Results 121 to 130 of about 1,589,421 (354)

CellPolaris: Transfer Learning for Gene Regulatory Network Construction to Guide Cell State Transitions

Advanced Science, EarlyView.
CellPolaris decodes how transcription factors guide cell fate by building gene regulatory networks from transcriptomic data using transfer learning. It generates tissue‐ and cell‐type‐specific networks, identifies master regulators in cell state transitions, and simulates TF perturbations in developmental processes.
Guihai Feng, Xin Qin, Jiahao Zhang, Wuliang Huang, Yiyang Zhang, Wentao Cui, Yao Chen, Shirui Li, Wenhao Liu, Yao Tian, Yana Liu, Jingxi Dong, Ping Xu, Zhenpeng Man, Guole Liu, Zhongming Liang, Xinlong Jiang, Xiaodong Yang, Pengfei Wang, Ge Yang, Hongmei Wang, Xuezhi Wang, Ming‐Han Tong, Yuanchun Zhou, Shihua Zhang, Yiqiang Chen, Yong Wang, Xin Li   +27 more
wiley   +1 more source

Non-Parametric Signal Interpolation

Austrian Journal of Statistics, 2016
This paper considers the problem of interpolation (smoothing) of a partially observable Markov random sequence. For the dynamic observation models, an equation for the interpolation of the posterior probability density is derived.
Alexandr V. Dobrovidov
doaj   +1 more source

Fused kernel-spline smoothing for repeatedly measured outcomes in a generalized partially linear model with functional single index [PDF]

, 2015
Fei Jiang, Yanyuan Ma, Yuanjia Wang
openalex   +1 more source

Single‐Field Evolution Rule Governs the Dynamics of Representational Drift in Mouse Hippocampal Dorsal CA1 Region

Advanced Science, EarlyView.
Long‐term hippocampal place‐code dynamics are investigated using calcium imaging across weeks of maze navigation. Analyses reveal a novelty‐irrelevant Single‐Field Evolution Rule (SFER), where active fields promote persistence and inactive fields decline.
Cong Chen, Shuyang Yao, Sihui Cheng, Jiayi Tian, Ang Li, Yusen Yan, Xiang Zhang, Yuanjing Liu, Yumeng Wang, Qichen Cao, Chenglin Miao   +10 more
wiley   +1 more source

Integral operators with infinitely smooth bi-Carleman kernels of Mercer type [PDF]

, 2012
Igor M. Novitskii
openalex   +1 more source

The Structurally Smoothed Graphlet Kernel

, 2014
A commonly used paradigm for representing graphs is to use a vector that contains normalized frequencies of occurrence of certain motifs or sub-graphs. This vector representation can be used in a variety of applications, such as, for computing similarity between graphs. The graphlet kernel of Shervashidze et al. [32] uses induced sub-graphs of k nodes (
Yanardag, Pinar, Vishwanathan, S. V. N.
openaire   +2 more sources

The Geometry of Layer 2/3 Cortical Sound Processing in Slow Wave Sleep

Advanced Science, EarlyView.
Sleep is associated with a sensory disconnection whose mechanisms remain elusive. Large neuronal population recordings in the auditory cortex revealed that, in NREM sleep, the neural code for sounds is highly similar to wakefulness, but coordinated modulations of neuron responsiveness intermittently disconnect the local cortical networks from sensory ...
Allan Muller, Anton Filipchuk, Sophie Bagur, Brice Bathellier   +3 more
wiley   +1 more source

Empirical and Kernel Estimation of the ROC Curve

Acta Universitatis Lodziensis. Folia Oeconomica, 2015
The paper presents chosen methods for estimating the ROC (Receiver Operating Characteristic) curve, including parametric and nonparametric procedures.
Aleksandra Katarzyna Baszczyńska
doaj  

Kernel-Based Smoothness Analysis of Residual Networks [PDF]

, 2020
Tom Tirer, Joan Bruna, Raja Giryes
openalex   +1 more source

Nonparametric Regression on Latent Covariates with an Application to Semiparametric GARCH-in-Mean Models [PDF]

We consider time series models in which the conditional mean of the response variable given the past depends on latent covariates. We assume that the covariates can be estimated consistently and use an iterative nonparametric kernel smoothing procedure ...
Christian Conrad, Enno Mammen
core