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Browsing by Author "Tang, Haixu"
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Item Identification of Glycopeptides with Multiple Hydroxylysine O-Glycosylation Sites by Tandem Mass Spectrometry(ACS, 2015-11) Zhang, Yanlin; Yu, Chuan-Yih; Song, Ehwang; Li, Shuai Cheng; Mechref, Yehia; Tang, Haixu; Liu, Xiaowen; Department of Biohealth Informatics, IU School of Informatics and ComputingGlycosylation is one of the most common post-translational modifications in proteins, existing in ∼50% of mammalian proteins. Several research groups have demonstrated that mass spectrometry is an efficient technique for glycopeptide identification; however, this problem is still challenging because of the enormous diversity of glycan structures and the microheterogeneity of glycans. In addition, a glycopeptide may contain multiple glycosylation sites, making the problem complex. Current software tools often fail to identify glycopeptides with multiple glycosylation sites, and hence we present GlycoMID, a graph-based spectral alignment algorithm that can identify glycopeptides with multiple hydroxylysine O-glycosylation sites by tandem mass spectra. GlycoMID was tested on mass spectrometry data sets of the bovine collagen α-(II) chain protein, and experimental results showed that it identified more glycopeptide-spectrum matches than other existing tools, including many glycopeptides with two glycosylation sites.Item MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification(Springer Nature, 2021-06-08) Wang, Tongxin; Shao, Wei; Huang, Zhi; Tang, Haixu; Zhang, Jie; Ding, Zhengming; Huang, Kun; Medicine, School of MedicineTo fully utilize the advances in omics technologies and achieve a more comprehensive understanding of human diseases, novel computational methods are required for integrative analysis of multiple types of omics data. Here, we present a novel multi-omics integrative method named Multi-Omics Graph cOnvolutional NETworks (MOGONET) for biomedical classification. MOGONET jointly explores omics-specific learning and cross-omics correlation learning for effective multi-omics data classification. We demonstrate that MOGONET outperforms other state-of-the-art supervised multi-omics integrative analysis approaches from different biomedical classification applications using mRNA expression data, DNA methylation data, and microRNA expression data. Furthermore, MOGONET can identify important biomarkers from different omics data types related to the investigated biomedical problems.