Mendelian diseases tend to manifest clinically in certain tissues, yet their affected cell types typically remain elusive. Single-cell expression studies showed that overexpression of disease-associated genes may point to the affected cell types. Here, we developed a method that infers disease-affected cell types from the preferential expression of disease-associated genes in cell types (PrEDiCT).
View Article and Find Full Text PDFPurpose: To evaluate the incidence and risk factors for inflammatory conditions among patients with primary acquired nasolacrimal duct obstruction (PANDO).
Methods: A retrospective case-control study was conducted among patients of Clalit Health Services (CHS) in Israel from 2001 to 2022. For each case, three controls were matched among all CHS patients according to year of birth, sex, and ethnicity.
How do aberrations in widely expressed genes lead to tissue-selective hereditary diseases? Previous attempts to answer this question were limited to testing a few candidate mechanisms. To answer this question at a larger scale, we developed "Tissue Risk Assessment of Causality by Expression" (TRACE), a machine learning approach to predict genes that underlie tissue-selective diseases and selectivity-related features. TRACE utilized 4,744 biologically interpretable tissue-specific gene features that were inferred from heterogeneous omics datasets.
View Article and Find Full Text PDFHereditary diseases tend to manifest clinically in few selected tissues. Knowledge of those tissues is important for better understanding of disease mechanisms, which often remain elusive. However, information on the tissues inflicted by each disease is not easily obtainable.
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