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SnoReport 2.0: new features and a refined Support Vector Machine to improve snoRNA identification

João Victor de Araujo Oliveira, Fabrizio Costa, Rolf Backofen, Peter Florian Stadler, Maria Emília Machado Telles Walter, Jana Hertel
2016 BMC Bioinformatics  
Here, we present snoReport 2.0, which substantially improves and extends in the original method by: extracting new features for both box C/D and H/ACA box snoRNAs; developing a more sophisticated technique  ...  To validate the new version and to demonstrate its improved performance, we tested snoReport 2.0 in different organisms.  ...  , in order to build a feature vector, which will be the input for the Support Vector Machine (SVM).  ... 
doi:10.1186/s12859-016-1345-6 pmid:28105919 pmcid:PMC5249026 fatcat:ofuwwfxoqrcm7j3xw2fuipevty

Defining the role and mechanisms of miRNAs in cartilage ageing and disease

Panagiotis Balaskas
2021
Gain and loss of function approaches supported a role for miR-107 and miR-143-3p in important pathways relevant to OA. qPCR analysis suggested the involvement of miR-107 in the Wingless-Related Integration  ...  This thesis identified a large set of DE miRNAs involved in cartilage biology and OA and supported the implication of specific miRNAs in important cellular mechanisms that regulate chondrocyte proliferation  ...  Acknowledgements Acknowledgments: We would like to thank Catarina Castanheira for her assistance with the laboratory work.  ... 
doi:10.17638/03139415 fatcat:grly6xpwnfhbfmuw7yknwlrami

RNA secondary structure thermodynamics and kinetics

Ronny Lorenz
2014 unpublished
By exploration of variable and static energy landscapes with stochastic and deterministic techniques the new approaches can even be used to predict folding kinetics during transcription.  ...  This sets the scene to investigate the folding and refolding dynamics of RNA molecules.  ...  We are grateful to all beta testers who were unrestrained in reporting bugs on preliminary versions of ViennaRNA Package 2.0.  ... 
doi:10.25365/thesis.34176 fatcat:xs3v7buvcjhwfnq3hgfrvqfrje