Analyzing Transnational Narratives in Twitter: An Unsupervised Approach Using PARAFAC

2021 ◽  
pp. 59-71
Author(s):  
Peter A. Chew ◽  
Jonathan A. G. Chew
Keyword(s):  
Unsupervised Approach

scholarly journals CSO Classifier 3.0: a scalable unsupervised method for classifying documents in terms of research topics

2021 ◽  
Author(s):  
Angelo Salatino ◽  
Francesco Osborne ◽  
Enrico Motta
Keyword(s):  
Computer Science ◽  
Digital Libraries ◽  
Gold Standard ◽  
Research Trends ◽  
Alternative Methods ◽  
Relevant Research ◽  
Research Topics ◽  
Research Papers ◽  
Research Areas ◽  
Unsupervised Approach

AbstractClassifying scientific articles, patents, and other documents according to the relevant research topics is an important task, which enables a variety of functionalities, such as categorising documents in digital libraries, monitoring and predicting research trends, and recommending papers relevant to one or more topics. In this paper, we present the latest version of the CSO Classifier (v3.0), an unsupervised approach for automatically classifying research papers according to the Computer Science Ontology (CSO), a comprehensive taxonomy of research areas in the field of Computer Science. The CSO Classifier takes as input the textual components of a research paper (usually title, abstract, and keywords) and returns a set of research topics drawn from the ontology. This new version includes a new component for discarding outlier topics and offers improved scalability. We evaluated the CSO Classifier on a gold standard of manually annotated articles, demonstrating a significant improvement over alternative methods. We also present an overview of applications adopting the CSO Classifier and describe how it can be adapted to other fields.

scholarly journals A deep learning unsupervised approach for fault diagnosis of household appliances

2020 ◽  
Vol 53 (2) ◽  
pp. 10749-10754
Author(s):  
Francesco Cordoni ◽  
Gianluca Bacchiega ◽  
Giulio Bondani ◽  
Robert Radu ◽  
Riccardo Muradore
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Household Appliances ◽  
Unsupervised Approach

scholarly journals Protein Binding Partners of Dysregulated miRNAs in Parkinson’s Disease Serum

Cells ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 791
Author(s):  
Wolfgang P. Ruf ◽  
Axel Freischmidt ◽  
Veselin Grozdanov ◽  
Valerie Roth ◽  
Sarah J. Brockmann ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Protein Binding ◽  
Neurodegenerative Diseases ◽  
Extracellular Vesicles ◽  
Abundance Pattern ◽  
Binding Partners ◽  
Direct Binding ◽  
Unsupervised Approach ◽  
High Degree

Accumulating evidence suggests that microRNAs (miRNAs) are a contributing factor to neurodegenerative diseases. Although altered miRNA profiles in serum or plasma have been reported for several neurodegenerative diseases, little is known about the interaction between dysregulated miRNAs and their protein binding partners. We found significant alterations of the miRNA abundance pattern in serum and in isolated serum-derived extracellular vesicles of Parkinson’s disease (PD) patients. The differential expression of miRNA in PD patients was more robust in serum than in isolated extracellular vesicles and could separate PD patients from healthy controls in an unsupervised approach to a high degree. We identified a novel protein interaction partner for the strongly dysregulated hsa-mir-4745-5p. Our study provides further evidence for the involvement of miRNAs and HNF4a in PD. The demonstration that miRNA-protein binding might mediate the pathologic effects of HNF4a both by direct binding to it and by binding to proteins regulated by it suggests a complex role for miRNAs in pathology beyond the dysregulation of transcription.

scholarly journals DNA Methylation-based Signatures Classify Sporadic Pituitary Tumors According to Clinicopathological Features

2021 ◽  
Author(s):  
Maritza S Mosella ◽  
Thais S Sabedot ◽  
Tiago C Silva ◽  
Tathiane M Malta ◽  
Felipe D Segato ◽  
...  
Keyword(s):  
Functional Status ◽  
Validation Cohort ◽  
Pituitary Tumors ◽  
Clinicopathological Features ◽  
Cell Lineages ◽  
Adenohypophyseal Cell ◽  
Genome Wide ◽  
Unsupervised Approach ◽  
Recent Classification ◽  
Methylation Patterns

