@article{Orchard,

title = {Orchard: Building large cancer phylogenies using stochastic combinatorial search},

author = {Ethan Kulman and Rui Kuang and Quaid Morris},

url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012653},

doi = {https://doi.org/10.1371/journal.pcbi.1012653},

year  = {2024},

date = {2024-12-30},

journal = {PLOS Computational Biology},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Predicting spatially resolved gene expression via tissue morphology using adaptive spatial GNNs},

author = {Tianci Song and Eric Cosatto and Gaoyuan Wang and Rui Kuang and Mark Gerstein and Martin Renqiang Min and Jonathan Warrell},

url = {https://academic.oup.com/bioinformatics/article/40/Supplement_2/ii111/7749079},

doi = {10.1093/bioinformatics/btae383},

year  = {2024},

date = {2024-09-01},

urldate = {2024-09-01},

journal = {Bioinformatics (Supplement Issue of the Proceedings of the 23rd European Conference on Computational Biology ECCB2024)↩},

volume = {40},

number = {2},

issue = {Supplement},

pages = {ii111-ii119},

keywords = {Graph Neural Networks, Spatial Transcriptomics},

pubstate = {published},

tppubtype = {article}

}

@article{GraphTucker,

title = {Deciphering High-order Structures in Spatial Transcriptomes with Graph-guided Tucker Decomposition},

author = {Charles Broadbent and Tianci Song and Rui Kuang},

url = {https://academic.oup.com/bioinformatics/article/40/Supplement_1/i529/7700901},

doi = {doi.org/10.1093/bioinformatics/btae245},

year  = {2024},

date = {2024-07-12},

urldate = {2024-07-12},

journal = {Bioinformatics Supplement Issue of the Proceedings of 32nd International Conference on Intelligent Systems for Molecular Biology (Ian Lawson Van Toch Memorial Award for Outstanding Student Paper)↩},

volume = {40},

number = {Supplement_1},

pages = {i529-i538},

howpublished = {To appear In the Proceedings of International Conference on Intelligent Systems for Molecular Biology (ISMB) 2024},

keywords = {Protein-Protein Interaction Network, Spatial Transcriptomics, Tensor Completion},

pubstate = {published},

tppubtype = {article}

}

@article{kadaba15structural,

title = {Structural Neuroimaging Markers of NPH versus AD and PD, and chronic TBI-A Narrative Review},

author = {Sharada Kadaba Sridhar and Jen Dysterheft Robb and Rishabh Gupta and Rui Kuang and Uzma Samadani},

doi = {doi: 10.3389/fneur.2024.1347200},

year  = {2024},

date = {2024-03-14},

journal = {Frontiers in Neurology},

volume = {15},

pages = {1347200},

publisher = {Frontiers},

keywords = {},

pubstate = {forthcoming},

tppubtype = {article}

}

@article{sridhar2024ventriculomegaly,

title = {A ventriculomegaly feature computational pipeline to improve the screening of normal pressure hydrocephalus on CT},

author = {Sharada Kadaba Sridhar and Rui Kuang and Jen Dysterheft Robb and Uzma Samadani},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Journal of Neurosurgery},

volume = {1},

number = {aop},

pages = {1–11},

publisher = {American Association of Neurological Surgeons},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{GNTD,

title = {GNTD: Reconstructing Spatial Transcriptomes with Graph-guided Neural Tensor Decomposition Informed by Spatial and Functional Relations},

author = {Tianci Song and Charles Broadbent and Rui Kuang},

url = {https://www.nature.com/articles/s41467-023-44017-0},

year  = {2023},

date = {2023-04-01},

urldate = {2023-04-01},

journal = {Nature Communications},

volume = {14},

number = {8276},

keywords = {Protein-Protein Interaction Network, Spatial Transcriptomics, Tensor Completion},

pubstate = {published},

tppubtype = {article}

}

@article{spatialGCNNb,

title = {Detecting Spatially Co-expressed Gene Clusters with Functional Coherence by Graph-regularized Convolutional Neural Network},

author = {Tianci Song and Kathleen K. Markham and Zhuliu Li and Kristen E. Muller and Kathleen Greenham and Rui Kuang},

