Research – Kuang Lab

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.

7 entries « ‹ 1 of 2 › »

Broadbent, Charles; Song, Tianci; Kuang, Rui

Deciphering High-order Structures in Spatial Transcriptomes with Graph-guided Tucker Decomposition Best Paper Journal Article

In: Bioinformatics (Supplement Issue of the Proceedings of 32nd International Conference on Intelligent Systems for Molecular Biology (ISMB))↩, vol. 40, no. Supplement_1, pp. i529-i538, 2024.

Links | BibTeX

Song, Tianci; Broadbent, Charles; Kuang, Rui

GNTD: Reconstructing Spatial Transcriptomes with Graph-guided Neural Tensor Decomposition Informed by Spatial and Functional Relations Journal Article

In: Nature Communications, vol. 14, no. 8276, 2023.

Links | BibTeX

Atkins, Thomas Karl; Song, Tianci; Kuang, Rui

FIST-nD: A tool for n-dimensional spatial transcriptomics data imputation via graph-regularized tensor completio Technical Report

2022.

Links | BibTeX

Song, Tianci; Markham, Kathleen K.; Li, Zhuliu; Muller, Kristen E.; Greenham, Kathleen; Kuang, Rui

Detecting Spatially Co-expressed Gene Clusters with Functional Coherence by Graph-regularized Convolutional Neural Network Journal Article

In: Bioinformatics, vol. 38, no. 5, pp. 1344–1352, 2022.

Links | BibTeX

Li, Zhuliu; Song, Tianci; Yong, Jeongsik; Kuang, Rui

Imputation of spatially-resolved transcriptomes by graph-regularized tensor completion Journal Article

In: PLoS computational biology, vol. 17, no. 4, pp. e1008218, 2021.

Links | BibTeX

7 entries « ‹ 1 of 2 › »

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.

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Phenome-genome association analysis: Development of graph-based learning algorithms for analyzing disease and gene associations in a network context.

11 entries « ‹ 1 of 3 › »

Broadbent, Charles; Song, Tianci; Kuang, Rui

Deciphering High-order Structures in Spatial Transcriptomes with Graph-guided Tucker Decomposition Best Paper Journal Article

In: Bioinformatics (Supplement Issue of the Proceedings of 32nd International Conference on Intelligent Systems for Molecular Biology (ISMB))↩, vol. 40, no. Supplement_1, pp. i529-i538, 2024.

Links | BibTeX

Li, Zhuliu; Petegrosso, Raphael; Smith, Shaden; Sterling, David; Karypis, George; Kuang, Rui

Scalable Label Propagation for Multi-relational Learning on the Tensor Product of Graphs Journal Article

In: IEEE Transactions on Knowledge and Data Engineering, 2021.

Links | BibTeX

Petegrosso, Raphael; Song, Tianci; Kuang, Rui

Hierarchical Canonical Correlation Analysis Reveals Phenotype, Genotype, and Geoclimate Associations in Plants Journal Article

In: Plant Phenomics, vol. 2020, no. 1969142, 2020.

Links | BibTeX

Li, Zhuliu; Zhang, Wei; Huang, R Stephanie; Kuang, Rui

Learning a Low-rank Tensor of Pharmacogenomic Multi-relations from Biomedical Networks Proceedings

IEEE International Conference on Data Mining 2019.

Abstract | Links | BibTeX

@proceedings{GTCORP2019b,

title = {Learning a Low-rank Tensor of Pharmacogenomic Multi-relations from Biomedical Networks},