Abstract Background Distinct genome-wide methylation patterns cluster pituitary neuroendocrine tumors (PitNETs) into molecular groups associated with specific clinicopathological features. Here we aim to identify, characterize and validate methylation signatures that objectively classify PitNET into clinicopathological groups. Methods Combining in-house and publicly available data, we conducted an analysis of the methylome profile of a comprehensive cohort of 177 tumors (Panpit cohort) and 20 nontumor specimens from the pituitary gland. We also retrieved methylome data from an independent PitNET cohort (N=86) to validate our findings. Results We identified three methylation clusters associated with adenohypophyseal cell lineages and functional status using an unsupervised approach. Differentially methylated CpG probes (DMP) significantly distinguished the Panpit clusters and accurately assigned the samples of the validation cohort to their corresponding lineage and functional subtypes memberships. The DMPs were annotated in regulatory regions enriched of enhancer elements, associated with pathways and genes involved in pituitary cell identity, function, tumorigenesis, invasiveness. Some DMPs correlated with genes with prognostic and therapeutic values in other intra or extracranial tumors. Conclusions We identified and validated methylation signatures, mainly annotated in enhancer regions that distinguished PitNETs by distinct adenohypophyseal cell lineages and functional status. These signatures provide the groundwork to develop an unbiased approach to classifying PitNETs according to the most recent classification recommended by the 2017 WHO and to explore their biological and clinical relevance in these tumors.

A composed supervised/unsupervised approach to improve change detection from remote sensing

2007 ◽  
Vol 28 (4) ◽  
pp. 405-413 ◽  
Author(s):  
L. Castellana ◽  
A. D’Addabbo ◽  
G. Pasquariello
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Unsupervised Approach

scholarly journals Hydrometeor classification through statistical clustering of polarimetric radar measurements: a semi-supervised approach

2016 ◽  
Vol 9 (9) ◽  
pp. 4425-4445 ◽  
Author(s):  
Nikola Besic ◽  
Jordi Figueras i Ventura ◽  
Jacopo Grazioli ◽  
Marco Gabella ◽  
Urs Germann ◽  
...  
Keyword(s):  
Polarimetric Radar ◽  
Statistical Framework ◽  
X Band ◽  
Different Types ◽  
Unsupervised Approach ◽  
Radar Measurements ◽  
Supervised Classification Methods ◽  
Operational Potential ◽  
Statistical Clustering

Abstract. Polarimetric radar-based hydrometeor classification is the procedure of identifying different types of hydrometeors by exploiting polarimetric radar observations. The main drawback of the existing supervised classification methods, mostly based on fuzzy logic, is a significant dependency on a presumed electromagnetic behaviour of different hydrometeor types. Namely, the results of the classification largely rely upon the quality of scattering simulations. When it comes to the unsupervised approach, it lacks the constraints related to the hydrometeor microphysics. The idea of the proposed method is to compensate for these drawbacks by combining the two approaches in a way that microphysical hypotheses can, to a degree, adjust the content of the classes obtained statistically from the observations. This is done by means of an iterative approach, performed offline, which, in a statistical framework, examines clustered representative polarimetric observations by comparing them to the presumed polarimetric properties of each hydrometeor class. Aside from comparing, a routine alters the content of clusters by encouraging further statistical clustering in case of non-identification. By merging all identified clusters, the multi-dimensional polarimetric signatures of various hydrometeor types are obtained for each of the studied representative datasets, i.e. for each radar system of interest. These are depicted by sets of centroids which are then employed in operational labelling of different hydrometeors. The method has been applied on three C-band datasets, each acquired by different operational radar from the MeteoSwiss Rad4Alp network, as well as on two X-band datasets acquired by two research mobile radars. The results are discussed through a comparative analysis which includes a corresponding supervised and unsupervised approach, emphasising the operational potential of the proposed method.

An unsupervised approach for fault diagnosis of power transformers

2021 ◽  
Author(s):  
Luis Dias ◽  
Miguel Ribeiro ◽  
Armando Leitão ◽  
Luis Guimarães ◽  
Leonel Carvalho ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Power Transformers ◽  
Unsupervised Approach
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