Results 91 to 100 of about 149,758 (276)

STAID: A Self‐Refining Deep Learning Framework for Spatial Cell‐Type Deconvolution with Biologically Informed Modeling

Advanced Science, EarlyView.
STAID is a unified deep learning framework that couples iterative pseudo‐spot refinement with neural network training through a feedback loop and exploits gene co‐expression information to model higher‐order interactions, achieving accurate and robust cell‐type deconvolution in spatial transcriptomics.
Jixin Liu, Shuli Sun, Zhengliang Lv, Xinyu Liu, Yihua Wang, Bingqiang Liu   +5 more
wiley   +1 more source

dbnR: Gaussian Dynamic Bayesian Network Learning and Inference in R

Journal of Statistical Software
Dynamic Bayesian networks are a type of multivariate time series forecasting model capable of a level of interpretability thanks to their graphical representation.
David Quesada, Pedro Larrañaga, Concha Bielza   +2 more
doaj   +1 more source

Reverse engineering directed gene regulatory networks from transcriptomics and proteomics data of biomining bacterial communities with approximate Bayesian computation and steady-state signalling simulations

BMC Bioinformatics, 2020
Background Network inference is an important aim of systems biology. It enables the transformation of OMICs datasets into biological knowledge.
Antoine Buetti-Dinh, Malte Herold, Stephan Christel, Mohamed El Hajjami, Francesco Delogu, Olga Ilie, Sören Bellenberg, Paul Wilmes, Ansgar Poetsch, Wolfgang Sand, Mario Vera, Igor V. Pivkin, Ran Friedman, Mark Dopson   +13 more
doaj   +1 more source

Emerging Memory and Device Technologies for Hardware‐Accelerated Model Training and Inference

Advanced Electronic Materials, EarlyView.
This review investigates the suitability of various emerging memory technologies as compute‐in‐memory hardware for artificial intelligence (AI) applications. Distinct requirements for training‐ and inference‐centric computing are discussed, spanning device physics, materials, and system integration.
Yoonho Cho, Dae‐won Kim, Yujin Kim, Jung‐Heum Na, Yuwon Jeong, Dong‐Yeop Lee, Shinhyun Choi   +6 more
wiley   +1 more source

Accelerating delayed-acceptance Markov chain Monte Carlo algorithms

, 2019
Delayed-acceptance Markov chain Monte Carlo (DA-MCMC) samples from a probability distribution via a two-stages version of the Metropolis-Hastings algorithm, by combining the target distribution with a "surrogate" (i.e.
Boomsma, Wouter, Forman, Julie Lyng, Lindorff-Larsen, Kresten, Picchini, Umberto, Wiqvist, Samuel   +4 more
core  

Approximating Bayesian Inference through Internal Sampling [PDF]

, 2023
Sundh, Joakim, Sanborn, Adam, Zhu, Jian-Qiao, Spicer, Jake, León-Villagrá, Pablo, Chater, Nick   +5 more
openaire   +1 more source

Inversion of the Impedance Response Towards Physical Parameter Extraction Using Interpretable Machine Learning

Advanced Energy Materials, EarlyView.
ABSTRACT Interpreting the impedance response of perovskite solar cells (PSCs) is challenging due to the complex coupling of ionic and electronic motion. While drift‐diffusion (DD) modelling is a reliable method, its mathematical complexity makes directly extracting physical parameters from experimental data infeasible.
Mahmoud Nabil, Isel Grau, Ricardo Grau‐Crespo, Said Hamad, Juan A. Anta   +4 more
wiley   +1 more source

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

Parameter estimation for allometric trophic network models: A variational Bayesian inverse problem approach

Methods in Ecology and Evolution
Differential equation models are powerful tools for predicting biological systems, capable of projecting far into the future and incorporating data recorded at arbitrary times.
Maria Tirronen, Anna Kuparinen
doaj   +1 more source

Structured Bayesian Approximate Inference [PDF]

, 2018
This thesis seeks to investigate different facets of the class of Bayesian probabilistic models where the random variables exhibit strong dependencies and simultaneously lack any conditional independence structure, preventing the distribution from being factorized.
openaire