TY - GEN
T1 - An integrative genomic study for multimodal genomic data using multi-block bipartite graph
AU - Kang, Mingon
AU - Park, Juyoung
AU - Kim, Dong Chul
AU - Biswas, Ashis K.
AU - Liu, Chunyu
AU - Gao, Jean
N1 - Publisher Copyright: © 2015 IEEE.
PY - 2015/12/16
Y1 - 2015/12/16
N2 - Human diseases involve a sequence of complex interactions in multiple biological processes. In particular, multiple genomic data such as Single Nucleotide Polymorphism (SNP), Copy Number Variation (CNV), and DNA Methylation (DM) and their interactions simultaneously play an important role in the variation of mRNA transcription in human diseases. However, despite of the widely known complex multi-layer biological processes and increased availability of the heterogeneous genomic data, most research has considered only a single type of the genomic data. Furthermore, recent integrative genomic studies for the multiple genomic data have also been facing difficulties due to the high-dimensionality and complexity, especially when considering their intra-and inter-block interactions. In this paper, we introduce a novel multi-block bipartite graph and its inference methods, MB2I and sMB2I, for the integrative genomic study. The proposed methods not only integrate the multiple genomic data but also incorporate their intra/inter-block interactions by using a multi-block bipartite graph. In addition, the methods can be used to predict quantitative traits (e.g. gene expression, survival time) from the multi-block genomic data. The outstanding performance was assessed by simulation experiments that implement practical situations.
AB - Human diseases involve a sequence of complex interactions in multiple biological processes. In particular, multiple genomic data such as Single Nucleotide Polymorphism (SNP), Copy Number Variation (CNV), and DNA Methylation (DM) and their interactions simultaneously play an important role in the variation of mRNA transcription in human diseases. However, despite of the widely known complex multi-layer biological processes and increased availability of the heterogeneous genomic data, most research has considered only a single type of the genomic data. Furthermore, recent integrative genomic studies for the multiple genomic data have also been facing difficulties due to the high-dimensionality and complexity, especially when considering their intra-and inter-block interactions. In this paper, we introduce a novel multi-block bipartite graph and its inference methods, MB2I and sMB2I, for the integrative genomic study. The proposed methods not only integrate the multiple genomic data but also incorporate their intra/inter-block interactions by using a multi-block bipartite graph. In addition, the methods can be used to predict quantitative traits (e.g. gene expression, survival time) from the multi-block genomic data. The outstanding performance was assessed by simulation experiments that implement practical situations.
KW - integrative genomic study
KW - multi-block bipartite graph
KW - multimodal genomic data
UR - https://www.scopus.com/pages/publications/84962362317
U2 - 10.1109/BIBM.2015.7359744
DO - 10.1109/BIBM.2015.7359744
M3 - Conference contribution
T3 - Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
SP - 563
EP - 568
BT - Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
A2 - Schapranow, lng. Matthieu
A2 - Zhou, Jiayu
A2 - Hu, Xiaohua Tony
A2 - Ma, Bin
A2 - Rajasekaran, Sanguthevar
A2 - Miyano, Satoru
A2 - Yoo, Illhoi
A2 - Pierce, Brian
A2 - Shehu, Amarda
A2 - Gombar, Vijay K.
A2 - Chen, Brian
A2 - Pai, Vinay
A2 - Huan, Jun
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
Y2 - 9 November 2015 through 12 November 2015
ER -