Results 51 to 60 of about 112,162 (267)
Molecular Oncology, EarlyView.Single‐cell multi‐omics reveals epigenetic heterogeneity across therapy‐adaptive tumor states, including quiescent/dormant, drug‐tolerant persister, and EMT‐like phenotypes. By linking regulatory features with state‐associated biomarkers, these approaches inform biomarker‐guided therapeutic strategies for evolving tumors.
Hee Jung Kim +3 more
wiley +1 more source
Observational Learning by Reinforcement Learning
CoRR, 2017 Observational learning is a type of learning that occurs as a function of observing, retaining and possibly replicating or imitating the behaviour of another agent. It is a core mechanism appearing in various instances of social learning and has been found to be employed in several intelligent species, including humans. In this paper, we investigate to
Diana Borsa +3 more
openaire +2 more sources
Imitation Learning by Reinforcement Learning
CoRR, 2021 Imitation learning algorithms learn a policy from demonstrations of expert behavior. We show that, for deterministic experts, imitation learning can be done by reduction to reinforcement learning with a stationary reward. Our theoretical analysis both certifies the recovery of expert reward and bounds the total variation distance between the expert and
openaire +3 more sources
Molecular Oncology, EarlyView.Single‐cell DNA methylation (scDNAme) profiling maps epimutational clonal evolution, revealing mechanisms of malignancy and therapeutic resistance across diverse cancer types. By providing a high‐resolution landscape of intratumoral heterogeneity, these technologies empower precise patient stratification, guide the development of enhanced ...
Ik Soo Kim
wiley +1 more source
Reinforcement Explanation Learning
CoRR, 2021 Deep Learning has become overly complicated and has enjoyed stellar success in solving several classical problems like image classification, object detection, etc. Several methods for explaining these decisions have been proposed. Black-box methods to generate saliency maps are particularly interesting due to the fact that they do not utilize the ...
Siddhant Agarwal +4 more
openaire +2 more sources
FEBS Open Bio, EarlyView.The cytoskeleton‐mediated transport of mitochondria via tunnelling nanotubes restores respiration, increases ATP production, rescues cells from apoptosis, activates the AKT/mTOR signalling pathway, promotes cell migration and invasiveness, contributes to cancer progression and treatment resistance.
Stanislava Martínková, Jan Trnka
wiley +1 more source
Intelligent Tutoring Systems for Adult Learning in STEM Disciplines
New Directions for Adult and Continuing Education, EarlyView.ABSTRACT Intelligent tutoring systems (ITS) are reshaping adult learning in STEM by providing adaptive, data‐driven instruction across classrooms, workplaces, and informal environments. In the context of ITS, this article compares generative AI, which creates personalized explanations and practice materials, with explainable AI, which focuses on ...
Jill Zarestky, Amanda R. Lager Gleason
wiley +1 more source
Safe Model-Based Reinforcement Learning for Systems With Parametric Uncertainties
Frontiers in Robotics and AI, 2021 Reinforcement learning has been established over the past decade as an effective tool to find optimal control policies for dynamical systems, with recent focus on approaches that guarantee safety during the learning and/or execution phases.
S. M. Nahid Mahmud +3 more
doaj +1 more source
New Directions for Adult and Continuing Education, EarlyView.ABSTRACT Mental well‐being is central to adult learner success, yet many adult education institutions lack capacity to provide timely and accessible support. This article examines how artificial intelligence (AI) can strengthen mental health–adjacent supports in adult and continuing higher education, with attention to professional practice and ...
Adam L. McClain, Thomas Wade
wiley +1 more source
Clustering with Reinforcement Learning
, 2007 We show how a previously derived method of using reinforcement learning for supervised clustering of a data set can lead to a sub-optimal solution if the cluster prototypes are initialised to poor positions. We then develop three novel reward functions which show great promise in overcoming poor initialization. We illustrate the results on several data
Barbakh, Wesam, Fyfe, Colin
openaire +2 more sources