Results 61 to 70 of about 207,453 (329)

Transmembrane topology and signal peptide prediction using dynamic bayesian networks. [PDF]

PLoS Computational Biology, 2008
Hidden Markov models (HMMs) have been successfully applied to the tasks of transmembrane protein topology prediction and signal peptide prediction.
Sheila M Reynolds, Lukas Käll, Michael E Riffle, Jeff A Bilmes, William Stafford Noble   +4 more
doaj   +1 more source

Dynamic Bayesian networks and variable length genetic algorithm for designing cue-based model for dialogue act recognition [PDF]

, 2010
The automatic recognition of dialogue act is a task of crucial importance for the processing of natural language dialogue at discourse level. It is also one of the most challenging problems as most often the dialogue act is not expressed directly in ...
Mahmod, Ramlan, Ramli, Abd Rahman, Yahya, Anwar Ali   +2 more
core   +1 more source

Active Learning‐Accelerated Discovery of Fibrous Hydrogels with Tissue‐Mimetic Viscoelasticity

Advanced Functional Materials, EarlyView.
Active learning accelerates the design of fibrous hydrogels that mimic the viscoelasticity of native tissues. By integrating multi‐objective optimization and closed‐loop experimentation, this approach efficiently identifies optimal formulations from thousands of possibilities and decouples elasticity and viscosity. The resulting hydrogels offer tunable
Zhengkun Chen, Zheyuan Miao, Gary Tom, Austin H. Cheng, Elizaveta Gusarova, Benjamen Zondag, Tianyi Wu, Dianoosh Kalhori, Boris Hinz, Milad Abolhasani, Alán Aspuru‐Guzik, Eugenia Kumacheva   +11 more
wiley   +1 more source

CausNet-partial: 'Partial Generational Orderings' based search for optimal sparse Bayesian networks via dynamic programming with parent set constraints.

PLoS ONE
In our recent work, we developed a novel dynamic programming algorithm to find optimal Bayesian networks with parent set constraints. This 'generational orderings' based dynamic programming algorithm-CausNet-efficiently searches the space of possible ...
Nand Sharma, Joshua Millstein
doaj   +1 more source

Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks [PDF]

, 2012
Recurrent neural networks (RNNs) are widely used in computational neuroscience and machine learning applications. In an RNN, each neuron computes its output as a nonlinear function of its integrated input.
A Doucet, A Lazar, A Parlos, AH Jazwinski, B Cessac, C Archambeau, C Summerfield, D Debanne, D Mottet, D Perdikis, D Perdikis, D Verstraeten, DV Buonomano, EA Wan, EA Wan, EK Miller, EK Pissadaki, FH Hamker, G Schöner, GE Hinton, H Jaeger, J Daunizeau, J Ting-Ho Lo, JAS Kelso, JL Elman, JT Connor, K Friston, K Friston, K Narendra, K Sidiropoulou, KJ Friston, KJ Friston, KJ Friston, M Bar, M Boerlin, MI Rabinovich, N Spruston, R Blake, R Legenstein, R Wilson, RC Sotero, RP Rao, RP Rao, RPN Rao, S Denève, S Denève, S Rodrigues, S Roweis, Sebastian Bitzer, SJ Kiebel, SJ Kiebel, SJ Kiebel, SJ Kiebel, Stefan J. Kiebel, TB Schön, V Wassenhove van, VK Jirsa, W Maass, Z Ghahramani   +58 more
core   +2 more sources

Beyond Presumptions: Toward Mechanistic Clarity in Metal‐Free Carbon Catalysts for Electrochemical H2O2 Production via Data Science

Advanced Materials, EarlyView.
Metal‐free carbon catalysts enable the sustainable synthesis of hydrogen peroxide via two‐electron oxygen reduction; however, active site complexity continues to hinder reliable interpretation. This review critiques correlation‐based approaches and highlights the importance of orthogonal experimental designs, standardized catalyst passports ...
Dayu Zhu, Hsi‐wen Wu, Xiao Wang, Jie Zhang   +3 more
wiley   +1 more source

System-of-Systems Resilience Analysis and Design Using Bayesian and Dynamic Bayesian Networks

Mathematics
A System-of-Systems (SoS) is characterized both by independence and by inter-dependency. This inter-dependency, while allowing an SoS to achieve its objectives, also means that failures can cascade throughout the SoS. An SoS needs to be resilient to deal
Tianci Jiao, Hao Yuan, Jing Wang, Jun Ma, Xiaoling Li, Aimin Luo   +5 more
doaj   +1 more source

Asynchronous Dynamic Bayesian Networks

, 2012
Systems such as sensor networks and teams of autonomous robots consist of multiple autonomous entities that interact with each other in a distributed, asynchronous manner. These entities need to keep track of the state of the system as it evolves.
Pfeffer, Avi, Tai, Terry
openaire   +2 more sources

Self‐Assembled Monolayers in p–i–n Perovskite Solar Cells: Molecular Design, Interfacial Engineering, and Machine Learning–Accelerated Material Discovery

Advanced Materials, EarlyView.
This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley   +1 more source

Learning dynamic Bayesian networks [PDF]

, 1998
Bayesian networks are a concise graphical formalism for describing probabilistic models. We have provided a brief tutorial of methods for learning and inference in dynamic Bayesian networks. In many of the interesting models, beyond the simple linear dynamical system or hidden Markov model, the calculations required for inference are intractable.
openaire   +1 more source