M
Martin Crane
Researcher at Dublin City University
Publications - 113
Citations - 2985
Martin Crane is an academic researcher from Dublin City University. The author has contributed to research in topics: Lifelog & Cryptocurrency. The author has an hindex of 19, co-authored 107 publications receiving 2655 citations. Previous affiliations of Martin Crane include Royal College of Surgeons in Ireland & Florida State University.
Papers
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Wisdom of crowds for robust gene network inference
Daniel Marbach,James C. Costello,Robert Küffner,Nicole M. Vega,Robert J. Prill,Diogo M. Camacho,Kyle R. Allison,Andrej Aderhold,Richard Bonneau,Yukun Chen,James J. Collins,Francesca Cordero,Martin Crane,Frank Dondelinger,Mathias Drton,Roberto Esposito,Rina Foygel,Alberto de la Fuente,Jan Gertheiss,Pierre Geurts,Alex Greenfield,Marco Grzegorczyk,Anne-Claire Haury,Benjamin Holmes,Torsten Hothorn,Dirk Husmeier,Vân Anh Huynh-Thu,Alexandre Irrthum,Manolis Kellis,Guy Karlebach,Sophie Lèbre,Vincenzo De Leo,Aviv Madar,Subramani Mani,Fantine Mordelet,Harry Ostrer,Zhengyu Ouyang,Ravi Pandya,Tobias Petri,Andrea Pinna,Christopher S. Poultney,Serena Rezny,Heather J. Ruskin,Yvan Saeys,Ron Shamir,Alina Sîrbu,Mingzhou Song,Nicola Soranzo,Alexander Statnikov,Gustavo Stolovitzky,Nicci Vega,Paola Vera-Licona,Jean-Philippe Vert,Alessia Visconti,Haizhou Wang,Louis Wehenkel,Lukas Windhager,Yang Zhang,Ralf Zimmer +58 more
TL;DR: A comprehensive blind assessment of over 30 network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae and in silico microarray data defines the performance, data requirements and inherent biases of different inference approaches, and provides guidelines for algorithm application and development.
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Techniques for clustering gene expression data
TL;DR: This review paper provides a framework for the evaluation of clustering in gene expression analyses and surveys state of the art applications which recognise these limitations and addresses them.
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Random matrix theory for portfolio optimization: a stability approach
TL;DR: RMT is applied to an empirically measured financial correlation matrix, C, and it is shown that this matrix contains a large amount of noise, and a technique of filtering C is proposed that has many advantages, from the stability point of view, over the existing method of cleaning.
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RNA-Seq vs dual- and single-channel microarray data: sensitivity analysis for differential expression and clustering.
TL;DR: Three alternative gene expression time series datasets for the Drosophila melanogaster embryo development are studied, in order to compare three measurement techniques: RNA-seq, single-channel and dual-channel microarrays.
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Time and scale Hurst exponent analysis for financial markets
TL;DR: The time–scale dependence of the referred measures demonstrates the relevance of entropy measures in distinguishing the several characteristics of market indices: “effects” include early awareness, patterns of evolution as well as comparative behaviour distinctions in emergent/established markets.