Results 101 to 110 of about 2,318,151 (292)
Biomolecular Interaction Prediction: The Era of AI
Advanced SciencePredicting biomolecular interactions is a crucial task in drug discovery and molecular biology. Deep learning, with its ability to learn complex patterns from large datasets, has shown promising results in predicting biomolecular interactions.
Haoping Wang, Xiangjie Meng, Yang Zhang
doaj +1 more source
Learning Interactions in Reaction Diffusion Equations by Neural Networks. [PDF]
Entropy (Basel), 2023 Chen S, Brunel NJ, Yang X, Cui X.
europepmc +1 more source
Stakeholder engagement as a facilitator of organizational learning [PDF]
, 2008 This paper examines the relationship between stakeholder engagement and competence building. Following the dual perspective of the firm, which indicated that managers deal with both transactions and competences concurrently, we argue that stakeholder ...
Eweje, Gabriel, Wu, Minyu
core
CLIC: Curriculum Learning and Imitation for object Control in non-rewarding environments
, 2019 In this paper we study a new reinforcement learning setting where the environment is non-rewarding, contains several possibly related objects of various controllability, and where an apt agent Bob acts independently, with non-observable intentions.
Chetouani, Mohamed +3 more
core +1 more source
Adult‐Onset Subacute Sclerosing Panencephalitis Presenting With Subacute Cognitive Deficits
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT We describe the case of a 41‐year‐old man diagnosed with adult‐onset subacute sclerosing panencephalitis (SSPE). The patient presented with subacute progressive cognitive deficits and a neuropsychological profile indicating predominant frontoparietal dysfunction. MRI showed only mild parietal‐predominant cerebral atrophy.
Dennis Yeow +4 more
wiley +1 more source
Learning epistatic polygenic phenotypes with Boolean interactions.
PLoS ONEDetecting epistatic drivers of human phenotypes is a considerable challenge. Traditional approaches use regression to sequentially test multiplicative interaction terms involving pairs of genetic variants.
Merle Behr +11 more
doaj +3 more sources
Learning to interact, interacting to learn action-centric reinforcement learning
, 2021 Dans cette thèse de doctorat, nous étudions l'apprentissage séquentiel (dit ``par renforcement'') en intelligence artificielle, plus particulièrement les notions d'actions et d'interactivité. En apprentissage par renforcement, un agent reçoit des informations sur son environnement et agit en conséquence.
openaire +1 more source
Applying an Ethical Lens to the Treatment of People With Multiple Sclerosis
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT The practice of neurology requires an understanding of clinical ethics for decision‐making. In multiple sclerosis (MS) care, there are a wide range of ethical considerations that may arise. These involve shared decision‐making around selection of a disease‐modifying therapy (DMT), risks and benefits of well‐studied medications in comparison to
Methma Udawatta, Farrah J. Mateen
wiley +1 more source
Museum Learning via Social Media: (How) Can Interactions on Twitter Enhance the Museum Learning Experience? [PDF]
, 2011 Museums are rich sources of artifacts, people and potential dialogic interactions. Recent developments in web technologies pose big challenges to museums to integrate such technologies in their learning provision.
Charitonos, Koula
core
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Glioma recurrence severely impacts patient prognosis, with current treatments showing limited efficacy. Traditional methods struggle to analyze recurrence mechanisms due to challenges in assessing tumor heterogeneity, spatial dynamics, and gene networks.
Lei Qiu +10 more
wiley +1 more source