0000000000019686

AUTHOR

Armando La Placa

showing 3 related works from this author

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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Identifying the k Best Targets for an Advertisement Campaign via Online Social Networks

2020

We propose a novel approach for the recommendation of possible customers (users) to advertisers (e.g., brands) based on two main aspects: (i) the comparison between On-line Social Network profiles, and (ii) neighborhood analysis on the On-line Social Network. Profile matching between users and brands is considered based on bag-of-words representation of textual contents coming from the social media, and measures such as the Term Frequency-Inverse Document Frequency are used in order to characterize the importance of words in the comparison. The approach has been implemented relying on Big Data Technologies, allowing this way the efficient analysis of very large Online Social Networks. Resul…

Social and Information Networks (cs.SI)FOS: Computer and information sciencesMatching (statistics)Social networkSettore INF/01 - Informaticabusiness.industryComputer scienceBig dataDatabases (cs.DB)AdvertisingComputer Science - Social and Information NetworksOnline Social Networks Social Advertising tf-idf Profile Matching.Term (time)Computer Science - Information RetrievalSet (abstract data type)Computer Science - DatabasesOrder (business)Computer Science - Data Structures and AlgorithmsData Structures and Algorithms (cs.DS)Social mediabusinessRepresentation (mathematics)Information Retrieval (cs.IR)
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Prediction of lncRNA-Disease Associations from Tripartite Graphs

2021

The discovery of novel lncRNA-disease associations may provide valuable input to the understanding of disease mechanisms at 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 time and cost of biological experiments. We propose an approach for the prediction of lncRNA-disease associations based on neighborhood analysis performed on a tripartite graph, built upon …

Tripartite graphsDecision support systemComputer scienceDisease mechanismsIdentification (biology)lncRNA-disease associations predictionDiseaseComputational biologyTime complexityGraphDecision support
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