Doron Stupp

Google (United States)

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Yuval Tabach

Hebrew University of Jerusalem

Idit Bloch

Hebrew University of Jerusalem

Elad Sharon

Hebrew University of Jerusalem

Yossi Matias

Google (United States)

MŽ

Marinka Žitnik

Harvard University

Avinatan Hassidim

Bar-Ilan University

Or Zuk

Hebrew University of Jerusalem

Eyal Oren

San Diego State University

Dana Sherill-Rofe

Hebrew University of Jerusalem

Lior Nissim

Hebrew University of Jerusalem

Cooperative Institutions

Hebrew University of Jerusalem

Google (United States)

Harvard University

Massachusetts Institute of Technology

European Bioinformatics Institute

Stanford University

Broad Institute

National Institutes of Health

Tel Aviv University

Hadassah Medical Center

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A high-throughput screening and computation platform for identifying synthetic promoters with enhanced cell-state specificity (SPECS)

Nature Communications (2019)

Ming-Ru Wu Lior Nissim Doron Stupp Erez Pery Adina Binder-Nissim

Abstract Cell state-specific promoters constitute essential tools for basic research and biotechnology because they activate gene expression only under certain biological conditions. Synthetic Promoters with Enhanced Cell-State Specificity (SPECS) can be superior to native ones, but the design of such promoters is challenging and frequently requires gene regulation or transcriptome knowledge that is not readily available. Here, to overcome this challenge, we use a next-generation sequencing approach combined with machine learning to screen a synthetic promoter library with 6107 designs for high-performance SPECS for potentially any cell state. We demonstrate the identification of multiple SPECS that exhibit distinct spatiotemporal activity during the programmed differentiation of induced pluripotent stem cells (iPSCs), as well as SPECS for breast cancer and glioblastoma stem-like cells. We anticipate that this approach could be used to create SPECS for gene therapies that are activated in specific cell states, as well as to study natural transcriptional regulatory networks.

Synthetic Biology

Identification

10.1038/s41467-019-10912-8

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Citations (63)

Mondo: Unifying diseases for the world, by the world

medRxiv (Cold Spring Harbor Laboratory) (2022)

Nicole Vasilevsky Nicolas Matentzoglu Sabrina Toro Joseph Eugene Flack Harshad Hegde

Abstract There are thousands of distinct disease entities and concepts, each of which are known by different and sometimes contradictory names. The lack of a unified system for managing these entities poses a major challenge for both machines and humans that need to harmonize information to better predict causes and treatments for disease. The Mondo Disease Ontology is an open, community-driven ontology that integrates key medical and biomedical terminologies, supporting disease data integration to improve diagnosis, treatment, and translational research. Mondo records the sources of all data and is continually updated, making it suitable for research and clinical applications that require up-to-date disease knowledge.

10.1101/2022.04.13.22273750

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Citations (61)

Machine-learning of complex evolutionary signals improves classification of SNVs

NAR Genomics and Bioinformatics (2022)

Sapir Labes Doron Stupp Naama Wagner Idit Bloch Michal Lotem

Conservation is a strong predictor for the pathogenicity of single-nucleotide variants (SNVs). However, some positions that present complex conservation patterns across vertebrates stray from this paradigm. Here, we analyzed the association between complex conservation patterns and the pathogenicity of SNVs in the 115 disease-genes that had sufficient variant data. We show that conservation is not a one-rule-fits-all solution since its accuracy highly depends on the analyzed set of species and genes. For example, pairwise comparisons between the human and 99 vertebrate species showed that species differ in their ability to predict the clinical outcomes of variants among different genes using conservation. Furthermore, certain genes were less amenable for conservation-based variant prediction, while others demonstrated species that optimize prediction. These insights led to developing EvoDiagnostics, which uses the conservation against each species as a feature within a random-forest machine-learning classification algorithm. EvoDiagnostics outperformed traditional conservation algorithms, deep-learning based methods and most ensemble tools in every prediction-task, highlighting the strength of optimizing conservation analysis per-species and per-gene. Overall, we suggest a new and a more biologically relevant approach for analyzing conservation, which improves prediction of variant pathogenicity.

