TOP-Rank: A Novel Unsupervised Approach for Topic Prediction Using Keyphrase Extraction for Urdu Documents

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.

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A deep learning unsupervised approach for fault diagnosis of household appliances

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2020.12.2856 ◽

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

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

Cells ◽

10.3390/cells10040791 ◽

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.

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