A5035719375- Bioinformatics and Genomic Networks
- Gene Regulatory Network Analysis
- Genomics and Rare Diseases
- Cell Image Analysis Techniques
- Single-cell and spatial transcriptomics
- Genetics, Aging, and Longevity in Model Organisms
- RNA modifications and cancer
- RNA and protein synthesis mechanisms
Ben-Gurion University of the Negev
2022-2023
How do aberrations in widely expressed genes lead to tissue-selective hereditary diseases? Previous attempts answer this question were limited testing a few candidate mechanisms. To at larger scale, we developed "Tissue Risk Assessment of Causality by Expression" (TRACE), machine learning approach predict that underlie diseases and selectivity-related features. TRACE utilized 4,744 biologically interpretable tissue-specific gene features inferred from heterogeneous omics datasets....
Abstract Motivation The distinct functionalities of human tissues and cell types underlie complex phenotype–genotype relationships, yet often remain elusive. Harnessing the multitude bulk single-cell transcriptomes while focusing on processes can help reveal these functionalities. Results Tissue-Process Activity (TiPA) method aims to identify that are preferentially active or under-expressed in specific contexts, by comparing expression levels process genes between contexts. We tested TiPA...
Abstract The distinct functions and phenotypes of human tissues cells derive from the activity biological processes that varies in a context-dependent manner. Here, we present Process Activity (ProAct) webserver estimates preferential tissues, cells, other contexts. Users can upload differential gene expression matrix measured across contexts or use built-in 34 tissues. Per context, ProAct associates ontology (GO) with estimated scores, which are inferred input matrix. visualizes these...