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Restructured version of Gene Expression Omnibus (GEO) that provides a user friendly interface for curating GEO database

Open source data visualization and analysis for novice and experts. Data mining through visual programming or Python scripting. Components for machine learning. Add-ons for bioinformatics and text mining. Packed with features for data analytics.

Ontology-based systematic representation and analysis of drug adverse events and its usage in study of adverse events given different patient age and disease conditions.

TISSUES is a weekly updated web resource that integrates evidence on tissue expression from manually curated literature, proteomics and transcriptomics screens, and automatic text mining. We map all evidence to common protein identifiers and Brenda Tissue Ontology terms, and further unify it by assigning confidence scores that facilitate comparison of the different types and sources of evidence. We finally visualize these scores on a schematic human body to provide a convenient overview.

This repository contains the software necessary for extracting the features and selecting the Sun intersecting cloudy pixels using the functions contained in files posted in other repositories. See preprint article: https://arxiv.org/pdf/2110.05622.pdf See preprint article: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4064631 See realated data: https://doi.org/10.5061/dryad.zcrjdfn9m Additional funding was provided by the King Felipe VI Endowed Chair of the University of New Mexico.

Automated label extraction from GEO metadata.

Web application for efficient and accurate Human Phenotype Ontology (HPO) concept curation.

Package that facilitates access to extensive metadata in computable form covering the Bioconductor package ecosystem, facilitating downstream applications such as custom reporting, data and text mining of Bioconductor package text descriptions, graph analytics over package dependencies, and custom search approaches.

Swami is a tool to automatically extract test oracles and generate executable tests from structured natural language specifications. A research paper describing Swami, its underlying technique, and its evaluation, titled "Automatically Generating Precise Oracles from Structured Natural Language Specifications" has been published in The International Conference on Software Engineering (ICSE) 2019. Paper: https://people.cs.umass.edu/~brun/pubs/pubs/Motwani19icse.pdf Conference: https://conf.researchr.org/home/icse-2019 Also supported by the National Science Foundation under grant no: 1763423: SHF: Medium: Fairness in Software Systems

This repository contains the files implemented for the different experiments carried out to develop an optimal Deep Learning (DL) intra-hour solar forecasting using infrared ground-based images. The model is based on deep recurrent networks architectures. The state-of-the-art is implemented in solarnet.py and sunset.py for comparission purposes. See journal preprint: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4064631 See realated data: https://doi.org/10.5061/dryad.zcrjdfn9m