author = {Zhuliu Li and Wei Zhang and R Stephanie Huang and Rui Kuang},

url = {http://compbio.cs.umn.edu/08970888.pdf},

year  = {2019},

date = {2019-08-31},

organization = {IEEE International Conference on Data Mining},

abstract = {Learning pharmacogenomic multi-relations among diseases, genes and chemicals from content-rich biomedical and biological networks can provide important guidance for drug discovery, drug repositioning and disease treatment. Most of the existing methods focus on imputing missing values in the diseasegene, disease-chemical and gene-chemical pairwise relations from the observed relations instead of being designed for learning high-order disease-gene-chemical multi-relations. To achieve the goal, we propose a general tensor-based optimization framework and a scalable Graph-Regularized Tensor Completion from Observed Pairwise Relations (GT-COPR) algorithm to infer the multi-relations among the entities across multiple networks in a low-rank tensor, based on manifold regularization with the graph Laplacian of a Cartesian, tensor or strong product of the networks, and consistencies between the collapsed tensors and the observed bipartite relations. Our theoretical analyses also prove the convergence and efficiency of GT-COPR. In the experiments, the tensor fiber-wise and slice-wise evaluations demonstrate the accuracy of GT-COPR for predicting the diseasegene-chemical associations across the large-scale protein-protein interactions network, chemical structural similarity network and phenotype-based human disease network; and the validation on Genomics of Drug Sensitivity in Cancer cell line dataset shows a potential clinical application of GT-COPR for learning diseasespecific chemical-gene interactions. Statistical enrichment analysis demonstrates that GT-COPR is also capable of producing both topologically and biologically relevant disease, gene and chemical components with high significance.

Source code: https://github.com/kuanglab/GT-COPR},

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Zhang, Wei; Chien, Jeremy; Yong, Jeongsik; Kuang, Rui

Network-based Machine Learning and Graph Theory Algorithms for Precision Oncology Journal Article

In: NPJ Precision Oncology, no. 25, 2017.

Abstract | Links | BibTeX

11 entries « ‹ 1 of 3 › »

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 « ‹ 1 of 3 › »

Petegrosso, Raphael; Li, Zhuliu; Srour, Molly A.; Saad, Yousef; Zhang, Wei; Kuang, Rui

Scalable Remote Homology Detection and Fold Recognition in Massive Protein Networks Journal Article

In: PROTEINS: Structure, Function, and Bioinformatics, vol. 87, no. 6, pp. 478-491, 2019.

Abstract | Links | BibTeX

@article{scalable2019petegrosso,

title = {Scalable Remote Homology Detection and Fold Recognition in Massive Protein Networks},

author = {Raphael Petegrosso and Zhuliu Li and Molly A. Srour and Yousef Saad and Wei Zhang and Rui Kuang},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/prot.25669},

year  = {2019},

date = {2019-01-31},

journal = {PROTEINS: Structure, Function, and Bioinformatics},

volume = {87},

number = {6},

pages = {478-491},

abstract = {The global connectivities in very large protein similarity networks contain traces of evolution among the proteins for detecting protein remote evolutionary relations or structural similarities. To investigate how well a protein network captures the evolutionary information, a key limitation is the intensive computation of pairwise sequence similarities needed to construct very large protein networks. In this paper, we introduce Label Propagation on Low-rank Kernel Approximation (LP-LOKA) for searching massively large protein networks. LP-LOKA propagates initial protein similarities in a low-rank graph by Nystrom approximation without computing all pairwise similarities. With scalable parallel implementations based on distributed-memory using message-passing interface and Apache-Hadoop/Spark on cloud, LP-LOKA can search protein networks with one million proteins or more. In the experiments on Swiss-Prot/ADDA/CASP data, LP-LOKA significantly improved protein ranking over the widely used HMM-HMM or profile-sequence alignment methods utilizing large protein networks. It was observed that the larger the protein similarity network, the better the performance, especially on relatively small protein superfamilies and folds. The results suggest that computing massively large protein network is necessary to meet the growing need of annotating proteins from newly sequenced species and LP-LOKA is both scalable and accurate for searching massively large protein networks.},

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Min, Martin Renqiang; Kuang, Rui; Bonner, Anthony J; Zhang, Zhaolei

Learning Random-Walk Kernels for Protein Remote Homology Identification and Motif Discovery. Proceedings Article

In: SDM, pp. 133–144, SIAM 2009, ISBN: 978-0-89871-682-5.

Abstract | Links | BibTeX

Ngo, Thanh; Kuang, Rui

Partial profile alignment kernels for protein classification Proceedings Article

In: 2009 IEEE International Workshop on Genomic Signal Processing and Statistics, pp. 1–4, IEEE 2009, ISBN: 978-1-4244-4761-9.

Abstract | Links | BibTeX

Kuang, Rui; Gu, Jianying; Cai, Hong; Wang, Yufeng

Improved prediction of malaria degradomes by supervised learning with SVM and profile kernel Journal Article

In: Genetica, vol. 136, no. 1, pp. 189–209, 2008.

Abstract | Links | BibTeX

Melvin, Iain; Ie, Eugene; Kuang, Rui; Weston, Jason; Noble, William Stafford; Leslie, Christina

SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition Journal Article

In: BMC bioinformatics, vol. 8, no. 4, 2007.

