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Unsupervised pattern recognition: An introduction to the whys and wherefores of clustering microarray data

P. C. Boutros, A. B. Okey
2005 Briefings in Bioinformatics  
Clustering has become an integral part of microarray data analysis and interpretation.  ...  The algorithmic basis of clustering -the application of unsupervised machine-learning techniques to identify the patterns inherent in a data set -is well established.  ...  Acknowledgments The authors gratefully thank Dr Albert Wong and Ms Mawahib Semeralul for permission to use their  ... 
doi:10.1093/bib/6.4.331 pmid:16420732 fatcat:iresoac4bzcqfjy4v5gmuwphii

Toward reliable biomarker signatures in the age of liquid biopsies - how to standardize the small RNA-Seq workflow

Dominik Buschmann, Anna Haberberger, Benedikt Kirchner, Melanie Spornraft, Irmgard Riedmaier, Gustav Schelling, Michael W. Pfaffl
2016 Nucleic Acids Research  
The complexity of the small RNA-Seq workflow, however, bears challenges and biases that researchers need to be aware of in order to generate high-quality data.  ...  This will ultimately enhance the validity of biomarker signatures, and allow reliable and robust clinical predictions.  ...  These analyses generate clusters of similarities, specifically similar gene expression patterns in the case of RNA-Seq, by using methods for dimension reduction combined with pattern recognition technologies  ... 
doi:10.1093/nar/gkw545 pmid:27317696 pmcid:PMC5291277 fatcat:4fixci2cbbfyjc4bj6tq72eqdq

Time series distance measures [article]

Stephan Spiegel, Technische Universität Berlin, Technische Universität Berlin, Sahin Albayrak
2015
Our work contributes to the time series community by introducing novel approaches to pattern recognition in temporal data, presenting miscellaneous sensor fusion techniques for multivariate measurements  ...  This thesis is concerned with time series mining, including segmentation, classification, and clustering of temporal data.  ...  In general, we consider time series analysis in the context of data mining, machine learning, and pattern recognition, wherefore time series forecasting/prediction and signal estimation are out of scope  ... 
doi:10.14279/depositonce-4619 fatcat:ntmivkietrfotmkaylwn54a2km

Predictive Analytics for Energy Efficiency and Energy Retailing

Konstantin Hopf
2019
(2010) and R. T. Watson, Boudreau, et al. (2010) , as well as in the field of energy informatics (Goebel et al. 2014; Gholami et al. 2016; Ketter et al. 2018) .  ...  Thereby, I investigate how IS can help to obtain new insights on energy consumers that can be  ...  In short, ML algorithms have to deal with challenging pattern recognition tasks and must be effective in their sense-making of the data to deliver compelling results.  ... 
doi:10.20378/irbo-54833 fatcat:6reszziekrgtpcuxwoceihds6y