Results 161 to 170 of about 950,074 (346)
Advanced Biology, EarlyView.The study reveals that RRP9 is abnormally highly expressed in ESCC tissues and is closely associated with poor prognosis in patients. Furthermore, it is found that RRP9 promotes ESCC progression through enhancing the E2F1‐mediated transcriptional regulation of CDK1.
Gang He +14 more
wiley +1 more source
Systems Biology Toolbox for MATLAB: a computational platform for research in systems biology [PDF]
, 2005 Henning Schmidt, Mats Jirstrand
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Computational and Systems Biology [PDF]
Molecular BioSystems, 2009 M. Madan Babu, Hirotada Mori
openaire +3 more sources
Bridging Nature and Technology: A Perspective on Role of Machine Learning in Bioinspired Ceramics
Advanced Engineering Materials, EarlyView.Machine learning (ML) is revolutionizing the development of bioinspired ceramics. This article investigates how ML can be used to design new ceramic materials with exceptional performance, inspired by the structures found in nature. The research highlights how ML can predict material properties, optimize designs, and create advanced models to unlock a ...
Hamidreza Yazdani Sarvestani +2 more
wiley +1 more source
PLoS Computational Biology[This corrects the article DOI: 10.1371/journal.pcbi.1011469.].
PLOS Computational Biology Staff
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Computational Options for Bioinformatics Research in Evolutionary Biology [PDF]
, 2005 Michael A. Thomas +2 more
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Advanced Engineering Materials, EarlyView.Molecular dynamics simulations are advancing the study of ribonucleic acid (RNA) and RNA‐conjugated molecules. These developments include improvements in force fields, long‐timescale dynamics, and coarse‐grained models, addressing limitations and refining methods.
Kanchan Yadav, Iksoo Jang, Jong Bum Lee
wiley +1 more source
Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials
Advanced Engineering Materials, EarlyView.This article explores how machine learning (ML) revolutionizes the study and design of disordered materials by uncovering hidden patterns, predicting properties, and optimizing multiscale structures. It highlights key advancements, including generative models, graph neural networks, and hybrid ML‐physics methods, addressing challenges like data ...
Hamidreza Yazdani Sarvestani +4 more
wiley +1 more source
The blossoming of methods and software in computational biology. [PDF]
PLoS Comput Biol, 2023 Mac Gabhann F, Pitzer VE, Papin JA.
europepmc +1 more source
Rigorous performance evaluation in protein structure modelling and implications for computational biology [PDF]
, 2006 John Moult
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