M
Mingzhou Song
Researcher at New Mexico State University
Publications - 61
Citations - 3311
Mingzhou Song is an academic researcher from New Mexico State University. The author has contributed to research in topics: Gene regulatory network & Cluster analysis. The author has an hindex of 19, co-authored 61 publications receiving 2866 citations. Previous affiliations of Mingzhou Song include University of Washington & Queens College.
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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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Ckmeans.1d.dp: Optimal k-means Clustering in One Dimension by Dynamic Programming
Haizhou Wang,Mingzhou Song +1 more
TL;DR: In this paper, a dynamic programming algorithm for optimal one-dimensional clustering is proposed, which is implemented as an R package called Ckmeans.1d.dp.
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Inferring causal molecular networks: empirical assessment through a community-based effort
Steven M. Hill,Laura M. Heiser,Thomas Cokelaer,Michael Unger,Nicole K. Nesser,Daniel E. Carlin,Yang Zhang,Artem Sokolov,Evan O. Paull,Christopher K. Wong,Kiley Graim,Adrian Bivol,Haizhou Wang,Zhu Fan,Bahman Afsari,Ludmila Danilova,Alexander V. Favorov,Wai Shing Lee,Dane Taylor,Chenyue W. Hu,Byron L. Long,David P. Noren,Alex Bisberg,Gordon B. Mills,Joe W. Gray,Michael R. Kellen,Thea Norman,Stephen H. Friend,Amina A. Qutub,Elana J. Fertig,Yuanfang Guan,Mingzhou Song,Joshua M. Stuart,Paul T. Spellman,Heinz Koeppl,Gustavo Stolovitzky,Julio Saez-Rodriguez,Sach Mukherjee +37 more
TL;DR: The HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks, used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model to score networks.
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A comprehensive meta QTL analysis for fiber quality, yield, yield related and morphological traits, drought tolerance, and disease resistance in tetraploid cotton.
TL;DR: This study combines two previous meta-analysis studies and adds all other currently available QTL studies, making it the most comprehensiveMeta-analysis study in cotton to date.
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A comparative meta-analysis of QTL between intraspecific Gossypium hirsutum and interspecific G. hirsutum × G. barbadense populations.
Joseph I. Said,Mingzhou Song,Hantao Wang,Zhongxu Lin,Xianlong Zhang,David D. Fang,Jinfa Zhang +6 more
TL;DR: There are some universal QTL regions in the cultivated tetraploid cotton which remain consistent and some regions which differ between population types, elucidates the similarities and differences in QTL clusters and hotspots between intraspecific and interspecific populations.