Results 131 to 140 of about 1,047,077 (291)

Fast randomized approximate string matching with succinct hash data structures. [PDF]

BMC Bioinformatics, 2015
Policriti A, Prezza N.
europepmc   +1 more source

Efficient string matching

Communications of the ACM, 1975
A. Aho, M. J. Corasick
semanticscholar   +1 more source

Livestock Tango: U.S. and Latin America Dance Together, but Who Will Lead?

Applied Economic Perspectives and Policy, EarlyView.
ABSTRACT This study examines the competitiveness between Latin American and U.S. livestock and meat sectors. We employ a computable general equilibrium modeling framework to evaluate two scenarios: coordinated improvements in Latin American productivity, transport efficiency, and market access (Scenario I), and the minimum productivity gains required ...
Taís C. Menezes, Amanda M. Countryman, Dustin L. Pendell   +2 more
wiley   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

SeArcH schemes for Approximate stRing mAtching. [PDF]

NAR Genom Bioinform
Gottlieb SG, Reinert K.
europepmc   +1 more source

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source

Broadening the capture of natural products mentioned in FAERS using fuzzy string-matching and a Siamese neural network. [PDF]

Sci Rep
Dilán-Pantojas IO, Boonchalermvichien T, Taneja SB, Li X, Chapin MR, Karcher S, Boyce RD.   +6 more
europepmc   +1 more source

Accelerating Primary Screening of USP8 Inhibitors from Drug Repurposing Databases with Tree‐Based Machine Learning

Advanced Intelligent Discovery, EarlyView.
This study introduces a tree‐based machine learning approach to accelerate USP8 inhibitor discovery. The best‐performing model identified 100 high‐confidence repurposable compounds, half already approved or in clinical trials, and uncovered novel scaffolds not previously studied. These findings offer a solid foundation for rapid experimental follow‐up,
Yik Kwong Ng, Sharon Ai Er Lam, Wei Chun Lim, Hannah Hui Min Toh, Shi Jun Ang   +4 more
wiley   +1 more source

Next generation sequencing (NGS) database for tandem repeats with multiple pattern 2°-shaft multicore string matching. [PDF]

Genom Data, 2016
Someswara Rao C, Raju SV.
europepmc   +1 more source

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source