N
Ning Lan
Researcher at Yale University
Publications - 14
Citations - 5186
Ning Lan is an academic researcher from Yale University. The author has contributed to research in topics: Gene & Proteome. The author has an hindex of 11, co-authored 13 publications receiving 4896 citations. Previous affiliations of Ning Lan include Rutgers University.
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Journal ArticleDOI
Functional profiling of the Saccharomyces cerevisiae genome.
Guri Giaever,Angela M. Chu,Li Ni,Carla Connelly,Linda Riles,Steeve Veronneau,Sally Dow,Ankuta Lucau-Danila,Keith Anderson,Bruno André,Adam P. Arkin,Anna Astromoff,Mohamed El Bakkoury,Rhonda Bangham,Rocío Benito,Sophie Brachat,Stefano Campanaro,Matt Curtiss,Karen Davis,Adam M. Deutschbauer,K. D. Entian,Patrick Flaherty,Françoise Foury,David J. Garfinkel,Mark Gerstein,Deanna Gotte,Ulrich Güldener,Johannes H. Hegemann,Svenja Hempel,Zelek S. Herman,Daniel F. Jaramillo,Diane E. Kelly,Steven L. Kelly,Peter Kötter,Darlene LaBonte,David C. Lamb,Ning Lan,Hong Liang,Hong Liao,Lucy Y. Liu,Chuanyun Luo,Marc Lussier,Rong Mao,Patrice Menard,Siew Loon Ooi,José L. Revuelta,Christopher J. Roberts,Matthias Rose,Petra Ross-Macdonald,Bart Scherens,Greg Schimmack,Brenda Shafer,Daniel D. Shoemaker,Sharon Sookhai-Mahadeo,Reginald Storms,Jeffrey N. Strathern,Giorgio Valle,Marleen Voet,Guido Volckaert,Ching Yun Wang,Teresa R. Ward,Julie Wilhelmy,Elizabeth A. Winzeler,Yonghong Yang,Grace Yen,Elaine M. Youngman,Kexin Yu,Howard Bussey,Jef D. Boeke,Michael Snyder,Peter Philippsen,Ronald W. Davis,Mark Johnston +72 more
TL;DR: It is shown that previously known and new genes are necessary for optimal growth under six well-studied conditions: high salt, sorbitol, galactose, pH 8, minimal medium and nystatin treatment, and less than 7% of genes that exhibit a significant increase in messenger RNA expression are also required for optimal Growth in four of the tested conditions.
Journal ArticleDOI
Mining the Structural Genomics Pipeline: Identification of Protein Properties that Affect High-throughput Experimental Analysis
Chern Sing Goh,Ning Lan,Ning Lan,Shawn M. Douglas,Shawn M. Douglas,Baolin Wu,Nathaniel Echols,Nathaniel Echols,Andrew Marcus Smith,Andrew Marcus Smith,Duncan Milburn,Duncan Milburn,Gaetano T. Montelione,Hongyu Zhao,Mark Gerstein,Mark Gerstein +15 more
TL;DR: This work uses tree-based analyses and random forest algorithms to discover the most significant protein features that influence a protein's amenability to high-throughput experimentation and identifies combinations of features that best differentiate the small group of proteins for which a structure has been determined from all the currently selected targets.
Journal ArticleDOI
SPINE: an integrated tracking database and data mining approach for identifying feasible targets in high-throughput structural proteomics
Paul Bertone,Yuval Kluger,Ning Lan,Deyou Zheng,Dinesh Christendat,Adelinda Yee,Aled M. Edwards,Cheryl H. Arrowsmith,Gaetano T. Montelione,Mark Gerstein +9 more
TL;DR: This work developed a comprehensive set of data mining features for each protein, including several related to experimental progress and demonstrated in detail the application of a particular machine learning approach, decision trees, to the tasks of predicting a protein's solubility and propensity to crystallize based on sequence features.
Journal ArticleDOI
An integrated approach for finding overlooked genes in yeast
Anuj Kumar,Paul M. Harrison,Kei-Hoi Cheung,Ning Lan,Nathaniel Echols,Paul Bertone,Perry L. Miller,Mark Gerstein,Michael Snyder +8 more
TL;DR: The discovery of 137 previously unappreciated genes in yeast through a widely applicable and highly scalable approach integrating methods of gene-trapping, microarray-based expression analysis, and genome-wide homology searching, which provides an effective supplement to current gene-finding schemes.
Journal ArticleDOI
Integration of genomic datasets to predict protein complexes in yeast
TL;DR: This paper focuses on the prediction of membership in protein complexes for individual genes, and recruits six different data sources that include expression profiles, interaction data, essentiality and localization information, which can be improved by combining all of them.