Our lab is particularly interested in large-scale genomic and biomedical data analysis with machine learning and network-based methods for research problems in health-related and biological science. The two broad areas for my research are 1) phenome-genome association analysis and 2) cancer outcome prediction and biomarker identification. In the first area, we performed large-scale association analysis between all genes and the complete collection of phenotypes (phenome) by network-based machine learning methods. In the second area, we developed graph-based learning models and kernel methods to capture the structures in single-cell RNA sequencing data, high-dimensional gene (isoform) expressions and DNA copy number variations for improved cancer outcome prediction and robust biomarker identification. In addition, we also developed kernel methods for protein classification. Our current projects center around the following topics,

• Spatial and single-cell transcriptomics: Spatial transcriptomics technologies have enabled spatially-resolved RNA profiling of single cells with cell identities and localizations for understanding cells’ organizations and functions. Our group develops new machine learning methods for mining RNA profiles collected from single cells and their spatial locations.
  
  Sorry, no publications matched your criteria.

• Cancer genomics: Development of graph-based learning algorithms, sequence alignment algorithms and association rule-mining algorithms for building predictive models and mining biomarkers of cancer phenotypes from microarray or sequencing transcriptome data, DNA copy number variations, SNPs and protein-protein interactions.
  
  13 entries « ‹ 3 of 3 › »

  Hwang, TaeHyun; Tian, Ze; Kuang, Rui; Kocher, Jean-Pierre

  Learning on weighted hypergraphs to integrate protein interactions and gene expressions for cancer outcome prediction Proceedings Article

  In: 2008 Eighth IEEE International Conference on Data Mining, pp. 293–302, IEEE 2008, ISBN: 978-0-7695-3502-9.

  Abstract | Links | BibTeX

  @inproceedings{hwang2008learning,
  
  title = {Learning on weighted hypergraphs to integrate protein interactions and gene expressions for cancer outcome prediction},
  
  author = {TaeHyun Hwang and Ze Tian and Rui Kuang and Jean-Pierre Kocher},
  
  url = {http://compbio.cs.umn.edu/wp-content/uploads/2017/10/HyperGene.pdf},
  
  doi = {10.1109/ICDM.2008.37},
  
  isbn = {978-0-7695-3502-9},
  
  year  = {2008},
  
  date = {2008-12-15},
  
  booktitle = {2008 Eighth IEEE International Conference on Data Mining},
  
  pages = {293--302},
  
  organization = {IEEE},
  
  abstract = {Abstract: 
  
  Building reliable predictive models from multiple complementary genomic data for cancer study is a crucial step towards successful cancer treatment and a full understanding of the underlying biological principles. To tackle this challenging data integration problem, we propose a hypergraph-based learning algorithm called HyperGene to integrate microarray gene expressions and protein-protein interactions for cancer outcome prediction and biomarker identification. HyperGene is a robust two-step iterative method that alternatively finds the optimal outcome prediction and the optimal weighting of the marker genes guided by a protein-protein interaction network. Under the hypothesis that cancer-related genes tend to interact with each other, the HyperGene algorithm uses a protein-protein interaction network as prior knowledge by imposing a consistent weighting of interacting genes. Our experimental results on two large-scale breast cancer gene expression datasets show that HyperGene utilizing a curated protein-protein interaction network achieves significantly improved cancer outcome prediction. Moreover, HyperGene can also retrieve many known cancer genes as highly weighted marker genes.},
  
  keywords = {},
  
  pubstate = {published},
  
  tppubtype = {inproceedings}
  
  }
  
  

  Close

  Abstract:
  Building reliable predictive models from multiple complementary genomic data for cancer study is a crucial step towards successful cancer treatment and a full understanding of the underlying biological principles. To tackle this challenging data integration problem, we propose a hypergraph-based learning algorithm called HyperGene to integrate microarray gene expressions and protein-protein interactions for cancer outcome prediction and biomarker identification. HyperGene is a robust two-step iterative method that alternatively finds the optimal outcome prediction and the optimal weighting of the marker genes guided by a protein-protein interaction network. Under the hypothesis that cancer-related genes tend to interact with each other, the HyperGene algorithm uses a protein-protein interaction network as prior knowledge by imposing a consistent weighting of interacting genes. Our experimental results on two large-scale breast cancer gene expression datasets show that HyperGene utilizing a curated protein-protein interaction network achieves significantly improved cancer outcome prediction. Moreover, HyperGene can also retrieve many known cancer genes as highly weighted marker genes.

