Search results for " Computational Approaches"

showing 3 items of 3 documents

Five Ways in Which Computational Modeling Can Help Advance Cognitive Science

2019

Abstract There is a rich tradition of building computational models in cognitive science, but modeling, theoretical, and experimental research are not as tightly integrated as they could be. In this paper, we show that computational techniques—even simple ones that are straightforward to use—can greatly facilitate designing, implementing, and analyzing experiments, and generally help lift research to a new level. We focus on the domain of artificial grammar learning, and we give five concrete examples in this domain for (a) formalizing and clarifying theories, (b) generating stimuli, (c) visualization, (d) model selection, and (e) exploring the hypothesis space.

Linguistics and LanguageArtificial grammar learningComputer scienceCognitive Neuroscience[SHS.PSY]Humanities and Social Sciences/PsychologyExperimental and Cognitive PsychologyBayesian inferenceArtificial grammar learningArticle050105 experimental psychology03 medical and health sciences0302 clinical medicineArtificial IntelligenceHumans0501 psychology and cognitive sciencesCognitive scienceComputational modelPsycholinguisticsArtificial neural networkLift (data mining)Model selection05 social sciencesComputational modelingModels TheoreticalArtificial language learningFormal grammarsExperimental researchBayesian modelingVisualizationHuman-Computer InteractionCognitive ScienceNeural Networks ComputerForthcoming Topic: Learning Grammatical Structures: Developmental Cross‐species and Computational Approaches030217 neurology & neurosurgeryNeural networksTopics in Cognitive Science
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Prediction of Disease–lncRNA Associations via Machine Learning and Big Data Approaches

2021

This chapter introduces long non-coding RNAs and their role in the occurrence and progress of diseases. The discovery of novel lncRNA-disease associations may provide valuable input to the understanding of disease mechanisms at the lncRNA level, as well as to the detection of biomarkers for disease diagnosis, treatment, prognosis, and prevention. Unfortunately, due to costs and time complexity, the number of possible disease-related lncRNAs verified by traditional biological experiments is very limited. Computational approaches for the prediction of potential disease-lncRNA associations can effectively decrease the time and cost of biological experiments. We first review the main computatio…

business.industryComputer scienceBig Data Technologies Biological Processes Computational Approaches Disease–lncRNA Associations Non-Coding RNA Hypergeometric distribution Leave One Out Cross Validation Long non-coding RNA Master-Slave Architecture Micro-RNA.Big dataArtificial intelligenceDiseasebusinessMachine learningcomputer.software_genrecomputer
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Modelling Keloids Dynamics: A Brief Review and New Mathematical Perspectives

2022

The keloids are fibroproliferative disorders described by an excessive growth of fibrotic tissue, which also invades adjacent areas (beyond the original wound borders). Since these disorders are specific to humans (no other animal species naturally develop keloid-like tissue), the experimental in vivo/in vitro research has not lead to significant advances in this field. One possible approach could be to combine in vitro human models with calibrated in silico mathematical approaches (i.e., models and simulations) to generate new testable biological hypotheses related to biological mechanisms and improved treatments. Since these combined approaches do not really exist for keloid disorders, in…

[SDV] Life Sciences [q-bio]modelling cancer and wound healinginflammationkeloid disordersreviewmathematical modelling and computational approaches[MATH] Mathematics [math]biomechanics
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