Geneshot

Geneshot ranks genes associated with arbitrary biomedical terms by integrating publication-derived evidence and gene-gene similarity matrices to highlight under-studied genes and predict gene functions.

Key Features:

• Gene Ranking and Discovery: Accepts arbitrary biomedical terms and produces ranked lists of genes based on mentions in the scientific literature and relevance to the search terms.
• Data Integration: Integrates gene-gene similarity matrices derived from processed RNA-seq data and gene-gene co-occurrence datasets sourced from ARCHS4, Enrichr, Tagger, AutoRIF, and GeneRIF.
• Predictive Analysis: Predicts potential gene functions by analyzing similarities between genes using integrated similarity matrices and predictive models.
• Novelty Analysis and Gene Set Augmentation: Assesses the novelty of gene sets and augments them with additional relevant genes using literature evidence and similarity-based predictions.

Scientific Applications:

• Understudied Gene Identification: Identify under-researched genes linked to specific biomedical terms by combining publication-based evidence and similarity-based predictions.
• Hypothesis Generation: Suggest novel gene candidates related to biomedical terms to support hypothesis formulation.
• Disease and Process Association: Identify genes associated with specific conditions or biological processes (e.g., wound healing, hair loss) and explore genetic underpinnings of diseases such as glioblastoma and diabetes.
• Gene Set Expansion: Augment existing gene sets with additional relevant genes for more comprehensive genomic studies.

Methodology:

Aggregates data from scientific publications and gene-gene similarity matrices (including matrices derived from processed RNA-seq and co-occurrence datasets from ARCHS4, Enrichr, Tagger, AutoRIF, and GeneRIF) and ranks genes by combining direct literature evidence with predictive models based on integrated similarity datasets.