url = {https://academic.oup.com/bioinformatics/article/38/5/1344/6448221},

year  = {2022},

date = {2022-03-01},

urldate = {2022-03-01},

journal = {Bioinformatics},

volume = {38},

number = {5},

pages = {1344–1352},

keywords = {Graph Neural Networks, Spatial Transcriptomics},

pubstate = {published},

tppubtype = {article}

}

@article{li2021imputation,

title = {Imputation of spatially-resolved transcriptomes by graph-regularized tensor completion},

author = {Zhuliu Li and Tianci Song and Jeongsik Yong and Rui Kuang},

url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008218},

year  = {2021},

date = {2021-01-01},

journal = {PLoS computational biology},

volume = {17},

number = {4},

pages = {e1008218},

publisher = {Public Library of Science San Francisco, CA USA},

keywords = {Spatial Transcriptomics, Tensor Completion},

pubstate = {published},

tppubtype = {article}

}

@article{li2021scalable,

title = {Scalable Label Propagation for Multi-relational Learning on the Tensor Product of Graphs},

author = {Zhuliu Li and Raphael Petegrosso and Shaden Smith and David Sterling and George Karypis and Rui Kuang},

url = {https://ieeexplore.ieee.org/document/9369895/},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {IEEE Transactions on Knowledge and Data Engineering},

publisher = {IEEE},

keywords = {Multi-relational learning, Protein-Protein Interaction Network, Tensor Completion},

pubstate = {published},

tppubtype = {article}

}

@article{WeiZhang2020,

title = {Platform-integrated mRNA Isoform Quantification},

author = {Jiao Sun and Jae-Woong Chang and Teng Zhang and Jeongsik Yong and Rui Kuang and Wei Zhang

},

url = {https://academic.oup.com/bioinformatics/article-abstract/36/8/2466/5675495?redirectedFrom=fulltext},

year  = {2020},

date = {2020-04-15},

journal = {Bioinformatics},

volume = {36},

number = {8},

pages = {2466–2473},

keywords = {Isoform Quantification},

pubstate = {published},

tppubtype = {article}

}

@article{Petegrosso2020,

title = {Hierarchical Canonical Correlation Analysis Reveals Phenotype, Genotype, and Geoclimate Associations in Plants},

author = {Raphael Petegrosso and Tianci Song and Rui Kuang},

url = {https://spj.sciencemag.org/plantphenomics/2020/1969142/cta/},

doi = {10.34133/2020/1969142},

year  = {2020},

date = {2020-03-31},

journal = {Plant Phenomics},

volume = {2020},

number = {1969142},

keywords = {Phenome-genome Association},

pubstate = {published},

tppubtype = {article}

}

@article{nanostring,

title = {A large-scale comparative study of isoform expressions measured on four platforms},

author = {Wei Zhang and Raphael Petegrosso and Jae-Woong Chang and Jiao Sun and Jeongsik Yong and Jeremy Chien and Rui Kuang},

url = {https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-020-6643-8},

year  = {2020},

date = {2020-03-30},

journal = {BMC Bioinformatics},

volume = {21},

number = {272},

keywords = {Isoform Quantification},

pubstate = {published},

tppubtype = {article}

}

@article{roe2020accurate,

title = {Accurate and Efficient KIR Gene and Haplotype Inference from Genome Sequencing Reads with Novel K-mer Signatures},

author = {David Roe and Rui Kuang},

url = {https://www.frontiersin.org/articles/10.3389/fimmu.2020.583013/full},

year  = {2020},

date = {2020-01-01},

journal = {Frontiers in immunology},

volume = {11},

pages = {3102},

publisher = {Frontiers},

keywords = {KIR Haplotype Inference},

pubstate = {published},

tppubtype = {article}

}

@article{roe2020detailed,

title = {A Detailed View of KIR Haplotype Structures and Gene Families as Provided by a New Motif-based Multiple Sequence Alignment},

author = {David Roe and Cynthia Vierra-Green and Chul-Woo Pyo and Daniel E Geraghty and Stephen R Spellman and Martin Maiers and Rui Kuang},

