Yishai Shimoni
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View article: BMFM-RNA: An Open Framework for Building and Evaluating Transcriptomic Foundation Models
BMFM-RNA: An Open Framework for Building and Evaluating Transcriptomic Foundation Models Open
Transcriptomic foundation models (TFMs) have recently emerged as powerful tools for analyzing gene expression in cells and tissues, supporting key tasks such as cell-type annotation, batch correction, and perturbation prediction. However, …
View article: Peri-operative anti-inflammatory drug use and seizure recurrence after resective epilepsy surgery: Target trials emulation
Peri-operative anti-inflammatory drug use and seizure recurrence after resective epilepsy surgery: Target trials emulation Open
View article: Single‐microglia transcriptomic transition network‐based prediction and real‐world patient data validation identifies ketorolac as a repurposable drug for Alzheimer's disease
Single‐microglia transcriptomic transition network‐based prediction and real‐world patient data validation identifies ketorolac as a repurposable drug for Alzheimer's disease Open
INTRODUCTION High microglial heterogeneities hinder the development of microglia‐targeted treatment for Alzheimer's disease (AD). METHODS We integrated 0.7 million single‐nuclei RNA‐sequencing transcriptomes from human brains using a varia…
View article: Does your model understand genes? A benchmark of gene properties for biological and text models
Does your model understand genes? A benchmark of gene properties for biological and text models Open
The application of deep learning methods, particularly foundation models, in biological research has surged in recent years. These models can be text-based or trained on underlying biological data, especially omics data of various types. H…
View article: Single‐Microglia Transcriptomic Transition Network‐based Prediction and Real‐world Patient Data Validation Identifies Ketorolac as a Repurposable Drug for Alzheimer’s Disease
Single‐Microglia Transcriptomic Transition Network‐based Prediction and Real‐world Patient Data Validation Identifies Ketorolac as a Repurposable Drug for Alzheimer’s Disease Open
Background Microglia have been implicated as a key aspect of the pathology of Alzheimer’s disease (AD). However, high microglial heterogeneities, including disease‐associated microglia (DAM), tau microglia (tau‐pathology related), and neur…
View article: Improving Inverse Probability Weighting by Post-calibrating Its Propensity Scores
Improving Inverse Probability Weighting by Post-calibrating Its Propensity Scores Open
Theoretical guarantees for causal inference using propensity scores are partially based on the scores behaving like conditional probabilities. However, prediction scores between zero and one do not necessarily behave like probabilities, es…
View article: Hierarchical Bias-Driven Stratification for Interpretable Causal Effect Estimation
Hierarchical Bias-Driven Stratification for Interpretable Causal Effect Estimation Open
Interpretability and transparency are essential for incorporating causal effect models from observational data into policy decision-making. They can provide trust for the model in the absence of ground truth labels to evaluate the accuracy…
View article: EuCARE-POSTCOVID Study: a multicentre cohort study on long-term post-COVID-19 manifestations
EuCARE-POSTCOVID Study: a multicentre cohort study on long-term post-COVID-19 manifestations Open
View article: EuCARE-POSTCOVID Study: A Multicentre Cohort Study on Long-Term Post-COVID-19 Manifestations
EuCARE-POSTCOVID Study: A Multicentre Cohort Study on Long-Term Post-COVID-19 Manifestations Open
Background. Post-COVID-19 condition refers to persistent or new onset symptoms occurring three months after acute COVID-19, which are unrelated to alternative diagnoses. Symptoms include fatigue, breathlessness, palpitations, pain, concent…
View article: Single-Microglia Transcriptomic Transition Network-Based Prediction and Real-World Patient Data Validation Identifies Ketorolac as a Repurposable Drug for Alzheimer's Disease
Single-Microglia Transcriptomic Transition Network-Based Prediction and Real-World Patient Data Validation Identifies Ketorolac as a Repurposable Drug for Alzheimer's Disease Open
View article: Propensity score models are better when post-calibrated
Propensity score models are better when post-calibrated Open
Theoretical guarantees for causal inference using propensity scores are partly based on the scores behaving like conditional probabilities. However, scores between zero and one, especially when outputted by flexible statistical estimators,…
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: 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: Trends in clinical characteristics and associations of severe non-respiratory events related to SARS-CoV-2
Trends in clinical characteristics and associations of severe non-respiratory events related to SARS-CoV-2 Open
Background The 2019 novel coronavirus (SARS-CoV-2) is reported to result in both respiratory and non-respiratory severe health outcomes, but quantitative assessment of the risk – while adjusting for underlying risk driven by comorbidities …
View article: A discriminative approach for finding and characterizing positivity violations using decision trees
A discriminative approach for finding and characterizing positivity violations using decision trees Open
The assumption of positivity in causal inference (also known as common support and co-variate overlap) is necessary to obtain valid causal estimates. Therefore, confirming it holds in a given dataset is an important first step of any causa…
View article: An Evaluation Toolkit to Guide Model Selection and Cohort Definition in Causal Inference
An Evaluation Toolkit to Guide Model Selection and Cohort Definition in Causal Inference Open
Real world observational data, together with causal inference, allow the estimation of causal effects when randomized controlled trials are not available. To be accepted into practice, such predictive models must be validated for the datas…
View article: RW3 FRAMEWORK FOR RELIABLE VALUE ASSESSMENT OF TREATMENTS USING CAUSAL ANALYSIS OF OBSERVATIONAL DATA: SUPPORT MATCHING BIOLOGICAL THERAPY TO RHEUMATOID ARTHRITIS PATIENTS
RW3 FRAMEWORK FOR RELIABLE VALUE ASSESSMENT OF TREATMENTS USING CAUSAL ANALYSIS OF OBSERVATIONAL DATA: SUPPORT MATCHING BIOLOGICAL THERAPY TO RHEUMATOID ARTHRITIS PATIENTS Open
View article: Association between expression of random gene sets and survival is evident in multiple cancer types and may be explained by sub-classification
Association between expression of random gene sets and survival is evident in multiple cancer types and may be explained by sub-classification Open
One of the goals of cancer research is to identify a set of genes that cause or control disease progression. However, although multiple such gene sets were published, these are usually in very poor agreement with each other, and very few o…
View article: Benchmarking Framework for Performance-Evaluation of Causal Inference Analysis
Benchmarking Framework for Performance-Evaluation of Causal Inference Analysis Open
Causal inference analysis is the estimation of the effects of actions on outcomes. In the context of healthcare data this means estimating the outcome of counter-factual treatments (i.e. including treatments that were not observed) on a pa…
View article: Elucidating Compound Mechanism of Action by Network Perturbation Analysis
Elucidating Compound Mechanism of Action by Network Perturbation Analysis Open
View article: Stochastic analysis of bistability in coherent mixed feedback loops combining transcriptional and posttranscriptional regulations
Stochastic analysis of bistability in coherent mixed feedback loops combining transcriptional and posttranscriptional regulations Open
Mixed feedback loops combining transcriptional and posttranscriptional regulations are common in cellular regulatory networks. They consist of two genes, encoding a transcription factor and a small noncoding RNA (sRNA), which mutually regu…