Results 1 to 10 of about 522,455 (311)

Long Time CO2 Storage Under Ambient Conditions in Isolated Voids of a Porous Coordination Network Facilitated by the “Magic Door” Mechanism

Advanced Science
A coordination network containing isolated pores without interconnecting channels is prepared from a tetrahedral ligand and copper(I) iodide. Despite the lack of accessibility, CO2 is selectively adsorbed into these pores at 298 K and then retained for ...
Terumasa Shimada, Pavel M. Usov, Yuki Wada, Hiroyoshi Ohtsu, Taku Watanabe, Kiyohiro Adachi, Daisuke Hashizume, Takaya Matsumoto, Masaki Kawano   +8 more
doaj   +1 more source

Machine learning modeling practices to support the principles of AI and ethics in nutrition research

Nutrition & Diabetes, 2022
Background Nutrition research is relying more on artificial intelligence and machine learning models to understand, diagnose, predict, and explain data. While artificial intelligence and machine learning models provide powerful modeling tools, failure to
Diana M. Thomas, Samantha Kleinberg, Andrew W. Brown, Mason Crow, Nathaniel D. Bastian, Nicholas Reisweber, Robert Lasater, Thomas Kendall, Patrick Shafto, Raymond Blaine, Sarah Smith, Daniel Ruiz, Christopher Morrell, Nicholas Clark   +13 more
doaj   +1 more source

Design and analysis strategies for robust microbiome ageing research

FEBS Letters, EarlyView.
The gut microbiome changes with age and associates with age‐related morbidity and mortality, establishing it as a potential biomarker and intervention target for ageing. Realising this potential requires methodological rigour, yet distinguishing biological signals from methodological artefacts remains challenging across cohorts. This review provides an
Mark Olenik, İsmail Güderer, Yi Wang, Tayyaba Alvi, Prasoon Pandey, Handan Melike Dönertaş   +5 more
wiley   +1 more source

Efficient ensemble uncertainty estimation in Gaussian processes regression

Machine Learning: Science and Technology
Reliable uncertainty measures are required when using data-based machine learning interatomic potentials (MLIPs) for atomistic simulations. In this work, we propose for sparse Gaussian process regression (GPR) type MLIPs a stochastic uncertainty measure ...
Mads-Peter Verner Christiansen, Nikolaj Rønne, Bjørk Hammer   +2 more
doaj   +1 more source

Reconstructing enzyme evolution by protein engineering

FEBS Letters, EarlyView.
Natural enzyme evolution can be retraced by protein engineering methods such as directed evolution, rational design, and ancestral sequence reconstruction. These approaches reveal how enzymes emerged from ligand‐binding scaffolds, developed varying substrate preferences, formed oligomeric complexes, adapted to environmental changes, and evolved novel ...
Lukas Drexler, Markus R. Busch, Reinhard Sterner   +2 more
wiley   +1 more source

Development of Machine Learning Atomistic Potential for Molecular Simulation of Hematite–Water Interfaces

Crystals
A novel approach for constructing a machine-learned potential energy surface (MLP) from unlabeled training data is presented. Utilizing neural networks augmented with a pool-based active learning sampling method, a potential energy surface (PES) is ...
Mozhdeh Shiranirad, Niall J. English
doaj   +1 more source

Subtype‐specific enhancer RNAs define transcriptional regulators and prognosis in breast cancers

Molecular Oncology, EarlyView.
This study employed machine learning methodologies to perform the subtype‐specific classification of RNA‐seq data sets, which are mapped on enhancers from TCGA‐derived breast cancer patients. Their integration with gene expression (referred to as ProxCReAM eRNAs) and chromatin accessibility profiles has the potential to identify lineage‐specific and ...
Aamena Y. Patel, Peyman Zarrineh, Nathnael T. Tuffa, Jigar H. Sheth, Sumitra Mohan, Mudassar Iqbal, Sankari Nagarajan   +6 more
wiley   +1 more source

Phase Relations and Melting of SiO2 Under Mantle Conditions Determined From Machine Learning Potentials

Geophysical Research Letters
SiO2 is a fundamental component of planetary interiors, yet its high‐pressure melting and phase relations remain uncertain. We develop a machine learning potential with first‐principles accuracy and perform large‐scale two‐phase coexistence simulations ...
Xin Deng, Jie Deng, Michael J. Walter, Yingwei Fei, Jing Yang, R. E. Cohen   +5 more
doaj   +1 more source

Development of a TaN-Ce Machine Learning Potential and Its Application to Solid–Liquid Interface Simulations

Metals
This study develops a machine learning potential (MLP) based on the Moment Tensor Potential (MTP) method for the TaN-Ce system. This potential is employed to investigate the interfacial structure and wetting behavior between liquid Ce and solid TaN ...
Yunhan Zhang, Jianfeng Cai, Hongjian Chen, Xuming Lv, Bowen Huang   +4 more
doaj   +1 more source

Interpreting the effects of DNA polymerase variants at the structural level

Molecular Oncology, EarlyView.
Using MAVISp and molecular dynamics simulations, we analyzed over 60 000 missense variants in POLE and POLD1 from ClinVar, COSMIC, cBioPortal, and saturation mutagenesis. Identified mechanistic indicators, including stability, binding, and long‐range, enable structural interpretation, providing ACMG‐like evidence for possible reclassification of VUS ...
Matteo Arnaudi, Karolina Krzesińska, Ludovica Beltrame, Pablo Sánchez‐Izquierdo Besora, Matteo Tiberti, Mef Nilbert, Anna Rohlin, Elena Papaleo   +7 more
wiley   +1 more source