Results 201 to 210 of about 166,926 (313)

Improved gene regulatory network inference from single cell data with dropout augmentation. [PDF]

PLoS Comput Biol
Zhu H, Slonim DK.
europepmc   +1 more source

BACH, a Bayesian Optimization Protocol for Accurate Coarse‐Grained Parameterization of Organic Liquids

Advanced Functional Materials, EarlyView.
We present a fully automated Bayesian optimization (BO) protocol for the parameterization of nonbonded interactions in coarse‐grain CG force fields (BACH). Using experimental thermophysical data, we apply the protocol to a broad range of liquids, spanning linear, branched, and unsaturated hydrocarbons, esters, triglycerides, and water.
Janak Prabhu, Akhil Pratap Singh, Pablo Campomanes, Stefano Vanni   +3 more
wiley   +1 more source

Revealing hidden regulatory dependencies: multi-perspective graph learning for single-cell gene regulatory network inference. [PDF]

Brief Bioinform
He W, Zhang R, Zhu Y, Zhou H, Zuo Y, Bai Y, Yang L, Guo F.   +7 more
europepmc   +1 more source

Recurrent neural nets for networks inference of gene interactions using Markov chains

, 2007
Ígor Lorenzato Almeida
openalex   +1 more source

Shape‐Controlled Guanine Self‐Assemblies for Stable and Fast‐Ion Solid–Electrolyte Interphases in Sustainable Li Metal Batteries

Advanced Functional Materials, EarlyView.
The shape of guanine self‐assemblies is tuned by introducing alkyl (G8), fluoroalkyl (G8f), and oligoether (G8g) side chains into the G moiety. The scanning tunneling microscopy results and calculations show that the presence and type of the side chain strongly affect the G self‐assembly network.
So‐Huei Kang, So‐Dam Sohn, Kyung Min Lee, Seok‐Kyu Cho, Byongkyu Lee, Ji Eun Lee, Sang‐Young Lee, Sang Kyu Kwak, Hyung‐Joon Shin, Changduk Yang   +9 more
wiley   +1 more source

Gene regulatory network inference with popInfer reveals the dynamic regulation of hematopoietic stem cell quiescence. [PDF]

iScience
Rommelfanger MK, Behrends M, Chen Y, Martinez J, Kurella N, Geisler N, Guturu D, Bens M, Xiong L, Xiang Z, Rudolph KL, MacLean AL.   +11 more
europepmc   +1 more source

Structure Formation in Butterfly Scales: Interplay of Genetic Control, Mechanical Instabilities, and Dynamic Material Properties

Advanced Functional Materials, EarlyView.
Butterfly wing scales are intricate cuticular functional nanosctructures. This perspective suggests that spatially varying material properties, cytoskeletal constraints, and growth‐driven mechanical instabilities shape the resulting nanoscale architectures created from single cells.
Anupama Prakash, Victoria Lloyd, Yi Yang, Anna‐Lee Jessop, Anthony McDougal, Primož Pirih, Guillaume Duclos, Andrew Parnell, Mathias Kolle, Nicola J. Nadeau, Bodo D. Wilts   +10 more
wiley   +1 more source

TRENDY: gene regulatory network inference enhanced by transformer. [PDF]

Bioinformatics
Tian X, Patel Y, Wang Y.
europepmc   +1 more source

Inducing Ferromagnetism by Structural Engineering in a Strongly Spin‐Orbit Coupled Oxide

Advanced Functional Materials, EarlyView.
ABSTRACT Magnetic materials with strong spin‐orbit coupling (SOC) are essential for the advancement of spin‐orbitronic devices, as they enable efficient spin‐charge conversion, complex magnetic structures, spin‐valley physics, topological phases and other exotic phenomena.
Ji Soo Lim, Carmine Autieri, Merit Spring, Martin Kamp, Amar Fakhredine, Pavel Potapov, Daniel Wolf, Sergii Pylypenko, Axel Lubk, Johannes Schultz, Nicolas Perez, Börge Mehlhorn, Louis Veyrat, Mario Cuoco, Fadi Choueikan, Philippe Ohresser, Bernd Büchner, Giorgio Sangiovanni, Ralph Claessen, Michael Sing   +19 more
wiley   +1 more source

Generalized information criteria for personalized gene network inference. [PDF]

Front Genet
Park H, Imoto S, Konishi S.
europepmc   +1 more source