Protein Clustering and Classification

2007 ◽  
pp. 203-226 ◽  
Author(s):  
Ori Sasson ◽  
Michal Linial
Keyword(s):  
Protein Clustering ◽  
Clustering And Classification

Model-Based Clustering and Classification for Data Science

2019 ◽  
Author(s):  
Charles Bouveyron ◽  
Gilles Celeux ◽  
T. Brendan Murphy ◽  
Adrian E. Raftery
Keyword(s):  
Data Science ◽  
Model Based Clustering ◽  
Model Based ◽  
Clustering And Classification

Evaluation of protein clustering of pancreatic cancer

2016 ◽  
Vol 1 (2) ◽  
pp. 0-0
Author(s):  
Akram Safaei ◽  
Mostafa Rezaei Tavirani ◽  
Afsaneh Arefi Oskouei ◽  
Mona Zamanian Azodi ◽  
Seyed Reza Mohebbi
Keyword(s):  
Pancreatic Cancer ◽  
Protein Clustering

scholarly journals A set theory based similarity measure for text clustering and classification

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Ali A. Amer ◽  
Hassan I. Abdalla
Keyword(s):  
Set Theory ◽  
Similarity Measure ◽  
Similarity Measures ◽  
Text Clustering ◽  
Plagiarism Detection ◽  
K Nearest Neighbor ◽  
Single Measure ◽  
Highly Effective ◽  
Clustering And Classification ◽  
Effectiveness And Efficiency

Abstract Similarity measures have long been utilized in information retrieval and machine learning domains for multi-purposes including text retrieval, text clustering, text summarization, plagiarism detection, and several other text-processing applications. However, the problem with these measures is that, until recently, there has never been one single measure recorded to be highly effective and efficient at the same time. Thus, the quest for an efficient and effective similarity measure is still an open-ended challenge. This study, in consequence, introduces a new highly-effective and time-efficient similarity measure for text clustering and classification. Furthermore, the study aims to provide a comprehensive scrutinization for seven of the most widely used similarity measures, mainly concerning their effectiveness and efficiency. Using the K-nearest neighbor algorithm (KNN) for classification, the K-means algorithm for clustering, and the bag of word (BoW) model for feature selection, all similarity measures are carefully examined in detail. The experimental evaluation has been made on two of the most popular datasets, namely, Reuters-21 and Web-KB. The obtained results confirm that the proposed set theory-based similarity measure (STB-SM), as a pre-eminent measure, outweighs all state-of-art measures significantly with regards to both effectiveness and efficiency.

scholarly journals Bayesian analysis of data from segmented super-resolution images for quantifying protein clustering

2020 ◽  
Vol 22 (3) ◽  
pp. 1107-1114
Author(s):  
Tina Košuta ◽  
Marta Cullell-Dalmau ◽  
Francesca Cella Zanacchi ◽  
Carlo Manzo
Keyword(s):  
Bayesian Analysis ◽  
Relative Abundance ◽  
Bayesian Approach ◽  
Super Resolution ◽  
Molecular Aggregates ◽  
Protein Clustering ◽  
Precise Quantification

A Bayesian approach enables the precise quantification of the relative abundance of molecular aggregates of different stoichiometry from segmented super-resolution images.

scholarly journals Performance evaluation of ImmunoCAP® ISAC 112: a multi-site study

2017 ◽  
Vol 55 (4) ◽  
Author(s):  
Marianne van Hage ◽  
Peter Schmid-Grendelmeier ◽  
Chrysanthi Skevaki ◽  
Mario Plebani ◽  
Walter Canonica ◽  
...  
Keyword(s):  
South African ◽  
Low Frequency ◽  
Specific Ige ◽  
High Concentration ◽  
Serum Samples ◽  
Site Analysis ◽  
Calibration Sample ◽  
Clustering And Classification ◽  
Study Sites ◽  
Positive Results

Abstract Background: After the re-introduction of ImmunoCAP Methods: The study was carried out at 22 European and one South African site. Microarrays from different batches, eight specific IgE (sIgE) positive, three sIgE negative serum samples and a calibration sample were sent to participating laboratories where assays were performed according to the manufacturer’s instructions. Results: For both the negative and positive samples results were consistent between sites, with a very low frequency of false positive results (0.014%). A similar pattern of results for each of the samples was observed across the 23 sites. Homogeneity analysis of all measurements for each sample were well clustered, indicating good reproducibility; unsupervised hierarchical clustering and classification via random forests, showed clustering of identical samples independent of the assay site. Analysis of raw continuous data confirmed the good accuracy across the study sites; averaged standardized, site-specific ISU-E values fell close to the center of the distribution of measurements from all sites. After outlier filtering, variability across the whole study was estimated at 25.5%, with values of 22%, 27.1% and 22.4% for the ‘Low’, ‘Moderate to High’ and ‘Very High’ concentration categories, respectively. Conclusions: The study shows a robust performance of the ImmunoCAP

scholarly journals Kawasaki Disease Patient Stratification and Pathway Analysis Based on Host Transcriptomic and Proteomic Profiles

2021 ◽  
Vol 22 (11) ◽  
pp. 5655
Author(s):  
Heather Jackson ◽  
Stephanie Menikou ◽  
Shea Hamilton ◽  
Andrew McArdle ◽  
Chisato Shimizu ◽  
...  
Keyword(s):  
Kawasaki Disease ◽  
Pathway Analysis ◽  
Host Response ◽  
Viral Infections ◽  
Disease Patient ◽  
Inflammatory Disorder ◽  
Comprehensive Picture ◽  
Clustering And Classification ◽  
Infection And Inflammation ◽  
Proteomic Responses

The aetiology of Kawasaki disease (KD), an acute inflammatory disorder of childhood, remains unknown despite various triggers of KD having been proposed. Host ‘omic profiles offer insights into the host response to infection and inflammation, with the interrogation of multiple ‘omic levels in parallel providing a more comprehensive picture. We used differential abundance analysis, pathway analysis, clustering, and classification techniques to explore whether the host response in KD is more similar to the response to bacterial or viral infections at the transcriptomic and proteomic levels through comparison of ‘omic profiles from children with KD to those with bacterial and viral infections. Pathways activated in patients with KD included those involved in anti-viral and anti-bacterial responses. Unsupervised clustering showed that the majority of KD patients clustered with bacterial patients on both ‘omic levels, whilst application of diagnostic signatures specific for bacterial and viral infections revealed that many transcriptomic KD samples had low probabilities of having bacterial or viral infections, suggesting that KD may be triggered by a different process not typical of either common bacterial or viral infections. Clustering based on the transcriptomic and proteomic responses during KD revealed three clusters of KD patients on both ‘omic levels, suggesting heterogeneity within the inflammatory response during KD. The observed heterogeneity may reflect differences in the host response to a common trigger, or variation dependent on different triggers of the condition.

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