Results 241 to 250 of about 624,160 (323)
CAGEcleaner: reducing genomic redundancy in gene cluster mining. [PDF]
De Vrieze L +4 more
europepmc +1 more source
The unrolling of the peltate leaves in Syngonium podophyllum is analyzed and quantified (left‐hand side to center). These measurements serve to verify a mathematical model for leaf unrolling based on the model used in Schmidt (2007). An additional formula for obtaining a layer mismatch from a prescribed radius is derived.
Michelle Modert +4 more
wiley +1 more source
Integrating transcriptomic and epigenomic data to identify potential biomarkers in gestational diabetes mellitus patients. [PDF]
Mitra T +4 more
europepmc +1 more source
Bridging Nature and Technology: A Perspective on Role of Machine Learning in Bioinspired Ceramics
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
The BeeBiome data portal provides easy access to bee microbiome information. [PDF]
Rech de Laval V +3 more
europepmc +1 more source
Active Learning with Unbalanced Classes and Example-Generation Queries
Christopher Lin +2 more
openalex +2 more sources
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
Nucleotide dependency analysis of genomic language models detects functional elements. [PDF]
Tomaz da Silva P +7 more
europepmc +1 more source
Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials
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
Improving data sharing and knowledge transfer via the Neuroelectrophysiology Analysis Ontology (NEAO). [PDF]
Köhler CA, Grün S, Denker M.
europepmc +1 more source

