A5009354743- Protein Structure and Dynamics
- Enzyme Structure and Function
- Suicide and Self-Harm Studies
- Machine Learning in Bioinformatics
- Computational Drug Discovery Methods
- Genetics, Bioinformatics, and Biomedical Research
- Scientific Computing and Data Management
- Mass Spectrometry Techniques and Applications
- Parallel Computing and Optimization Techniques
- Health disparities and outcomes
- Viral Infections and Vectors
- interferon and immune responses
- Mental Health via Writing
- Cell Image Analysis Techniques
- Viral Infections and Outbreaks Research
- Autopsy Techniques and Outcomes
- Genomics and Chromatin Dynamics
- Matrix Theory and Algorithms
- Algorithms and Data Compression
- Genomics and Phylogenetic Studies
- Microbial Metabolic Engineering and Bioproduction
- Interconnection Networks and Systems
- Topic Modeling
- Distributed and Parallel Computing Systems
- Cancer Immunotherapy and Biomarkers
Lawrence Berkeley National Laboratory
2006-2025
Stanford University
2022
Veterans Health Administration
2022
United States Department of Veterans Affairs
2022
University of Pennsylvania
2022
Durham VA Health Care System
2022
University of California, Davis
2009-2020
California Institute of Technology
2006
University of Colorado Boulder
1995-2005
National Energy Research Scientific Computing Center
1999-2002
We present the results for CAPRI Round 46, third joint CASP-CAPRI protein assembly prediction challenge. The comprised a total of 20 targets including 14 homo-oligomers and 6 heterocomplexes. Eight homo-oligomer one heterodimer proteins that could be readily modeled using templates from Protein Data Bank, often available full assembly. remaining 11 5 homodimers, 3 heterodimers, two higher-order assemblies. These were more difficult to model, as their mainly involved "ab-initio" docking...
Abstract We present an ensemble transfer learning method to predict suicide from Veterans Affairs (VA) electronic medical records (EMR). A diverse set of base models was trained a binary outcome constructed reported suicide, attempt, and overdose diagnoses with varying choices study design prediction methodology. Each model used twenty cross-sectional 190 longitudinal variables observed in eight time intervals covering 7.5 years prior the prediction. Ensembles seven were created fine-tuned...
The protein structure prediction problem continues to elude scientists. Despite the introduction of many methods, only modest gains were made over last decade for certain classes targets. To address this challenge, a social-media based worldwide collaborative effort, named WeFold, was undertaken by 13 labs. During collaboration, laboratories simultaneously competing with each other. Here, we present first attempt at "coopetition" in scientific research applied and refinement problems....
Abstract The exponential growth of protein structure databases has motivated the development efficient deep learning methods that perform structural analysis tasks at large scale, ranging from classification experimentally determined proteins to quality assessment and ranking computationally generated models in context prediction. Yet, literature discussing these does not usually interpret what learned training or identify specific data attributes contribute regression task. While 3D 2D CNNs...
Post-translational modifications of the extracellular matrix receptor dystroglycan (DG) determine its functional state, and defects in these are linked to muscular dystrophies cancers. A prominent feature DG biosynthesis is a precursor cleavage that segregates ligand-binding transmembrane domains into noncovalently attached alpha- beta-subunits. We investigate here structural determinants significance this cleavage. show elicits conspicuous change activity. Mutations obstruct result...
The function of a protein is determined by its structure, which creates need for efficient methods structure determination to advance scientific and medical research. Because current experimental carry high price tag, computational predictions are highly desirable. Given sequence, produce numerous 3D structures known as decoys. Selection the best quality decoys both challenging essential end users can handle only few ones. Therefore, scoring functions central decoy selection. They combine...
With the advance of experimental procedures obtaining chemical crosslinking information is becoming a fast and routine practice. Information on crosslinks can greatly enhance accuracy protein structure modeling. Here, we review current state art in modeling structures with assistance experimentally determined within framework 13th meeting Critical Assessment Structure Prediction approaches. This largest-to-date blind assessment reveals benefits using data difficult to model prediction cases....
The onset and persistence of life events (LE) such as housing instability, job reduced social connection have been shown to increase risk suicide. Predictive models for suicide low sensitivity many these factors due under-reporting in structured electronic health records (EHR) data. In this study, we show how natural language processing (NLP) can help identify LE clinical notes at higher rates than reported medical codes. We compare domain-specific lexicons formulated from Unified Medical...