  Close

  • http://compbio.cs.umn.edu/wp-content/uploads/2017/10/HyperGene.pdf
  • doi:10.1109/ICDM.2008.37

  Close

  Hwang, TaeHyun; Kuang, Rui

  A Comparative Study of Breast Cancer Microarray Gene Expression Profiles using Label Propagation Proceedings Article

  In: Proceedings of the Workshop on Data Mining for Biomedical Informatics, held in conjunction with SIAM International Conference on Data Mining (SDM), 2008.

  Abstract | Links | BibTeX

  @inproceedings{hwang2008comparative,
  
  title = {A Comparative Study of Breast Cancer Microarray Gene Expression Profiles using Label Propagation},
  
  author = {TaeHyun Hwang and Rui Kuang},
  
  url = {http://compbio.cs.umn.edu/wp-content/uploads/2017/10/SDM2008.pdf},
  
  year  = {2008},
  
  date = {2008-04-24},
  
  booktitle = {Proceedings of the Workshop on Data Mining for Biomedical Informatics, held in conjunction with SIAM International Conference on Data Mining (SDM)},
  
  abstract = {A challenge in using microarray gene expression profiles to study breast cancer is to analyze the inconsistent discover-ies made from independent microarray datasets. The in-consistency is often related to the tuning of those sophis-ticated strategies needed for taking into account the depen-dence among the genes in the analysis as well as the differ-ence between the platforms and the protocols used for gen-erating the datasets. In this paper, we use a simple graph la-beling algorithm which can capture the dependency among the genes to study breast cancer microarry data. We per-form a comparative study of breast cancer metastasis on two datasets using the graph labeling algorithm and the standard statistics of correlation coefficients. We show that our algo-rithm predicts more consistent marker genes and pathways enriched by the marker genes on the two datasets than the correlation-coefficient statistics.},
  
  keywords = {},
  
  pubstate = {published},
  
  tppubtype = {inproceedings}
  
  }
  
  

  Close

  A challenge in using microarray gene expression profiles to study breast cancer is to analyze the inconsistent discover-ies made from independent microarray datasets. The in-consistency is often related to the tuning of those sophis-ticated strategies needed for taking into account the depen-dence among the genes in the analysis as well as the differ-ence between the platforms and the protocols used for gen-erating the datasets. In this paper, we use a simple graph la-beling algorithm which can capture the dependency among the genes to study breast cancer microarry data. We per-form a comparative study of breast cancer metastasis on two datasets using the graph labeling algorithm and the standard statistics of correlation coefficients. We show that our algo-rithm predicts more consistent marker genes and pathways enriched by the marker genes on the two datasets than the correlation-coefficient statistics.

  Close

  • http://compbio.cs.umn.edu/wp-content/uploads/2017/10/SDM2008.pdf

  Close

  Hwang, TaeHyun; Sicotte, Hugues; Tian, Ze; Wu, Baolin; Kocher, Jean-Pierre; Wigle, Dennis A; Kumar, Vipin; Kuang, Rui

  Robust and efficient identification of biomarkers by classifying features on graphs Journal Article

  In: Bioinformatics, vol. 24, no. 18, pp. 2023–2029, 2008, ISBN: 1460-2059.

