Results 161 to 170 of about 1,586,831 (297)

biochem4j: Integrated and extensible biochemical knowledge through graph databases. [PDF]

PLoS One, 2017
Swainston N, Batista-Navarro R, Carbonell P, Dobson PD, Dunstan M, Jervis AJ, Vinaixa M, Williams AR, Ananiadou S, Faulon JL, Mendes P, Kell DB, Scrutton NS, Breitling R.   +13 more
europepmc   +1 more source

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source

Fast and Secure Multiparty Querying over Federated Graph Databases. [PDF]

SN Comput Sci
Aljuaid N, Lisitsa A, Schewe S.
europepmc   +1 more source

Are graph databases ready for bioinformatics? [PDF]

Bioinformatics, 2013
Have CT, Jensen LJ.
europepmc   +1 more source

Graph rewriting primitives for semantic graph databases sanitization

International audienceDue to the rapid proliferation of data online, an important quantity of private or sensitive informations is being stored as linked data in graph databases (e.g., represented as RDF).
Eichler, Cédric, Nguyen, Benjamin, Boiret, Adrien, Taki, Sara   +3 more
core   +1 more source

Breaking Bottlenecks in Solid Electrolyte Discovery With Large Artificial Intelligence Models

Advanced Materials, EarlyView.
Large AI models are presented as a route to break key bottlenecks in solid electrolyte discovery by linking materials databases, machine‐learning interatomic potentials, large language models, and autonomous experiments. This perspective highlights how AI can accelerate the identification of solid electrolytes with fast ion transport, stable interfaces,
Eric Jianfeng Cheng, Min Hong, Zhiquan Zeng, Chuanyu Liu, Qian Wang, Melissa Jane Meadowcroft, Vlad Badilita, Carlos Miguel Costa, Senentxu Lanceros‐Méndez, Ying Li, Chenyao Ma, Weibo Gong, Surendra Kumar Martha, Aloysius Soon, Piao Ma, Di Zhang, Teguh Ariyanto, Sudaryanto, Hiroshi Kakinuma, Jie Zhao, Jiayu Peng, Pengfei Ou, Jun Lu, Shin‐ichi Orimo, Hao Li   +24 more
wiley   +1 more source

DFB-PPGSQ: Dynamic Forward-Private and Epochal Backward-Private Graph Similarity Matching Query over Encrypted Sensor Graph Databases. [PDF]

Sensors (Basel)
Hou H, Ma Y, Zhao Z, Ge X.
europepmc   +1 more source

Representing and querying disease networks using graph databases. [PDF]

BioData Min, 2016
Lysenko A, Roznovăţ IA, Saqi M, Mazein A, Rawlings CJ, Auffray C.   +5 more
europepmc   +1 more source

Backpropagation Through Soft Body: Investigating Information Processing in Brain–Body Coupling Systems

Advanced Robotics Research, EarlyView.
This study explores how information processing is distributed between brains and bodies through a codesign approach. Using the “backpropagation through soft body” framework, brain–body coupling agents are developed and analyzed across several tasks in which output is generated through the agents’ physical dynamics.
Hiroki Tomioka, Katsuma Inoue, Yasuo Kuniyoshi, Kohei Nakajima   +3 more
wiley   +1 more source

Creating an Interactive Web Interface for Networks Stored in Knowledge Graph Databases. [PDF]

Curr Protoc
Evangelista JE, Lutsky AD, Byrd AI, Clarke DJB, Prabhakaran A, Jenkins SL, Ma'ayan A.   +6 more
europepmc   +1 more source