Results 71 to 80 of about 771,482 (302)

Ovarian Cancer Diagnosis and Chemoresistance Prediction Model Based on cfRNA Molecular Signature

Advanced Science, EarlyView.
A deep learning model analyzes cfRNA profiles extracted from the blood of OVCA patients. This innovative approach distinguishes OVCA from healthy controls with high accuracy. Crucially, it reliably predicts patient response to chemotherapy (sensitive versus resistant subgroups).
Qinhao Guo, Yangyang Zhang, Yongcheng Jin, Siwei Deng, Yongqi Chen, Zheng Feng, Hao Wen, Liu Wang, Yilin Li, Fanghong Ou, Yong Shen, Haiming Li, Tianyao Zhou, Xingzhu Ju, Xiaohua Wu   +14 more
wiley   +1 more source

Modelling the purchase dynamics of insurance customers using Markov chains

, 2005
This paper considers how various types of Markov chains can be used to help forecast the purchase behaviour of customers. The models are used in a case study of the purchase behaviour of the customers of a major insurance company.
Thomas, Lyn C., Thomas, Stephen, Bozzetto, Jean-Francois, Tang, Leilei   +3 more
core   +1 more source

An Application of Graph Theory in Markov Chains Reliability Analysis

Advances in Electrical and Electronic Engineering, 2014
The paper presents reliability analysis which was realized for an industrial company. The aim of the paper is to present the usage of discrete time Markov chains and the flow in network approach.
Pavel Skalny
doaj   +1 more source

On Learning Markov Chains

CoRR, 2018
The problem of estimating an unknown discrete distribution from its samples is a fundamental tenet of statistical learning. Over the past decade, it attracted significant research effort and has been solved for a variety of divergence measures. Surprisingly, an equally important problem, estimating an unknown Markov chain from its samples, is still far
Yi Hao, Alon Orlitsky, Venkatadheeraj Pichapati   +2 more
openaire   +4 more sources

Exact Discrete Stochastic Simulation With Deep‐Learning‐Scale Gradient Optimization

Advanced Science, EarlyView.
A 203,796‐parameter gene regulatory network classifies handwritten digits with 98.4% accuracy using exact stochastic dynamics. The framework decouples forward simulation from backward differentiation, making continuous‐time Markov chain models compatible with deep‐learning optimization.
Jose M. G. Vilar, Leonor Saiz
wiley   +1 more source

Whole‐Genome Sequencing Pilot of the Central Asian Genomic Diversity Project Reveals Distinct Histories, Adaptation, and Introgression

Advanced Science, EarlyView.
As a pilot phase of the Central Asian Genomic Diversity Project, whole‐genome sequencing of 166 individuals from 20 Central Asian and Afghan Hazara populations reveals fine‐scale substructure shaped by repeated trans‐Eurasian migration and admixture. Integrated analyses uncover post‐admixture adaptation, archaic introgression, and medically relevant ...
Mengge Wang, Shuhan Duan, Qiuxia Sun, Renkuan Tang, Lintao Luo, Jie Zhong, Zhaxylyk Sabitov, CAGDP Consortium†, Lan‐Hai Wei, Chao Liu, Maxat Zhabagin, Guanglin He   +11 more
wiley   +1 more source

Enhanced Antibacterial Properties of Lyotropic Liquid Crystalline Nanoparticles via Curvature Modulation

Advanced Science, EarlyView.
Lipid nanostructured particles with increasing negative Gaussian curvature progress from vesicles to P‐cubosomes and D‐cubosomes. This curvature hierarchy enhances interaction with bacterial membranes, promotes membrane remodeling and leakage, and potentiates daptomycin activity against MRSA.
Xiangfeng Lai, Shuhong Wang, Chenguang Ding, Xenia Kostoulias, Anton P. Le Brun, Hsien‐Yi Hsu, Jhih‐Hang Jiang, Yajun Wang, Richard A. Strugnell, Anton Y. Peleg, Hsin‐Hui Shen   +10 more
wiley   +1 more source

Exponential inequalities for nonstationary Markov chains

Dependence Modeling, 2019
Exponential inequalities are main tools in machine learning theory. To prove exponential inequalities for non i.i.d random variables allows to extend many learning techniques to these variables.
Alquier Pierre, Doukhan Paul, Fan Xiequan   +2 more
doaj   +1 more source

A Hybrid Markov and LSTM Model for Indoor Location Prediction

IEEE Access, 2019
Accurate and robust indoor location prediction plays an important role in indoor location services. Markov chains (MCs) have been widely adopted for location prediction due to their strong interpretability. However, multi-order Markov chains (k -MCs) are
Peixiao Wang, Hongen Wang, Hengcai Zhang, Feng Lu, Sheng Wu   +4 more
doaj   +1 more source

Markov chains conditioned never to wait too long at the origin [PDF]

, 2009
Motivated by Feller's coin-tossing problem, we consider the problem of conditioning an irreducible Markov chain never to wait too long at 0. Denoting by τ the first time that the chain, X, waits for at least one unit of time at the origin, we consider ...
Jacka, Saul D.
core   +1 more source