BACKGROUND The outbreak of the COVID-19 pandemic had a major effect on the consumption of health care services. Changes in the use of routine diagnostic exams, increased incidences of postacute COVID-19 syndrome (PCS), and other pandemic-…
View article: A causal inference approach for estimating effects of non-pharmaceutical interventions during Covid-19 pandemic
A causal inference approach for estimating effects of non-pharmaceutical interventions during Covid-19 pandemic Open
In response to the outbreak of the coronavirus disease 2019 (Covid-19), governments worldwide have introduced multiple restriction policies, known as non-pharmaceutical interventions (NPIs). However, the relative impact of control measures…
View article: Predictive and Causal Analysis of No-Shows for Medical Exams During COVID-19: A Case Study of Breast Imaging in a Nationwide Israeli Health Organization
Predictive and Causal Analysis of No-Shows for Medical Exams During COVID-19: A Case Study of Breast Imaging in a Nationwide Israeli Health Organization Open
"No-shows", defined as missed appointments or late cancellations, is a central problem in healthcare systems. It has appeared to intensify during the COVID-19 pandemic and the nonpharmaceutical interventions, such as closures, taken to slo…
View article: Artificial Intelligence for Reducing Workload in Breast Cancer Screening with Digital Breast Tomosynthesis
Artificial Intelligence for Reducing Workload in Breast Cancer Screening with Digital Breast Tomosynthesis Open
Background Digital breast tomosynthesis (DBT) has higher diagnostic accuracy than digital mammography, but interpretation time is substantially longer. Artificial intelligence (AI) could improve reading efficiency. Purpose To evaluate the …
View article: Emulated Clinical Trials from Longitudinal Real-World Data Efficiently Identify Candidates for Neurological Disease Modification: Examples from Parkinson’s Disease
Emulated Clinical Trials from Longitudinal Real-World Data Efficiently Identify Candidates for Neurological Disease Modification: Examples from Parkinson’s Disease Open
Real-world healthcare data hold the potential to identify therapeutic solutions for progressive diseases by efficiently pinpointing safe and efficacious repurposing drug candidates. This approach circumvents key early clinical development …
View article: Predictive and Causal Analysis of No-Shows for Medical Exams During COVID-19: A Case Study of Breast Imaging in a Nationwide Israeli Health Organization
Predictive and Causal Analysis of No-Shows for Medical Exams During COVID-19: A Case Study of Breast Imaging in a Nationwide Israeli Health Organization Open
“No-shows”, defined as missed appointments or late cancellations, is a central problem in healthcare systems. It has appeared to intensify during the COVID-19 pandemic and the nonpharmaceutical interventions, such as closures, taken to slo…
View article: Pre-biopsy Multi-class Classification of Breast Lesion Pathology in Mammograms
Pre-biopsy Multi-class Classification of Breast Lesion Pathology in Mammograms Open
View article: Framework for identifying drug repurposing candidates from observational healthcare data
Framework for identifying drug repurposing candidates from observational healthcare data Open
Objective Observational medical databases, such as electronic health records and insurance claims, track the healthcare trajectory of millions of individuals. These databases provide real-world longitudinal information on large cohorts of …
View article: Emulated Clinical Trials from Longitudinal Real-World Data Efficiently Identify Candidates for Neurological Disease Modification: Examples from Parkinson’s Disease
Emulated Clinical Trials from Longitudinal Real-World Data Efficiently Identify Candidates for Neurological Disease Modification: Examples from Parkinson’s Disease Open
Real-world healthcare data hold the potential to identify therapeutic solutions for progressive diseases by efficiently pinpointing safe and efficacious repurposing drug candidates. This approach circumvents key early clinical development …
View article: Framework for Identifying Drug Repurposing Candidates from Observational Healthcare Data
Framework for Identifying Drug Repurposing Candidates from Observational Healthcare Data Open
Objective Observational medical databases, such as electronic health records and insurance claims, track the healthcare trajectory of millions of individuals. These databases provide real-world longitudinal information on large cohorts of …
View article: Adversarial Balancing for Causal Inference
Adversarial Balancing for Causal Inference Open
Biases in observational data of treatments pose a major challenge to estimating expected treatment outcomes in different populations. An important technique that accounts for these biases is reweighting samples to minimize the discrepancy …
View article: Characterizing Subpopulations with Better Response to Treatment Using Observational Data – an Epilepsy Case Study
Characterizing Subpopulations with Better Response to Treatment Using Observational Data – an Epilepsy Case Study Open
Electronic health records and health insurance claims, providing observational data on millions of patients, offer great opportunities, and challenges, for population health studies. The objective of this study is identifying subpopulation…
View article: Changing the approach to treatment choice in epilepsy using big data
Changing the approach to treatment choice in epilepsy using big data Open
Chances of treatment success were improved if patients received the model-predicted treatment. Using the model's prediction system may enable personalized, evidence-based epilepsy care, accelerating the match between patients and their ide…