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Thuc D Le

Showing results (1-10 of 15) with videos related to

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BMC Bioinformatics|April 1, 2017
Identifying miRNA sponge modules using biclustering and regulatory scoresJunpeng Zhang, Thuc D Le, Lin Liu, et al.
Bioinformatics (Oxford, England)|March 7, 2021
NIBNA: a network-based node importance approach for identifying breast cancer driversMandar S Chaudhary, Vu V H Pham, Thuc D Le
BMC Bioinformatics|June 4, 2021
Uncovering the roles of microRNAs/lncRNAs in characterising breast cancer subtypes and prognosisXiaomei Li, Buu Truong, Taosheng Xu, et al.
Briefings in Functional Genomics|April 29, 2022
Identifying preeclampsia-associated genes using a control theory methodXiaomei Li, Lin Liu, Clare Whitehead, et al.
BMC Bioinformatics|March 19, 2013
Inferring microRNA and transcription factor regulatory networks in heterogeneous dataThuc D Le, Lin Liu, Bing Liu, et al.
Plos Computational Biology|August 25, 2020
A novel single-cell based method for breast cancer prognosisXiaomei Li, Lin Liu, Gregory J Goodall, et al.
Bioinformatics (Oxford, England)|December 31, 2020
DriverGroup: a novel method for identifying driver gene groupsVu V H Pham, Lin Liu, Cameron P Bracken, et al.
Briefings in Bioinformatics|May 22, 2021
The winning methods for predicting cellular position in the DREAM single-cell transcriptomics challengeVu V H Pham, Xiaomei Li, Buu Truong, et al.
Bioinformatics (Oxford, England)|April 27, 2021
pDriver: a novel method for unravelling personalized coding and miRNA cancer driversVu V H Pham, Lin Liu, Cameron P Bracken, et al.
Plos Computational Biology|December 3, 2019
CBNA: A control theory based method for identifying coding and non-coding cancer driversVu V H Pham, Lin Liu, Cameron P Bracken, et al.
Pageof 2

Showing results (1-10 of 15) with videos related to

Sort By:
Pageof 2
BMC Bioinformatics|April 1, 2017
Identifying miRNA sponge modules using biclustering and regulatory scoresJunpeng Zhang, Thuc D Le, Lin Liu, et al.
Bioinformatics (Oxford, England)|March 7, 2021
NIBNA: a network-based node importance approach for identifying breast cancer driversMandar S Chaudhary, Vu V H Pham, Thuc D Le
BMC Bioinformatics|June 4, 2021
Uncovering the roles of microRNAs/lncRNAs in characterising breast cancer subtypes and prognosisXiaomei Li, Buu Truong, Taosheng Xu, et al.
Briefings in Functional Genomics|April 29, 2022
Identifying preeclampsia-associated genes using a control theory methodXiaomei Li, Lin Liu, Clare Whitehead, et al.
BMC Bioinformatics|March 19, 2013
Inferring microRNA and transcription factor regulatory networks in heterogeneous dataThuc D Le, Lin Liu, Bing Liu, et al.
Plos Computational Biology|August 25, 2020
A novel single-cell based method for breast cancer prognosisXiaomei Li, Lin Liu, Gregory J Goodall, et al.
Bioinformatics (Oxford, England)|December 31, 2020
DriverGroup: a novel method for identifying driver gene groupsVu V H Pham, Lin Liu, Cameron P Bracken, et al.
Briefings in Bioinformatics|May 22, 2021
The winning methods for predicting cellular position in the DREAM single-cell transcriptomics challengeVu V H Pham, Xiaomei Li, Buu Truong, et al.
Bioinformatics (Oxford, England)|April 27, 2021
pDriver: a novel method for unravelling personalized coding and miRNA cancer driversVu V H Pham, Lin Liu, Cameron P Bracken, et al.
Plos Computational Biology|December 3, 2019
CBNA: A control theory based method for identifying coding and non-coding cancer driversVu V H Pham, Lin Liu, Cameron P Bracken, et al.
Pageof 2