Correlative

10.1093/nargab/lqac025

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Citations (4)

Synthetic RNA-Based Immunomodulatory Gene Circuits for Cancer Immunotherapy

Cell (2017)

Lior Nissim Ming-Ru Wu Erez Pery Adina Binder-Nissim Hiroshi Suzuki

Cancer Immunotherapy

10.1016/j.cell.2017.09.049

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Citations (136)

CladeOScope: elucidating functional interactions via a clade co-evolution prism

Zenodo (CERN European Organization for Nuclear Research) (2021)

Doron Stupp Tomer Tsaban Idit Bloch Elad Sharon Yuval Tabach

Accompanying files to the publication "CladeOScope: elucidating functional interactions via a clade co-evolution prism" The files attached here constitute the phylogenetic profiling matrices required to reproduce the method and its comparisons as detailed in the publication. For more details and the code required to reproduce the publication please read the publication and visit the following github repository: https://github.com/dst1/CladeOScope To cite our paper use the following citation (bibtex):

@article{Tsaban_2021, doi = {10.1093/nargab/lqab024}, url = {https://doi.org/10.1093%2Fnargab%2Flqab024}, year = 2021, month = {apr}, publisher = {Oxford University Press ({OUP})}, volume = {3}, number = {2}, author = {Tomer Tsaban and Doron Stupp and Dana Sherill-Rofe and Idit Bloch and Elad Sharon and Ora Schueler-Furman and Reuven Wiener and Yuval Tabach}, title = {{CladeOScope}: functional interactions through the prism of clade-wise co-evolution}, journal = {{NAR} Genomics and Bioinformatics} }

Prism

10.5281/zenodo.4464120

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Citations (0)

Structure Annotations of Assessment and Plan Sections from MIMIC-III

Zenodo (CERN European Organization for Nuclear Research) (2022)

Doron Stupp Ronnie Barequet I‐Ching Lee Eyal Oren Amir Feder

Physicians record their detailed thought-processes about diagnoses and treatments as unstructured text in a section of a clinical note called the "assessment and plan". This information is more clinically rich than structured billing codes assigned for an encounter but harder to reliably extract given the complexity of clinical language and documentation habits. To structure these sections we collected a dataset of annotations over assessment and plan sections from the publicly available and de-identified MIMIC-III dataset, and developed deep-learning based models to perform this task, described in the associated paper available as a pre-print at: https://www.medrxiv.org/content/10.1101/2022.04.13.22273438v1 When using this data please cite our paper:

@article {Stupp2022.04.13.22273438, author = {Stupp, Doron and Barequet, Ronnie and Lee, I-Ching and Oren, Eyal and Feder, Amir and Benjamini, Ayelet and Hassidim, Avinatan and Matias, Yossi and Ofek, Eran and Rajkomar, Alvin}, title = {Structured Understanding of Assessment and Plans in Clinical Documentation}, year = {2022}, doi = {10.1101/2022.04.13.22273438}, publisher = {Cold Spring Harbor Laboratory Press}, URL = {https://www.medrxiv.org/content/early/2022/04/17/2022.04.13.22273438}, journal = {medRxiv} }

The dataset, presented here, contains annotations of assessment and plan sections of notes from the publicly available and de-identified MIMIC-III dataset, marking the active problems, their assessment description, and plan action items. Action items are additionally marked as one of 8 categories (listed below). The dataset contains over 30,000 annotations of 579 notes from distinct patients, annotated by 6 medical residents and students. The dataset is divided into 4 partitions - a training set (481 notes), validation set (50 notes), test set (48 notes) and an inter-rater set. The inter-rater set contains the annotations of each of the raters over the test set. Rater 1 in the inter-rater set should be regarded as an intra-rater comparison (details in the paper). The labels underwent automatic normalization to capture entire word boundaries and remove flanking non-alphanumeric characters. Code for transforming labels into TensorFlow examples and training models as described in the paper will be made available at GitHub: https://github.com/google-research/google-research/tree/master/assessment_plan_modeling In order to use these annotations, the user additionally needs to obtain the text of the notes which is found in the NOTE_EVENTS table from MIMIC-III, access to which is to be acquired independently (https://mimic.mit.edu/) Annotations are given as character spans in a CSV file with the following schema: Field Type Semantics partition categorical (one of [train, val, test, interrater] The set of ratings the span belongs to. rater_id int Unique id for each the raters note_id int The note’s unique note_id, links to the MIMIC-III notes table (as ROW-ID). span_type categorical (one of [PROBLEM_TITLE,
PROBLEM_DESCRIPTION, ACTION_ITEM] Type of the span as annotated by raters. char_start int Character offsets from note start char_end int action_item_type categorical (one of [MEDICATIONS, IMAGING, OBSERVATIONS_LABS, CONSULTS, NUTRITION, THERAPEUTIC_PROCEDURES, OTHER_DIAGNOSTIC_PROCEDURES, OTHER]) Type of action item if the span is an action item (empty otherwise) as annotated by raters.

