Results 111 to 120 of about 2,475,679 (305)

Optimizing inventory management: a causal inference-driven Bayesian network with transfer learning adaptation [PDF]

PeerJ Computer Science
Inventory management faces increasing challenges, including data limitations and demand uncertainty. To enhance inventory forecasting and optimization in supply chain management, this study proposes a Transfer-learning Bayesian Network (TBN) framework ...
Zhu Xi, Wei Guan, Ahmet Savasan
doaj   +2 more sources

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

Bayesian structure learning in graphical models

Journal of Multivariate Analysis, 2015
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Banerjee, Sayantan, Ghosal, Subhashis
openaire   +1 more source

CLinNET: An Interpretable and Uncertainty‐Aware Deep Learning Framework for Multi‐Modal Clinical Genomics

Advanced Science, EarlyView.
Identifying disease‐causing genes in neurocognitive disorders remains challenging due to variants of uncertain significance. CLinNET employs dual‐branch neural networks integrating Reactome pathways and Gene Ontology terms to provide pathway‐level interpretability of genomic alterations.
Ivan Bakhshayeshi, Mohammad Mahdi Hosseini, Ahmadreza Argha, Roxana Zahedi, Nigel H. Lovell, Hamid Alinejad‐Rokny   +5 more
wiley   +1 more source

A Parsimonious Model of Subjective Life Expectancy [PDF]

This paper develops a theoretical model for the formation of subjective beliefs on individual survival expectations. Data from the Health and Retirement Study (HRS) indicate that, on average, young respondents underestimate their true survival ...
Ludwig, Alexander, Zimper, Alexander
core  

DualPG‐DTA: A Large Language Model‐Powered Graph Neural Network Framework for Enhanced Drug‐Target Affinity Prediction and Discovery of Novel CDK9 Inhibitors Exhibiting in Vivo Anti‐Leukemia Activity

Advanced Science, EarlyView.
This study introduces DualPG‐DTA, a framework integrating two pre‐trained models to generate molecular and protein representations. It constructs dual graphs processed by specialized neural networks with dynamic attention for feature fusion, achieving superior benchmark performance.
Yihao Chen, Jindi Huang, Cong Liu, Shipeng Zhang, Xinze Li, Zhang Zhang, Tie‐Gen Chen, Ling Wang   +7 more
wiley   +1 more source

Learning Under Ambiguity [PDF]

This paper considers learning when the distinction between risk and ambiguity matters. It first describes thought experiments, dynamic variants of those provided by Ellsberg, that highlight a sense in which the Bayesian learning model is extreme - it ...
Larry Epstein, Martin Schneider
core  

S3RL: Enhancing Spatial Single‐Cell Transcriptomics With Separable Representation Learning

Advanced Science, EarlyView.
Separable Spatial Representation Learning (S3RL) is introduced to enhance the reconstruction of spatial transcriptomic landscapes by disentangling spatial structure and gene expression semantics. By integrating multimodal inputs with graph‐based representation learning and hyperspherical prototype modeling, S3RL enables high‐fidelity spatial domain ...
Laiyi Fu, Penglei Wang, Gaoyuan Xu, Jitao Lu, Qinke Peng, Danyang Wu, Hequan Sun   +6 more
wiley   +1 more source

Benchmarking uncertainty quantification for protein engineering.

PLoS Computational Biology
Machine learning sequence-function models for proteins could enable significant advances in protein engineering, especially when paired with state-of-the-art methods to select new sequences for property optimization and/or model improvement. Such methods
Kevin P Greenman, Ava P Amini, Kevin K Yang   +2 more
doaj   +1 more source

Modification Strategies of Carbon‐Based Electrodes From Structural Regulation to Multifunctional Integration

Advanced Science, EarlyView.
Tracing the evolution from structural regulation to multifunctional integration, this paper systematically analyzes modification strategies for carbon‐based electrodes. It evaluates how element doping, surface functionalization, and composite material design affect the electrode performance, and offers perspectives on future applications and challenges
Yunlei Wang, Shitao Dou, Yifan Wu, Mingguang Wang, Taibin Wu   +4 more
wiley   +1 more source