  Abstract | Links | BibTeX

  @article{hwang2008robustb,
  
  title = {Robust and efficient identification of biomarkers by classifying features on graphs},
  
  author = {Hwang, TaeHyun and Sicotte, Hugues and Tian, Ze and Wu, Baolin and Kocher, Jean-Pierre and Wigle, Dennis A and Kumar, Vipin and Kuang, Rui},
  
  url = {http://bioinformatics.oxfordjournals.org/content/24/18/2023.short},
  
  doi = {10.1093/bioinformatics/btn383},
  
  isbn = {1460-2059},
  
  year  = {2008},
  
  date = {2008-01-01},
  
  journal = {Bioinformatics},
  
  volume = {24},
  
  number = {18},
  
  pages = {2023--2029},
  
  publisher = {Oxford Univ Press},
  
  abstract = {Motivation: A central problem in biomarker discovery from large-scale gene expression or single nucleotide polymorphism (SNP) data is the computational challenge of taking into account the dependence among all the features. Methods that ignore the dependence usually identify non-reproducible biomarkers across independent datasets. We introduce a new graph-based semi-supervised feature classification algorithm to identify discriminative disease markers by learning on bipartite graphs. Our algorithm directly classifies the feature nodes in a bipartite graph as positive, negative or neutral with network propagation to capture the dependence among both samples and features (clinical and genetic variables) by exploring bi-cluster structures in a graph. Two features of our algorithm are: (1) our algorithm can find a global optimal labeling to capture the dependence among all the features and thus, generates highly reproducible results across independent microarray or other high-thoughput datasets, (2) our algorithm is capable of handling hundreds of thousands of features and thus, is particularly useful for biomarker identification from high-throughput gene expression and SNP data. In addition, although designed for classifying features, our algorithm can also simultaneously classify test samples for disease prognosis/diagnosis. 
  
   
  
  Results: We applied the network propagation algorithm to study three large-scale breast cancer datasets. Our algorithm achieved competitive classification performance compared with SVMs and other baseline methods, and identified several markers with clinical or biological relevance with the disease. More importantly, our algorithm also identified highly reproducible marker genes and enriched functions from the independent datasets. 
  
   
  
  Availability: Supplementary results and source code are available at http://localhost/~raphaelpetegrosso/wpcb/Feature_Class.},
  
  keywords = {},
  
  pubstate = {published},
  
  tppubtype = {article}
  
  }
  
  

  Close

  Motivation: A central problem in biomarker discovery from large-scale gene expression or single nucleotide polymorphism (SNP) data is the computational challenge of taking into account the dependence among all the features. Methods that ignore the dependence usually identify non-reproducible biomarkers across independent datasets. We introduce a new graph-based semi-supervised feature classification algorithm to identify discriminative disease markers by learning on bipartite graphs. Our algorithm directly classifies the feature nodes in a bipartite graph as positive, negative or neutral with network propagation to capture the dependence among both samples and features (clinical and genetic variables) by exploring bi-cluster structures in a graph. Two features of our algorithm are: (1) our algorithm can find a global optimal labeling to capture the dependence among all the features and thus, generates highly reproducible results across independent microarray or other high-thoughput datasets, (2) our algorithm is capable of handling hundreds of thousands of features and thus, is particularly useful for biomarker identification from high-throughput gene expression and SNP data. In addition, although designed for classifying features, our algorithm can also simultaneously classify test samples for disease prognosis/diagnosis.
  
  Results: We applied the network propagation algorithm to study three large-scale breast cancer datasets. Our algorithm achieved competitive classification performance compared with SVMs and other baseline methods, and identified several markers with clinical or biological relevance with the disease. More importantly, our algorithm also identified highly reproducible marker genes and enriched functions from the independent datasets.
  
  Availability: Supplementary results and source code are available at http://localhost/~raphaelpetegrosso/wpcb/Feature_Class.

  Close

  • http://bioinformatics.oxfordjournals.org/content/24/18/2023.short
  • doi:10.1093/bioinformatics/btn383

  Close

  13 entries « ‹ 3 of 3 › »
• Phenome-genome association analysis: Development of graph-based learning algorithms for analyzing disease and gene associations in a network context.
  
  Sorry, no publications matched your criteria.

• Protein remote homology detection: Development of string kernel algorithms and label propagation algorithms to infer the protein remote homologys and study their protein structures and functions.
  