10.5281/zenodo.6413404

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Structured Understanding of Assessment and Plans in Clinical Documentation

medRxiv (Cold Spring Harbor Laboratory) (2022)

Doron Stupp Ronnie Barequet I‐Ching Lee Eyal Oren Amir Feder

Abstract Physicians record their detailed thought-processes about diagnoses and treatments as unstructured text in a section of a clinical note called the assessment and plan. This information is more clinically rich than structured billing codes assigned for an encounter but harder to reliably extract given the complexity of clinical language and documentation habits. We describe and release a dataset containing annotations of 579 admission and progress notes from the publicly available and de-identified MIMIC-III ICU dataset with over 30,000 labels identifying active problems, their assessment, and the category of associated action items (e.g. medication, lab test). We also propose deep-learning based models that approach human performance, with a F1 score of 0.88. We found that by employing weak supervision and domain specific data-augmentation, we could improve generalization across departments and reduce the number of human labeled notes without sacrificing performance.

10.1101/2022.04.13.22273438

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Citations (2)

Expanded CUG Repeats Trigger Disease Phenotype and Expression Changes through the RNAi Machinery in C. elegans

Journal of Molecular Biology (2019)

Lena Qawasmi Maya Braun Irene Guberman Emiliano Cohen Lamis Naddaf

RNA Silencing

RNA-induced silencing complex

10.1016/j.jmb.2019.03.003

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Citations (13)

Using generative AI to investigate medical imagery models and datasets

EBioMedicine (2024)

Oran Lang Doron Stupp Ilana Traynis Heather Cole-Lewis Chloe R. Bennett

BackgroundAI models have shown promise in performing many medical imaging tasks. However, our ability to explain what signals these models have learned is severely lacking. Explanations are needed in order to increase the trust of doctors in AI-based models, especially in domains where AI prediction capabilities surpass those of humans. Moreover, such explanations could enable novel scientific discovery by uncovering signals in the data that aren't yet known to experts.MethodsIn this paper, we present a workflow for generating hypotheses to understand which visual signals in images are correlated with a classification model's predictions for a given task. This approach leverages an automatic visual explanation algorithm followed by interdisciplinary expert review. We propose the following 4 steps: (i) Train a classifier to perform a given task to assess whether the imagery indeed contains signals relevant to the task; (ii) Train a StyleGAN-based image generator with an architecture that enables guidance by the classifier ("StylEx"); (iii) Automatically detect, extract, and visualize the top visual attributes that the classifier is sensitive towards. For visualization, we independently modify each of these attributes to generate counterfactual visualizations for a set of images (i.e., what the image would look like with the attribute increased or decreased); (iv) Formulate hypotheses for the underlying mechanisms, to stimulate future research. Specifically, present the discovered attributes and corresponding counterfactual visualizations to an interdisciplinary panel of experts so that hypotheses can account for social and structural determinants of health (e.g., whether the attributes correspond to known patho-physiological or socio-cultural phenomena, or could be novel discoveries).FindingsTo demonstrate the broad applicability of our approach, we present results on eight prediction tasks across three medical imaging modalities—retinal fundus photographs, external eye photographs, and chest radiographs. We showcase examples where many of the automatically-learned attributes clearly capture clinically known features (e.g., types of cataract, enlarged heart), and demonstrate automatically-learned confounders that arise from factors beyond physiological mechanisms (e.g., chest X-ray underexposure is correlated with the classifier predicting abnormality, and eye makeup is correlated with the classifier predicting low hemoglobin levels). We further show that our method reveals a number of physiologically plausible, previously-unknown attributes based on the literature (e.g., differences in the fundus associated with self-reported sex, which were previously unknown).InterpretationOur approach enables hypotheses generation via attribute visualizations and has the potential to enable researchers to better understand, improve their assessment, and extract new knowledge from AI-based models, as well as debug and design better datasets. Though not designed to infer causality, importantly, we highlight that attributes generated by our framework can capture phenomena beyond physiology or pathophysiology, reflecting the real world nature of healthcare delivery and socio-cultural factors, and hence interdisciplinary perspectives are critical in these investigations. Finally, we will release code to help researchers train their own StylEx models and analyze their predictive tasks of interest, and use the methodology presented in this paper for responsible interpretation of the revealed attributes.FundingGoogle.

Generative model

10.1016/j.ebiom.2024.105075

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Citations (16)

CladeOScope: elucidating functional interactions via a clade co-evolution prism

Zenodo (CERN European Organization for Nuclear Research) (2021)

Doron Stupp Tomer Tsaban Idit Bloch Elad Sharon Yuval Tabach

@article{Tsaban_2021, doi = {10.1093/nargab/lqab024}, url = {https://doi.org/10.1093%2Fnargab%2Flqab024}, year = 2021, month = {apr}, publisher = {Oxford University Press ({OUP})}, volume = {3}, number = {2}, author = {Tomer Tsaban and Doron Stupp and Dana Sherill-Rofe and Idit Bloch and Elad Sharon and Ora Schueler-Furman and Reuven Wiener and Yuval Tabach}, title = {{CladeOScope}: functional interactions through the prism of clade-wise co-evolution}, journal = {{NAR} Genomics and Bioinformatics} }

Prism

10.5281/zenodo.4464119

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Citations (0)