Results 71 to 80 of about 22,384 (303)

PlantGFM: A Genomic Foundation Model for Discovery and Creation of Plant Genes

Advanced Science, EarlyView.
A plant genomic foundation model pre‐trained on 12 species enables both accurate gene prediction and de novo gene design. Through AI‐human knowledge screening, seven designed sequences showed transcriptional activity in plants, with two expressing stable proteins—demonstrating the first DNA‐RNA‐protein expression of LLM‐generated genes in plants and ...
Changhao Li, Qizhe Zhang, Hanchen Chen, Kepeng Lin, Chengfang Luo, Mengying Yang, Wei Xu, Fan Yao, Jianbing Yan, Qing‐Yong Yang, Xuehai Hu   +10 more
wiley   +1 more source

Deep Randomly-Connected Conditional Random Fields For Image Segmentation

IEEE Access, 2017
The use of Markov random fields (MRFs) is a common approach for performing image segmentation, where the problem is modeled using MRFs that incorporate priors on neighborhood nodes to allow for efficient Maximum a Posteriori inference.
Mohammad Javad Shafiee, Alexander Wong, Paul Fieguth   +2 more
doaj   +1 more source

MCMC generation of cosmological fields far beyond Gaussianity

The Open Journal of Astrophysics, 2021
Structure formation in our Universe creates non-Gaussian random fields that will soon be observed over almost the entire sky by the Euclid satellite, the Vera-Rubin observatory, and the Square Kilometre Array.
Joey R. Braspenning, Elena Sellentin
doaj   +1 more source

Relational Neural Markov Random Fields

CoRR, 2021
Statistical Relational Learning (SRL) models have attracted significant attention due to their ability to model complex data while handling uncertainty. However, most of these models have been limited to discrete domains due to their limited potential functions.
Yuqiao Chen, Sriraam Natarajan, Nicholas Ruozzi   +2 more
openaire   +3 more sources

Morphological Markov random fields [PDF]

, 1994
We introduce a set of morphologically specified Markov random fields, that extends the standard set of models by using the operators of mathematical morphology.
Carstensen, Jens Michael
core   +1 more source

PDIA6–SCD1 Axis Rewires Lipid Metabolism to Drive Gastric Cancer Progression

Advanced Science, EarlyView.
Protein disulfide isomerase A6 (PDIA6) is identified as an oncogenic driver in gastric cancer. PDIA6 directly binds and stabilizes SCD1 by limiting its ubiquitin–proteasome‐mediated degradation, thereby sustaining monounsaturated fatty acid (MUFA)‐enriched lipid homeostasis and lipid metabolic reprogramming.
Zhen Tian, Yifan Cheng, Jiajie Zhou, Ruiqi Li, Shuai Zhao, Ben Li, Zijie Xu, Mengli Zi, Yayan Fu, Chenkai Zhang, Qiannan Sun, Shantanu Baral, Sen Wang, Daorong Wang   +13 more
wiley   +1 more source

Decision Fusion Framework for Hyperspectral Image Classification Based on Markov and Conditional Random Fields

Remote Sensing, 2019
Classification of hyperspectral images is a challenging task owing to the high dimensionality of the data, limited ground truth data, collinearity of the spectra and the presence of mixed pixels.
Vera Andrejchenko, Wenzhi Liao, Wilfried Philips, Paul Scheunders   +3 more
doaj   +1 more source

MAP entropy estimation: applications in robust image filtering [PDF]

Journal of the European Optical Society-Rapid Publications, 2013
We introduce a new approach for image filtering in a Bayesian framework. In this case the probability density function (pdf) of the likelihood function is approximated using the concept of non-parametric or kernel estimation.
de la Rosa J. I., Villa-Hernandez J., de la Rosa M. E., Gonzalez-Ramirez E., Gutierrez O., Escalante N., Ivanov R., Fleury G.   +7 more
doaj   +1 more source

Random walk with barycentric self-interaction [PDF]

, 2010
We study the asymptotic behaviour of a $d$-dimensional self-interacting random walk $X_n$ ($n = 1,2,...$) which is repelled or attracted by the centre of mass $G_n = n^{-1} \sum_{i=1}^n X_i$ of its previous trajectory. The walk's trajectory $(X_1,...,X_n)
Volkov, S., Stanislav Volkov, Volkov, S, Comets, F., Wade, Andrew R., Wade, A.R., Andrew R. Wade, Francis Comets, Menshikov, Mikhail V., Mikhail V. Menshikov, Comets, Francis, Menshikov, M.V., Wade, AR, Volkov, Stanislav   +13 more
core   +2 more sources

AI‐Physics‐Experiment Trinity for Integrated Protein Dynamics Modeling

Advanced Science, EarlyView.
This review unites experiments, physics‐based simulations, and AI as a synergistic triad for protein dynamics modeling. It highlights integrative strategies, resolves sampling and forcefield bottlenecks, and outlines challenges and future directions for accurate, interpretable conformational ensemble prediction.
Chen Shi, Minying Low, Peng Xiu, Kresten Lindorff‐Larsen, Yong Wang   +4 more
wiley   +1 more source