Results 191 to 200 of about 64,337 (241)

Human herpesvirus-associated transposable element activation in human aging brains with Alzheimer's disease. [PDF]

Alzheimers Dement
Feng Y, Cao SQ, Shi Y, Sun A, Flanagan ME, Leverenz JB, Pieper AA, Jung JU, Cummings J, Fang EF, Zhang P, Cheng F.   +11 more
europepmc   +1 more source

Graph‐based imitation and reinforcement learning for efficient Benders decomposition

AIChE Journal, EarlyView.
Abstract This work introduces an end‐to‐end graph‐based agent for accelerating the computational efficiency of Benders Decomposition. The agent's policy is parameterized by a graph neural network, which takes as input a bipartite graph representation of the master problem and proposes a candidate solution.
Bernard T. Agyeman, Zhe Li, Ilias Mitrai, Prodromos Daoutidis   +3 more
wiley   +1 more source

Exploring Quantum Support Vector Regression for Predicting Hydrogen Storage Capacity of Nanoporous Materials

Advanced Intelligent Discovery, EarlyView.
In this study we employed support vector regressor and quantum support vector regressor to predict the hydrogen storage capacity of metal–organic frameworks using structural and physicochemical descriptors. This study presents a comparative analysis of classical support vector regression (SVR) and quantum support vector regression (QSVR) in predicting ...
Chandra Chowdhury
wiley   +1 more source

GenomeDelta: detecting recent transposable element invasions without repeat library. [PDF]

Genome Biol
Pianezza R, Haider A, Kofler R.
europepmc   +1 more source

A Machine Learning Perspective on the Brønsted–Evans–Polanyi Relation in Water‐Gas Shift Catalysis on MXenes

Advanced Intelligent Discovery, EarlyView.
Machine learning predicts activation energies for key steps in the water‐gas shift reaction on 92 MXenes. Random Forest is identified as the most accurate model. Reaction energy and reactant LogP emerge as key descriptors. The approach provides a predictive framework for catalyst design, grounded in density functional theory data and validated through ...
Kais Iben Nassar, Tiago L. P. Galvão, José D. Gouveia, José R. B. Gomes   +3 more
wiley   +1 more source

Effective population size does not explain long-term variation in genome size and transposable element content in animals. [PDF]

Elife
Marino A, Debaecker G, Fiston-Lavier AS, Haudry A, Nabholz B.   +4 more
europepmc   +1 more source

Macrophage Phenotype Detection Methodology on Textured Surfaces via Nuclear Morphology Using Machine Learning

Advanced Intelligent Discovery, EarlyView.
A novel machine learning approach classifies macrophage phenotypes with up to 98% accuracy using only nuclear morphology from DAPI‐stained images. Bypassing traditional surface markers, the method proves robust even on complex textured biomaterial surfaces. It offers a simpler, faster alternative for studying macrophage behavior in various experimental
Oleh Mezhenskyi, Kateryna Andriienkova, Tetyana Goldanova, Ezgi Antmen, Thibaut Soubrie, Nihal Engin Vrana   +5 more
wiley   +1 more source

MCHelper automatically curates transposable element libraries across eukaryotic species. [PDF]

Genome Res
Orozco-Arias S, Sierra P, Durbin R, González J.   +3 more
europepmc   +1 more source

From Junk DNA to Genomic Treasure: Impacts of Transposable Element DNA, RNA, and Protein in Mammalian Development and Disease. [PDF]

Wiley Interdiscip Rev RNA
Li TD, Toohill K, Modzelewski AJ.
europepmc   +1 more source

Evolutionary Dynamics of Transposable Element Activity and Regulation in the Apennine Yellow-Bellied Toad (Bombina pachypus). [PDF]

Genome Biol Evol
Ancona L, Nitta Fernandes FA, Biello R, Chiocchio A, Castrignanò T, Barucca M, Canestrelli D, Trucchi E.   +7 more
europepmc   +1 more source