Results 71 to 80 of about 30,788 (321)

HiST: Histological Images Reconstruct Tumor Spatial Transcriptomics via MultiScale Fusion Deep Learning

Advanced Science, EarlyView.
HiST, a multiscale deep learning framework, reconstructs spatially resolved gene expression profiles directly from histological images. It accurately identifies tumor regions, captures intratumoral heterogeneity, and predicts patient prognosis and immunotherapy response.
Wei Li, Dong Zhang, Eryu Peng, Shijun Shen, Hamid Alinejad‐Rokny, Yao Liu, Junke Zheng, Cizhong Jiang, Youqiong Ye   +8 more
wiley   +1 more source

Development of a partially supervised Markov decision-making model for a 3-link collaborative robot-manipulator

Радіоелектронні і комп'ютерні системи
The subject are mathematical models of decision-making under uncertainty in a production environment with human presence. The research objectives: is to form a safe and effective policy for controlling the motion of a three-link collaborative robot ...
Vladyslav Yevsieiev, Dmytro Gurin, Sergii Kulish, Yuliia Voloshyn   +3 more
doaj   +1 more source

Which states matter? An application of an intelligent discretization method to solve a continuous POMDP in conservation biology. [PDF]

PLoS ONE, 2012
When managing populations of threatened species, conservation managers seek to make the best conservation decisions to avoid extinction. Making the best decision is difficult because the true population size and the effects of management are uncertain ...
Sam Nicol, Iadine Chadès
doaj   +1 more source

scPER: A Rigorous Computational Approach to Determine Cellular Subtypes in Tumors Aligned With Cancer Phenotypes From Total RNA Sequencing

Advanced Science, EarlyView.
scPER presents an adversarial‐autoencoder framework that deconvolves bulk total RNA‐seq to quantify tumor‐microenvironment cell types and uncover phenotype‐linked subclusters. Across diverse benchmarks, scPER improves accuracy over existing tools.
Bingrui Li, Xiaobo Zhou, Raghu Kalluri
wiley   +1 more source

Detecting Changes and Avoiding Catastrophic Forgetting in Dynamic Partially Observable Environments

Frontiers in Neurorobotics, 2020
The ability of an agent to detect changes in an environment is key to successful adaptation. This ability involves at least two phases: learning a model of an environment, and detecting that a change is likely to have occurred when this model is no ...
Jeffery Dick, Pawel Ladosz, Eseoghene Ben-Iwhiwhu, Hideyasu Shimadzu, Hideyasu Shimadzu, Peter Kinnell, Praveen K. Pilly, Soheil Kolouri, Andrea Soltoggio   +8 more
doaj   +1 more source

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

Mechanism‐Driven Screening of Membrane‐Targeting and Pore‐Forming Antimicrobial Peptides

Advanced Science, EarlyView.
To combat antibiotic resistance, this study employs mechanism‐driven screening with machine learning to identify pore‐forming antimicrobial peptides from amphibian and human metaproteomes. Seven peptides are validated, showing minimal toxicity and membrane disruption.
Jiaxuan Li, Chenguang Yang, Ruihan Dong, Juan Francisco Bada Juarez, Lei Wang, Maximilian Emanuel Wettstein, Dali Wang, Chan Cao, Ying Lu, Chen Song   +9 more
wiley   +1 more source

Drone Swarm Energy Management

Journal of Optimization, Differential Equations and Their Applications
This note presents an analytical framework for decision-making in drone swarm systems operating under uncertainty, based on the integration of Partially Observable Markov Decision Processes (POMDP) with Deep Deterministic Policy Gradient (DDPG ...
M. Z. Zgurovsky, P. O. Kasyanov, L. S. Paliichuk   +2 more
doaj   +1 more source

A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision Processes [PDF]

, 2021
Chengchun Shi, Masatoshi Uehara, Jiawei Huang, Nan Jiang   +3 more
openalex   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source