ABSTRACT Background/Aims Predictive models of suicide risk have focused on predictors extracted from structured data found in electronic health records (EHR), with limited consideration predisposing life events (LE) expressed unstructured clinical text such as housing instability and marital troubles. Additionally, there has been work large-scale analysis natural language processing (NLP) derived for integration LE into longitudinal risk. This study aims to expand upon previous research,...
Abstract Introduction Forty percent of Veterans express concern about military toxic exposures (TEs) which may include airborne hazards. with overseas wartime service and TEs show increased rates cardiometabolic disorders, including heart disease, stroke, type 2 diabetes mellitus (T2DM). Additionally, 24% have been diagnosed Obstructive Sleep Apnea (OSA), is epidemiologically linked to disease. However, the extent OSA modifies risk developing comorbidities after remains unknown Methods Using...
Abstract Introduction Obstructive sleep apnea (OSA) is a prevalent condition among Veterans affecting 24% and ranking the top five most common diagnoses in Health Administration (VHA). Epidemiologic studies demonstrate strong association between OSA cardiometabolic disease. This study describes temporal distribution of comorbidities relationship to first test within VHA. Methods sub-analysis data from larger project leveraging Artificial Intelligence (AI) predict which with are likely...
The transition toward exascale computing will be accompanied by a performance dichotomy. Computational peak rapidly increase; I/O either grow slowly or completely stagnant. Essentially, the rate at which data are generated much faster than can read from and written to disk. MD simulations soon face problem of efficiently writing reading disk on next generation supercomputers. This article targets proposes novel technique for in situ analysis indexing trajectories. Our maps individual...
Capsule Networks have great potential to tackle problems in structural biology because of their attention hierarchical relationships. This paper describes the implementation and application a Network architecture classification RAS protein family structures on GPU-based computational resources. The proposed trained 2D 3D encodings can successfully classify HRAS KRAS structures. also protein-based dataset derived from PSI-BLAST search sequences mutations. Our results show an accuracy...
We describe an interactive visualization and modeling program for the creation of protein structures "from scratch." The input to our is amino acid sequence - decoded from a gene predicted secondary structure types each provided by external prediction programs. Our can be used in set-up phase process; created with it serve as subsequent global internal energy minimization, or another method prediction. supports basic methods structures, manipulation based on inverse kinematics, guides aid...
Suicide is a major public health problem affecting US Veterans and the in general. Many variables (e.g., demographic, clinical, biological, geographic) have been associated with risk for suicide suicidal behavior, including altitude; however, exact nature of relationship between altitude remains unclear part due to fact that previous studies used either geospatial data or individual-level data, but not both. Prior research has also failed consider full range thoughts behaviors, ranging from...
Abstract We describe a method that can thoroughly sample protein conformational space given the primary sequence of amino acids and secondary structure predictions. Specifically, we target proteins with β‐sheets because they are particularly challenging for ab initio prediction complexity sampling long‐range strand pairings. Using some basic packing principles, inverse kinematics (IK), β‐pairing scores, this creates all possible β‐sheet arrangements including those have correct β‐strands. It...
ABSTRACT In this study, we propose a scientific framework to detect capability among biomedical large language models (LLMs) for organizing expressions of comorbid disease and temporal progression. We hypothesize that LLMs pretrained on next-token prediction produce latent spaces implicitly capture "disease states" progression, i.e., the transitions over states time. describe how foundation may transfer knowledge from explicit pretraining tasks specific clinical applications. A scoring...
Abstract Importance Social and environmental determinants of health (SDOH EDOH) may contribute significantly to suicide rates among U.S. veterans. Objective To identify key predictive variables for assessing suicide-related death (SRR), which include deaths, firearm non-firearm deaths vulnerability areas. Design, Setting, Participants This case-control study utilized Electronic Health Record (EHR) data, included demographic mental information spanning from January 1, 2006, December 31, 2016....
The cyclic GMP-AMP synthase (cGAS) - stimulator of interferon genes (STING) pathway is crucial in the innate immune response, particularly cancer immunotherapy. Despite promising preclinical results, 5,6-dimethylxanthenone-4-acetic acid (DMXAA) showed limited efficacy human clinical trials due to species-specific differences STING activation. This study investigates these by analyzing binding dynamics and affinities various STING-ligand complexes using molecular (MD) simulations free energy...