B
Baolin Wu
Researcher at Harbin Institute of Technology
Publications - 192
Citations - 5201
Baolin Wu is an academic researcher from Harbin Institute of Technology. The author has contributed to research in topics: Attitude control & Genome-wide association study. The author has an hindex of 31, co-authored 167 publications receiving 4230 citations. Previous affiliations of Baolin Wu include Dalian University of Technology & University of Minnesota.
Papers
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Journal ArticleDOI
Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data
Baolin Wu,Thomas Abbott,David A. Fishman,Walter J. McMurray,Gil Mor,Kathryn L. Stone,David C. Ward,Kenneth R. Williams,Hongyu Zhao +8 more
TL;DR: This work compares the performance of several classes of statistical methods for the classification of cancer based on MS spectra and finds that RF outperforms other methods in the analysis of MS data.
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Cerebral malaria in children is associated with long-term cognitive impairment
Chandy C. John,Paul Bangirana,Justus Byarugaba,Robert O. Opoka,Richard Idro,Anne M. Jurek,Baolin Wu,Michael J. Boivin +7 more
TL;DR: Cerebral malaria is associated with long-term cognitive impairments in 1 of 4 child survivors and future studies should investigate the mechanisms involved so as to develop interventions aimed at prevention and rehabilitation.
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Malignant transformation initiated by Mll-AF9: gene dosage and critical target cells.
Weili Chen,Ashish R. Kumar,Wendy A. Hudson,Quanzhi Li,Baolin Wu,Rodney Staggs,Erik Lund,Thien N. Sam,John H. Kersey +8 more
TL;DR: Mll-AF9, when under endogenous regulatory control, efficiently transformed LSK (Lin(-)Sca1(+)c-kit(+)) stem cells, while committed granulocyte-monocyte progenitors (GMPs) were transformation resistant and did not cause leukemia.
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Information assessment on predicting protein-protein interactions
TL;DR: This analysis shows that the MIPS and Gene Ontology functional similarity datasets as the dominating information contributors for predicting the protein-protein interactions under the framework proposed by Jansen et al. can give highly accurate classifications.
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A statistical method for identifying differential gene--gene co-expression patterns
TL;DR: It is shown that genes associated with cancer may have differential gene-gene expression patterns with many other genes in different cell states, by discovering such patterns, and may be able to identify carcinogenesis related genes.