Results 231 to 240 of about 1,386,749 (339)

Big-data approaches to protein structure prediction

, 2017
Johannes Söding
openalex   +2 more sources

Accuracy of Diagnostic Codes and Algorithms Used to Identify Rheumatoid Arthritis and Juvenile Idiopathic Arthritis in Administrative Claims and Electronic Health Records: Systematic Review and Meta‐Analysis

Arthritis Care &Research, EarlyView.
Objective This systematic review aimed to assess the diagnostic accuracy of algorithms used to identify rheumatoid arthritis and juvenile idiopathic arthritis in electronic health records. Methods We searched Medline, Embase, and Cochrane Central Register for Controlled Trials databases and included studies that validated case definitions against a ...
Constanza Saka‐Herrán, Jessica Bennett, Yara Alkabti, Muhammad Fatir, Barbara Clyne, Caroline McCarthy, Gráinne Tynan, Nikki Dunne, Michelle Flood, Eoghan McCarthy, Frank Moriarty   +10 more
wiley   +1 more source

Deep learning for protein structure prediction and design-progress and applications. [PDF]

Mol Syst Biol
Jänes J, Beltrao P.
europepmc   +1 more source

MOLECULAR PHYLOGENY AND STRUCTURE PREDICTION OF RICE RFT1 PROTEIN

, 2016
Shahkila Mohd Arif, Abdulrahman Mahmoud Dogara, Nyuk Ling, Mohd Shahir Shamsir, Sepideh Parvizpour, Zaidah Rahmat   +5 more
openalex   +1 more source

HYPROSP: a hybrid protein secondary structure prediction algorithm--a knowledge-based approach [PDF]

, 2004
Kuanlin Wu
openalex   +1 more source

Clinical Practice Guideline for Evaluation and Management of Peripheral Nervous System Manifestations in Sjögren's Disease

Arthritis Care &Research, Accepted Article.
Objectives Sjögren's disease is an autoimmune disorder that can impact multiple organ systems, including the peripheral nervous system (PNS). PNS manifestations, which can exist concurrently, include mononeuropathies, polyneuropathies, and autonomic nervous system neuropathies. To help patients and providers in the decision‐making process, we developed
Anahita Deboo, Robert Fox, Katherine M. Hammitt, Julie Frantsve‐Hawley, Matthew C. Baker, Stamatina Danielides, Eduardo De Sousa, Brent P. Goodman, Jennifer K. King, Steven Mandel, Ghaith Noaiseh, Pantelis P. Pavlakis, George Sarka, R. Hal Scofield, Arun Varadhachary, Daniel J. Wallace, Matt Makara, Nancy Carteron, Steven Carsons, In collaboration with the Consensus Expert Panel (CEP) members   +19 more
wiley   +1 more source

Single-sequence protein structure prediction by integrating protein language models. [PDF]

Proc Natl Acad Sci U S A
Jing X, Wu F, Luo X, Xu J.
europepmc   +1 more source

Advances in Protein Structure Prediction Highlight Unexpected Commonalities Between Gram-positive and Gram-negative Conjugative T4SSs

Annika Breidenstein, Dennis Svedberg, Josy ter Beek, Ronnie P.‐A. Berntsson   +3 more
openalex   +1 more source

What Do Large Language Models Know About Materials?

Advanced Engineering Materials, EarlyView.
If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer, Thomas Wallmersperger, Gianaurelio Cuniberti   +2 more
wiley   +1 more source

Metaheuristics for protein structure prediction: A comprehensive review and empirical analysis

Sunday O. Oladejo
openalex   +1 more source