References of "Truică, Ciprian-Octavian"
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See detailHISTORIAE, History of Socio-Cultural Transformation as Linguistic Data Science. A Humanities Use Case
Armaselu, Florentina UL; Apostol, Elena-Simona; Khan, Anas Fahad et al

in Open Access Series in Informatics (2021), 93(2021), 341-3413

The paper proposes an interdisciplinary approach including methods from disciplines such as history of concepts, linguistics, natural language processing (NLP) and Semantic Web, to create a comparative ... [more ▼]

The paper proposes an interdisciplinary approach including methods from disciplines such as history of concepts, linguistics, natural language processing (NLP) and Semantic Web, to create a comparative framework for detecting semantic change in multilingual historical corpora and generating diachronic ontologies as linguistic linked open data (LLOD). Initiated as a use case (UC4.2.1) within the COST Action Nexus Linguarum, European network for Web-centred linguistic data science, the study will explore emerging trends in knowledge extraction, analysis and representation from linguistic data science, and apply the devised methodology to datasets in the humanities to trace the evolution of concepts from the domain of socio-cultural transformation. The paper will describe the main elements of the methodological framework and preliminary planning of the intended workflow. [less ▲]

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See detailStatistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions
Moreno-Indias, Isabel; Lahti, Leo; Nedyalkova, Miroslava et al

in Frontiers in Microbiology (2021), 12

The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and ... [more ▼]

The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 “ML4Microbiome” that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies. [less ▲]

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