url = {https://www.frontiersin.org/articles/10.3389/fimmu.2020.585731/full},

year  = {2020},

date = {2020-01-01},

journal = {Frontiers in immunology},

volume = {11},

publisher = {Frontiers Media SA},

keywords = {KIR Haplotype Inference},

pubstate = {published},

tppubtype = {article}

}

@article{roe2020efficient,

title = {Efficient Sequencing, Assembly, and Annotation of Human KIR Haplotypes},

author = {David Roe and Jonathan Williams and Keyton Ivery and Jenny Brouckaert and Nick Downey and Chad Locklear and Rui Kuang and Martin Maiers},

url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7581912/},

year  = {2020},

date = {2020-01-01},

journal = {Frontiers in immunology},

volume = {11},

publisher = {Frontiers Media SA},

keywords = {KIR Haplotype Inference},

pubstate = {published},

tppubtype = {article}

}

@article{petegrosso2019scrnaseq,

title = {Machine Learning and Statistical Methods for Clustering Single-cell RNA-sequencing Data},

author = {Raphael Petegrosso and Zhuliu Li and Rui Kuang},

url = {https://doi.org/10.1093/bib/bbz063},

year  = {2019},

date = {2019-06-29},

journal = {Briefings in Bioinformatics},

abstract = {Single-cell RNAsequencing (scRNA-seq) technologies have enabled the large-scale whole-transcriptome profiling of each individual single cell in a cell population. A core analysis of the scRNA-seq transcriptome profiles is to cluster the single cells to reveal cell subtypes and infer cell lineages based on the relations among the cells. This article reviews the machine learning and statistical methods for clustering scRNA-seq transcriptomes developed in the past few years. The review focuses on how conventional clustering techniques such as hierarchical clustering, graph-based clustering, mixture models, $k$-means, ensemble learning, neural networks and density-based clustering are modified or customized to tackle the unique challenges in scRNA-seq data analysis, such as the dropout of low-expression genes, low and uneven read coverage of transcripts, highly variable total mRNAs from single cells and ambiguous cell markers in the presence of technical biases and irrelevant confounding biological variations. We review how cell-specific normalization, the imputation of dropouts and dimension reduction methods can be applied with new statistical or optimization strategies to improve the clustering of single cells. We will also introduce those more advanced approaches to cluster scRNA-seq transcriptomes in time series data and multiple cell populations and to detect rare cell types. Several software packages developed to support the cluster analysis of scRNA-seq data are also reviewed and experimentally compared to evaluate their performance and efficiency. Finally, we conclude with useful observations and possible future directions in scRNA-seq data analytics.



AVAILABILITY:

All the source code and data are available at https://github.com/kuanglab/single-cell-review},

keywords = {scRNA-Seq, scRNA-Seq Clustering},

pubstate = {published},

tppubtype = {article}

}

@article{song2019path,

title = {Path segmentation for movement trajectories with irregular sampling frequency using space-time interpolation and density-based spatial clustering},

author = {Ying Song and Tianci Song and Rui Kuang},

url = {https://doi.org/10.1111/tgis.12549},

year  = {2019},

date = {2019-06-05},

journal = {Transactions in GIS},

volume = {23},

number = {3},

pages = {558--578},

abstract = {Path segmentation methods have been developed to distinguish stops and moves along movement trajectories. However, most studies do not focus on handling irregular sampling frequency of the movement data. This article proposes a four‐step method to handle various time intervals between two consecutive records, including parameter setting, space‐time interpolation, density‐based spatial clustering, and integrating the geographic context. The article uses GPS tracking data provided by HOURCAR, a non‐profit car‐sharing service in Minnesota, as a case study to demonstrate our method and present the results. We also implement the DB‐SMoT algorithm as a comparison. The results show that our four‐step method can handle various time intervals between consecutive records, group consecutive stops close to each other, and distinguish different types of stops and their inferred activities. These results can provide novel insights into car‐sharing behaviors such as trip purposes and activity scheduling.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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.},

keywords = {Protein Remote Homology Detection},

pubstate = {published},

tppubtype = {article}

}