Results 191 to 200 of about 43,364 (297)

Grammar error diagnosis using graph convolutional networks with knowledge graph integration. [PDF]

Sci Rep
Zhang J, Ma Y.
europepmc   +1 more source

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties With Phonon‐Informed Datasets

Advanced Intelligent Discovery, EarlyView.
Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez, Cibrán López, Edgardo Saucedo, Teruyasu Mizoguchi, Claudio Cazorla   +4 more
wiley   +1 more source

SigRescueR: a pan-system framework for noise correction and mutational signature identification across sequencing platforms. [PDF]

Brief Bioinform
Nguyen PT, Zhivagui M.
europepmc   +1 more source

The Interoperability Challenge in DFT Workflows Across Implementations

Advanced Intelligent Discovery, EarlyView.
Interoperability and cross‐validation remain major challenges in the computational materials science. In this work, we introduce a common input/output standard that enables internal translation across multiple workflow managers—AiiDA, PerQueue, Pipeline Pilot, and SimStack—while producing results in a unified schema.
Simon K. Steensen, Tushar Singh Thakur, Manuel Dillenz, Johan M. Carlsson, Celso R. C. Rêgo, Eibar Flores, Hamidreza Hajiyani, Felix Hanke, Juan María García Lastra, Wolfgang Wenzel, Nicola Marzari, Tejs Vegge, Giovanni Pizzi, Ivano E. Castelli   +13 more
wiley   +1 more source

Explainable Lightweight Model Using Low-Rank and Convolutional Block Attention for Pancreatic Cancer Diagnosis. [PDF]

Int J Med Robot
Tanwar V, Sharma B, Yadav DP, Liatsis P.
europepmc   +1 more source

A Robust Deep Temporal Causal Discovery Platform for Single‐Cell Gene Regulatory Network Reconstruction

Advanced Intelligent Discovery, EarlyView.
scTIGER2.0 is a deep‐learning framework that infers gene regulatory networks from single‐cell RNA sequencing data. By integrating correlation, pseudotime ordering, deep learning and bootstrap‐based significance testing, it reduces false positives and reveals directional gene interactions.
Nishi Gupta, Madison Dautle, Shaoqiang Zhang, Yong Chen   +3 more
wiley   +1 more source

Machine Learning the Decoherence Property of Superconducting and Semiconductor Quantum Devices from Graph Connectivity. [PDF]

Entropy (Basel)
Fu Q, Liu J, Wang X, Xiong R.
europepmc   +1 more source

Multi‐Property Machine Learning Models to Accelerate the Transition Toward Bio‐Based Emulsion Polymers

Advanced Intelligent Discovery, EarlyView.
A machine learning framework simultaneously predicts four critical properties of monomers for emulsion polymerization: propagation rate constant, reactivity ratios, glass transition temperature, and water solubility. These tools can be used to systematically identify viable bio‐based monomer pairs as replacements for conventional formulations, with ...
Kiarash Farajzadehahary, Nicholas Ballard   +1 more
wiley   +1 more source

Correction for Lee et al., Emergence of a contrast-invariant representation of naturalistic texture in macaque visual cortex. [PDF]

Proc Natl Acad Sci U S A
europepmc   +1 more source

Brief comments on "Perturbation response scanning of drug-target networks: Drug repurposing for multiple sclerosis". [PDF]

J Pharm Anal
Giuliani A.
europepmc   +1 more source