Results 141 to 150 of about 533,476 (267)

Learning representations on graphs [PDF]

National Science Review, 2018
openaire   +1 more source

AI‐Powered Framework for Evaluating Drug Efficacy for Three‐Dimensional In Vitro Cancer Models in Robot‐Assisted Production

Advanced Robotics Research, EarlyView.
An AI‐powered, robot‐assisted framework automatically produces, images, and analyzes 3D tumor spheroids to evaluate drug efficacy. Integrated modules handle spheroid formation, live/dead staining, brightfield imaging, and automated image analysis, including spheroid segmentation, viability and metrics to assess the drug treatment efficacy. The workflow
Dalia Mahdy, Jesus Nieves, Lukas Königer, Matthias Peindl, Fabio Erhard, Marc Möllmann, Anke Klingner, Markus Münig, Bibin Babu, Thomas Schwarz, Marco Metzger, Gudrun Dandekar, Maximiliane Wußmann, Jan Hansmann   +13 more
wiley   +1 more source

Graph-based clinical recommender: Predicting specialists procedure orders using graph representation learning. [PDF]

J Biomed Inform, 2023
Fouladvand S, Gomez FR, Nilforoshan H, Schwede M, Noshad M, Jee O, You J, Sosic R, Leskovec J, Chen J.   +9 more
europepmc   +1 more source

Grounding Large Language Models for Robot Task Planning Using Closed‐Loop State Feedback

Advanced Robotics Research, EarlyView.
BrainBody‐Large Language Model (LLM) introduces a hierarchical, feedback‐driven planning framework where two LLMs coordinate high‐level reasoning and low‐level control for robotic tasks. By grounding decisions in real‐time state feedback, it reduces hallucinations and improves task reliability.
Vineet Bhat, Ali Umut Kaypak, Prashanth Krishnamurthy, Ramesh Karri, Farshad Khorrami   +4 more
wiley   +1 more source

A knowledge graph representation learning approach to predict novel kinase-substrate interactions. [PDF]

Mol Omics, 2022
Gavali S, Ross K, Chen C, Cowart J, Wu CH.   +4 more
europepmc   +1 more source

Asymmetry in Skipping Enhances Viability Against Control Input Noise

Advanced Robotics Research, EarlyView.
Quadruped animals use asymmetric galloping gaits at high speeds, yet the functional role of this asymmetry remains unclear. This study shows that left–right asymmetry in touchdown angles enhances robustness to control noise. Using a simple two‐legged locomotion model and viability theory, it demonstrates that asymmetric skipping substantially enlarges ...
Yuichi Ambe, Alvin So, Shinya Aoi
wiley   +1 more source

iGRLDTI: an improved graph representation learning method for predicting drug-target interactions over heterogeneous biological information network. [PDF]

Bioinformatics, 2023
Zhao BW, Su XR, Hu PW, Huang YA, You ZH, Hu L.   +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

OFGPMA: Optimal frequency graph representation learning for pseudogene and miRNA association prediction. [PDF]

Front Genet
Zeng Y, Xiong L, Luo Y.
europepmc   +1 more source

iHerd: an integrative hierarchical graph representation learning framework to quantify network changes and prioritize risk genes in disease. [PDF]

PLoS Comput Biol, 2023
Duan Z, Dai Y, Hwang A, Lee C, Xie K, Xiao C, Xu M, Girgenti MJ, Zhang J.   +8 more
europepmc   +1 more source