A5065438402- Computational Drug Discovery Methods
- Protein Structure and Dynamics
- vaccines and immunoinformatics approaches
- Gene expression and cancer classification
- Microbial Natural Products and Biosynthesis
- Bioinformatics and Genomic Networks
- Art History and Market Analysis
- Advanced Graph Neural Networks
- Consumer Retail Behavior Studies
- Cinema and Media Studies
- Biomedical Text Mining and Ontologies
- SARS-CoV-2 and COVID-19 Research
Lawrence Livermore National Laboratory
2021-2023
Lawrence Livermore National Security
2021-2023
University of Illinois Urbana-Champaign
2019-2020
We present Knowledge Engine for Genomics (KnowEnG), a free-to-use computational system analysis of genomics data sets, designed to accelerate biomedical discovery. It includes tools popular bioinformatics tasks such as gene prioritization, sample clustering, set analysis, and expression signature analysis. The specializes in "knowledge-guided" mining machine learning algorithms, which user-provided are analyzed light prior information about genes, aggregated from numerous knowledge bases...
A rapid response is necessary to contain emergent biological outbreaks before they can become pandemics. The novel coronavirus (SARS-CoV-2) that causes COVID-19 was first reported in December of 2019 Wuhan, China and reached most corners the globe less than two months. In just over a year since initial infections, infected almost 100 million people worldwide. Although similar SARS-CoV MERS-CoV, SARS-CoV-2 has resisted treatments are effective against other coronaviruses. Crystal structures...
Structure-based Deep Fusion models were recently shown to outperform several physics- and machine learning-based protein-ligand binding affinity prediction methods. As part of a multi-institutional COVID-19 pandemic response, over 500 million small molecules computationally screened against four protein structures from the novel coronavirus (SARS-CoV-2), which causes COVID-19. Three enhancements made in order evaluate more than 5 billion docked poses on SARS-CoV-2 targets. First, concept was...
Abstract We present KnowEnG, a free-to-use computational system for analysis of genomics data sets, designed to accelerate biomedical discovery. It includes tools popular bioinformatics tasks such as gene prioritization, sample clustering, set and expression signature analysis. The offers ‘knowledge-guided’ data-mining machine learning algorithms, where user-provided are analyzed in light prior information about genes, aggregated from numerous knowledge-bases encoded massive ‘Knowledge...
Protein–ligand interactions are essential to drug discovery and development efforts. Desirable on-target or multitarget the first step in finding an effective therapeutic, while undesirable off-target assessing safety. In this work, we introduce a novel ligand-based featurization mapping of human protein pockets identify closely related targets project drugs into hybrid protein–ligand feature space their likely interactions. Using structure-based template matches from PDB, featured by...
Structure-based Deep Fusion models were recently shown to outperform several physics- and machine learning-based protein-ligand binding affinity prediction methods. As part of a multi-institutional COVID-19 pandemic response, over 500 million small molecules computationally screened against four protein structures from the novel coronavirus (SARS-CoV-2), which causes COVID-19. Three enhancements made in order evaluate more than 5 billion docked poses on SARS-CoV-2 targets. First, concept was...
Abstract Protein-ligand interactions are essential to drug discovery and development efforts. Desirable on-target or multi-target a first step in finding an effective therapeutic; undesirable off-target assessing safety. In this work, we introduce novel ligand-based featurization mapping of human protein pockets identify closely related targets, project drugs into hybrid protein-ligand feature space their likely interactions. Using structure-based template matches from PDB, featurized by the...