Abstract | Links | BibTeX

@article{melvin2007svm,

title = {SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition},

author = {Iain Melvin and Eugene Ie and Rui Kuang and Jason Weston and William Stafford Noble and Christina Leslie},

url = {http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-8-S4-S2},

doi = {10.1186/1471-2105-8-S4-S2},

year  = {2007},

date = {2007-05-22},

journal = {BMC bioinformatics},

volume = {8},

number = {4},

publisher = {BioMed Central},

abstract = {Background 

Predicting a protein's structural class from its amino acid sequence is a fundamental problem in computational biology. Much recent work has focused on developing new representations for protein sequences, called string kernels, for use with support vector machine (SVM) classifiers. However, while some of these approaches exhibit state-of-the-art performance at the binary protein classification problem, i.e. discriminating between a particular protein class and all other classes, few of these studies have addressed the real problem of multi-class superfamily or fold recognition. Moreover, there are only limited software tools and systems for SVM-based protein classification available to the bioinformatics community. 

Results 

We present a new multi-class SVM-based protein fold and superfamily recognition system and web server called SVM-Fold, which can be found at http://svm-fold.c2b2.columbia.edu. Our system uses an efficient implementation of a state-of-the-art string kernel for sequence profiles, called the profile kernel, where the underlying feature representation is a histogram of inexact matching k-mer frequencies. We also employ a novel machine learning approach to solve the difficult multi-class problem of classifying a sequence of amino acids into one of many known protein structural classes. Binary one-vs-the-rest SVM classifiers that are trained to recognize individual structural classes yield prediction scores that are not comparable, so that standard "one-vs-all" classification fails to perform well. Moreover, SVMs for classes at different levels of the protein structural hierarchy may make useful predictions, but one-vs-all does not try to combine these multiple predictions. To deal with these problems, our method learns relative weights between one-vs-the-rest classifiers and encodes information about the protein structural hierarchy for multi-class prediction. In large-scale benchmark results based on the SCOP database, our code weighting approach significantly improves on the standard one-vs-all method for both the superfamily and fold prediction in the remote homology setting and on the fold recognition problem. Moreover, our code weight learning algorithm strongly outperforms nearest-neighbor methods based on PSI-BLAST in terms of prediction accuracy on every structure classification problem we consider. 

Conclusion 

By combining state-of-the-art SVM kernel methods with a novel multi-class algorithm, the SVM-Fold system delivers efficient and accurate protein fold and superfamily recognition.},

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pubstate = {published},

tppubtype = {article}

}

Background
Predicting a protein's structural class from its amino acid sequence is a fundamental problem in computational biology. Much recent work has focused on developing new representations for protein sequences, called string kernels, for use with support vector machine (SVM) classifiers. However, while some of these approaches exhibit state-of-the-art performance at the binary protein classification problem, i.e. discriminating between a particular protein class and all other classes, few of these studies have addressed the real problem of multi-class superfamily or fold recognition. Moreover, there are only limited software tools and systems for SVM-based protein classification available to the bioinformatics community.
Results
We present a new multi-class SVM-based protein fold and superfamily recognition system and web server called SVM-Fold, which can be found at http://svm-fold.c2b2.columbia.edu. Our system uses an efficient implementation of a state-of-the-art string kernel for sequence profiles, called the profile kernel, where the underlying feature representation is a histogram of inexact matching k-mer frequencies. We also employ a novel machine learning approach to solve the difficult multi-class problem of classifying a sequence of amino acids into one of many known protein structural classes. Binary one-vs-the-rest SVM classifiers that are trained to recognize individual structural classes yield prediction scores that are not comparable, so that standard "one-vs-all" classification fails to perform well. Moreover, SVMs for classes at different levels of the protein structural hierarchy may make useful predictions, but one-vs-all does not try to combine these multiple predictions. To deal with these problems, our method learns relative weights between one-vs-the-rest classifiers and encodes information about the protein structural hierarchy for multi-class prediction. In large-scale benchmark results based on the SCOP database, our code weighting approach significantly improves on the standard one-vs-all method for both the superfamily and fold prediction in the remote homology setting and on the fold recognition problem. Moreover, our code weight learning algorithm strongly outperforms nearest-neighbor methods based on PSI-BLAST in terms of prediction accuracy on every structure classification problem we consider.
Conclusion
By combining state-of-the-art SVM kernel methods with a novel multi-class algorithm, the SVM-Fold system delivers efficient and accurate protein fold and superfamily recognition.

13 entries « ‹ 1 of 3 › »