  13 entries « ‹ 3 of 3 › »

  Kuang, Rui; Ie, Eugene; Wang, Ke; Wang, Kai; Siddiqi, Mahira; Freund, Yoav; Leslie, Christina

  Profile-based string kernels for remote homology detection and motif extraction Proceedings Article

  In: CSB 2004, IEEE, 2004, ISBN: 0-7695-2194-0.

  Abstract | Links | BibTeX

  @inproceedings{kuang2005profileb,
  
  title = {Profile-based string kernels for remote homology detection and motif extraction},
  
  author = {Rui Kuang and Eugene Ie and Ke Wang and Kai Wang and Mahira Siddiqi and Yoav Freund and Christina Leslie},
  
  url = {http://compbio.cs.umn.edu/paper/profile-kernel.pdf},
  
  doi = {10.1109/CSB.2004.1332428},
  
  isbn = {0-7695-2194-0},
  
  year  = {2004},
  
  date = {2004-08-19},
  
  booktitle = {CSB 2004},
  
  publisher = {IEEE},
  
  abstract = {We introduce novel profile-based string kernels for use with support vector machines (SVMs) for the problems of protein classification and remote homology detection. These kernels use probabilistic profiles, such as those produced by the PSI-BLAST algorithm, to define position-dependent mutation neighborhoods along protein sequences for inexact matching of k-length subsequences ("k-mers") in the data. By use of an efficient data structure, the kernels are fast to compute once the profiles have been obtained. For example, the time needed to run PSI-BLAST in order to build the profiles is significantly longer than both the kernel computation time and the SVM training time. We present remote homology detection experiments based on the SCOP database where we show that profile-based string kernels used with SVM classifiers strongly outperform all recently presented supervised SVM methods. We also show how we can use the learned SVM classifier to extract "discriminative sequence motifs" - short regions of the original profile that contribute almost all the weight of the SVM classification score - and show that these discriminative motifs correspond to meaningful structural features in the protein data. The use of PSI-BLAST profiles can be seen as a semi-supervised learning technique, since PSI-BLAST leverages unlabeled data from a large sequence database to build more informative profiles. Recently presented "cluster kernels " give general semi-supervised methods for improving SVM protein classification performance. We show that our profile kernel results are comparable to cluster kernels while providing much better scalability to large datasets.},
  
  keywords = {},
  
  pubstate = {published},
  
  tppubtype = {inproceedings}
  
  }
  
  

  Close

  We introduce novel profile-based string kernels for use with support vector machines (SVMs) for the problems of protein classification and remote homology detection. These kernels use probabilistic profiles, such as those produced by the PSI-BLAST algorithm, to define position-dependent mutation neighborhoods along protein sequences for inexact matching of k-length subsequences ("k-mers") in the data. By use of an efficient data structure, the kernels are fast to compute once the profiles have been obtained. For example, the time needed to run PSI-BLAST in order to build the profiles is significantly longer than both the kernel computation time and the SVM training time. We present remote homology detection experiments based on the SCOP database where we show that profile-based string kernels used with SVM classifiers strongly outperform all recently presented supervised SVM methods. We also show how we can use the learned SVM classifier to extract "discriminative sequence motifs" - short regions of the original profile that contribute almost all the weight of the SVM classification score - and show that these discriminative motifs correspond to meaningful structural features in the protein data. The use of PSI-BLAST profiles can be seen as a semi-supervised learning technique, since PSI-BLAST leverages unlabeled data from a large sequence database to build more informative profiles. Recently presented "cluster kernels " give general semi-supervised methods for improving SVM protein classification performance. We show that our profile kernel results are comparable to cluster kernels while providing much better scalability to large datasets.

  Close

  • http://compbio.cs.umn.edu/paper/profile-kernel.pdf
  • doi:10.1109/CSB.2004.1332428

  Close

  Leslie, Christina; Kuang, Rui; Eskin, Eleazar

  Inexact matching string kernels for protein classification Book

  MIT Press, Cambridge, MA, 2004, ISBN: 9780262256926.

