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Yazeed Zoabi
Researcher at Tel Aviv University
Publications - 8
Citations - 312
Yazeed Zoabi is an academic researcher from Tel Aviv University. The author has contributed to research in topics: Medicine & Transcriptome. The author has an hindex of 3, co-authored 6 publications receiving 83 citations.
Papers
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Journal ArticleDOI
Machine learning-based prediction of COVID-19 diagnosis based on symptoms
TL;DR: In this paper, a machine learning approach was used to detect COVID-19 cases by simple features accessed by asking basic questions, such as sex, age ≥ 60 years, known contact with an infected individual, and the appearance of five initial clinical symptoms.
Journal ArticleDOI
Differential network analysis of multiple human tissue interactomes highlights tissue-selective processes and genetic disorder genes
Omer Basha,Chanan M Argov,Raviv Artzy,Yazeed Zoabi,Idan Hekselman,Liad Alfandari,Vered Chalifa-Caspi,Esti Yeger-Lotem +7 more
TL;DR: It is demonstrated that differential analysis of multiple human tissue interactomes is a powerful tool for highlighting processes and genes with tissue-selective functionality and clinical impact.
Posted ContentDOI
COVID-19 diagnosis prediction by symptoms of tested individuals: a machine learning approach
Yazeed Zoabi,Noam Shomron +1 more
TL;DR: Overall, using nationwide data representing the general population, a model is developed that enables screening suspected COVID-19 patients according to simple features accessed by asking them basic questions and can be used, among other considerations, to prioritize testing for CO VID-19 when allocating limited testing resources.
Book ChapterDOI
Processing and Analysis of RNA-seq Data from Public Resources.
Yazeed Zoabi,Noam Shomron +1 more
TL;DR: In this paper, the authors provide an overview on how to begin the analysis pipeline, and how to explore and interpret the data provided by these publicly available resources, including the Genotype Tissue Expression project (GTEx) and The Cancer Genome Atlas (TCGA).
Journal ArticleDOI
Predicting bloodstream infection outcome using machine learning.
TL;DR: In this paper, the authors developed a machine learning model to predict patient outcomes of BSI using electronic medical record-based machine learning models, including demographics, blood tests, and the medical and diagnosis history of 7889 hospitalized patients diagnosed with BSI.