  BibTeX

  @book{leslie2004inexact,
  
  title = {Inexact matching string kernels for protein classification},
  
  author = {Christina Leslie and Rui Kuang and Eleazar Eskin},
  
  isbn = {9780262256926},
  
  year  = {2004},
  
  date = {2004-01-01},
  
  journal = {Kernel Methods in Computational Biology},
  
  volume = {1},
  
  pages = {95--112},
  
  publisher = {MIT Press, Cambridge, MA},
  
  keywords = {},
  
  pubstate = {published},
  
  tppubtype = {book}
  
  }
  
  

  Close

  Leslie, Christina; Kuang, Rui

  Fast kernels for inexact string matching Proceedings Article

  In: 16th Annual Conference on Computational Learning Theory and 7th Kernel Workshop (COLT/Kernel), Springer, 2003, ISBN: 978-3-540-45167-9.

  Abstract | Links | BibTeX

  @inproceedings{leslie2003fast,
  
  title = {Fast kernels for inexact string matching},
  
  author = {Christina Leslie and Rui Kuang},
  
  url = {http://link.springer.com/content/pdf/10.1007%2F978-3-540-45167-9_10.pdf},
  
  doi = {10.1007/978-3-540-45167-9_10},
  
  isbn = {978-3-540-45167-9},
  
  year  = {2003},
  
  date = {2003-01-01},
  
  booktitle = {16th Annual Conference on Computational Learning Theory and 7th Kernel Workshop (COLT/Kernel)},
  
  volume = {2777},
  
  publisher = {Springer},
  
  abstract = {We introduce several new families of string kernels designed in particular for use with support vector machines (SVMs) for classification of protein sequence data. These kernels – restricted gappy kernels, substitution kernels, and wildcard kernels – are based on feature spaces indexed by k-length subsequences from the string alphabet Σ (or the alphabet augmented by a wildcard character), and hence they are related to the recently presented (k,m)-mismatch kernel and string kernels used in text classification. However, for all kernels we define here, the kernel value K(x,y) can be computed in O(cK(|x| + |y|)) time, where the constant cK depends on the parameters of the kernel but is independent of the size |Σ| of the alphabet. Thus the computation of these kernels is linear in the length of the sequences, like the mismatch kernel, but we improve upon the parameter-dependent constant cK=km+1|Σ|mcK=km+1|Σ|m of the mismatch kernel. We compute the kernels efficiently using a recursive function based on a trie data structure and relate our new kernels to the recently described transducer formalism. Finally, we report protein classification experiments on a benchmark SCOP dataset, where we show that our new faster kernels achieve SVM classification performance comparable to the mismatch kernel and the Fisher kernel derived from profile hidden Markov models.},
  
  keywords = {},
  
  pubstate = {published},
  
  tppubtype = {inproceedings}
  
  }
  
  

  Close

  We introduce several new families of string kernels designed in particular for use with support vector machines (SVMs) for classification of protein sequence data. These kernels – restricted gappy kernels, substitution kernels, and wildcard kernels – are based on feature spaces indexed by k-length subsequences from the string alphabet Σ (or the alphabet augmented by a wildcard character), and hence they are related to the recently presented (k,m)-mismatch kernel and string kernels used in text classification. However, for all kernels we define here, the kernel value K(x,y) can be computed in O(cK(|x| + |y|)) time, where the constant cK depends on the parameters of the kernel but is independent of the size |Σ| of the alphabet. Thus the computation of these kernels is linear in the length of the sequences, like the mismatch kernel, but we improve upon the parameter-dependent constant cK=km+1|Σ|mcK=km+1|Σ|m of the mismatch kernel. We compute the kernels efficiently using a recursive function based on a trie data structure and relate our new kernels to the recently described transducer formalism. Finally, we report protein classification experiments on a benchmark SCOP dataset, where we show that our new faster kernels achieve SVM classification performance comparable to the mismatch kernel and the Fisher kernel derived from profile hidden Markov models.

  Close

  • http://link.springer.com/content/pdf/10.1007%2F978-3-540-45167-9_10.pdf
  • doi:10.1007/978-3-540-45167-9_10

  Close

  13 entries « ‹ 3 of 3 › »