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Yufei Huang

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Professor 

Department of Electrical and Computer Engineering, the University of Texas at San Antonio

Adjunct Professor

Department of Population Health Science, University of Texas Health Science Center at San Antonio

 

Research interests

  1. Computational Systems Biology
    1. m6A and epitranscriptome functions
    2. cancer genomics and precision oncology
    3. KSHV biology
  1. Artificial Intelligence and deep learning
    1. Interpretable DL models for genomics
    2. Human reasoning and decision making
  1.  Brain Computer Interface
    1. EEG-based prediction of cognitive states
    2. Passive BCI systems

Bio-Sketch

Yufei Huang received his Ph.D. degree in electrical engineering from the State University of New York at Stony Brook in 2001. Since 2002, he has been with the Department of Electrical and Computer Engineering at the University of Texas at San Antonio (UTSA), where he is now Professor. He is also an adjunct professor at the Dept. of Population Health Science at the University of Texas Health San Antonio. He has been a visiting professor at the Center of Bioinformatics, Harvard Center for Neurodegeneration & Repair.

 

Dr. Huang’s expertise is in the areas of computational biology, brain-computer interface, machine learning, and artificial intelligence. One of the current focuses is to study the functions of mRNA methylation using machine learning and high throughput sequencing technologies, where his lab developed several widely used m6A data analysis pipelines. His lab also develops artificial intelligence systems for precision medicine and passive EEG-based brain-machine-interaction systems for an understanding of human cognitive behaviors. He was a recipient of the National Science Foundation (NSF) CAREER Award, UTSA Presidential Achievement Award on Research Excellence, Best Paper Award IEEE Biomedical and Health Informatics Conference, Best Paper Award of Artificial Neural Networks in Engineering Conference, and Best Paper Award of IEEE Signal Processing Magazine. He is a member of the UTSA Academy of Distinguished Researchers. His research has been supported by NSF, NIH, Air Force Office of Scientific Research, Army Research Lab, Department of Defense, and Qatar National Research Fund. He serves on IEEE Biomedical and Health Informatics Technical Committee and in the role of Associate Editor for multiple journals including IEEE Transactions on Signal Processing, IEEE Transactions on BHI, BMC Systems Biology and Neurocomputing.

To prospective students:  

Research assistant positions are available for highly motivated students, who are committed to pursuing Ph.D. degrees in the area of computational biology and AI. Students must have strong machine learning, mathematics, statistics, and programming skills. Preference will be given to those with MS degree

Facility

Research

Computational Biology


Epitranscriptome and RNA Methylation: 

Epitranscriptome is an exciting new area that studies the mechanisms and functions of methylation in transcripts.


Decomposition of RNA methylome reveals co-methylation patterns induced by latent enzymatic regulators of the epitranscriptome. 
Despite the intriguing advancements, little is known so far about the dynamic landscape of RNA methylome across different cell types and how the epitranscriptome is regulated at the system level by enzymes, i.e., RNA methyltransferases and demethylases. To investigate this issue, a meta-analysis of m6A MeRIP-Seq datasets collected from 10 different experimental conditions (cell type/tissue or treatment) is performed.L Liu, SW Zhang, YC Zhang, H Liu, L Zhang, R Chen, Y Huang, J Meng Molecular BioSystems 11 (1), 262-274

MeT-DB
The MethylTranscriptome DataBase is the first comprehensive resource for N6-methyladenosine (m(6)A) in mammalian transcriptome.
Liu, H., Flores, M.A., Meng, J., Zhang, L., Zhao, X., Rao, M.K., Chen, Y. and Huang, Y. (2015) MeT-DB: a database of transcriptome methylation in mammalian cells. Nucleic acids research, 43, D197-203.
MeTDiff
A package developed for the differential analysis for MeRIP-seq data of two experimental conditions to unveil the dynamics in post-transcriptional regulation of the RNA methylome.
Xiaodong Cui, Lin Zhang, Jia Meng, Manjeet Rao, Yidong Chen,Yufei Huang IEEE/ACM Transactions on Computational Biology and Bioinformatics 03/2015; PP(99):1-1. DOI:10.1109/TCBB.2015.2403355

exomePeak
exomePeak is a R-based software package that performs methylation site detection and differential methylation detection from Methylated RNA immunoprecipitation sequencing (MeRIP-Seq) data.
Meng, J., Lu, Z., Liu, H., Zhang, L., Zhang, S., Chen, Y., Rao, M.K. and Huang, Y. (2014) A protocol for RNA methylation differential analysis with MeRIP-Seq data and exomePeak R/Bioconductor package. Methods, 69, 274-281.

microRNAs: 

microRNAs are recently discovered non-coding RNAs that act in the regulation of gene expression


TraceRNA
TRAceRNA is a computational tool and an online application to assist the exploration of ceRNAs.
Flores, M., Chen, Y. and Huang, Y. (2014) TraceRNA: a web application for competing endogenous RNA exploration. Circulation. Cardiovascular genetics, 7, 548-557.

Cellular Transformation and Tumorigenesis mediated by viral miRNAs
Here, we show that regulation of cell cycle progression and apoptosis by KSHV-encoded microRNAs (miRs) is required for KSHV-induced cellular transformation and tumorigenesis.
Moody R, Zhu Y, Huang Y, et al. KSHV MicroRNAs Mediate Cellular Transformation and Tumorigenesis by Redundantly Targeting Cell Growth and Survival Pathways. Grundhoff A, ed. PLoS Pathogens. 2013;9(12):e1003857. doi:10.1371/journal.ppat.1003857.

Prediction of microRNAs Associated with Human Diseases
More experimentally validated miRNA-disease associations have been accumulated recently. On the basis of these associations, it is essential to predict disease miRNAs for various human diseases
Xuan, P., Han, K., Guo, M., Guo, Y., Li, J., Ding, J., & Huang, Y*. (2013). Prediction of microRNAs Associated with Human Diseases Based on Weighted k Most Similar Neighbors. PLoS one, 8(8), e70204. (IF:3.73)

Uncover context-specific gene regulation by transcription factors and microRNAs
A novel Bayesian sparse non-negative factor regression (BSNFR) model is proposed for modeling the joint regulations of mRNAs by TFs and miRNAs and integration of multiple data types including gene expressions, microRNA expressions, TF targeted genes, and microRNA targets.
J. Meng, Y. Chen, Y. Huang, Uncover context-specific gene regulation by transcription factors and microRNAs using Bayesian sparse nonnegative factor regression analysis, Journal of Biological Systems, Vol. 20, No. 4 (2012) 377402

BCmicrO
A Bayesian Decision Fusion Approach for microRNA Target Prediction
Yue, D., Guo, M.Z., Chen, Y.D. and Huang, Y.F. (2012) A Bayesian decision fusion approach for microRNA target prediction. BMC Genomics, 13.

MaturePred
Efficient Identification of MicroRNAs within Novel Plant Pre-miRNAs
Xuan P1, Guo M, Huang Y, Li W, Huang Y. MaturePred: efficient identification of microRNAs within novel plant pre-miRNAs. PLoS One. 2011;6(11):e27422. doi: 10.1371/journal.pone.0027422. Epub 2011 Nov 16.

SVMicrO
Improving Performance of Mammalian MicroRNA Target Prediction
Liu, H., Yue, D., Chen, Y.D., Gao, S.J. and Huang, Y.F. (2010) Improving performance of mammalian microRNA target prediction. BMC bioinformatics.

Transcription and drug Networks; high throughput data analysis


BRCA-MoNet:
BRCA-Monet: a breast cancer specific drug treatment mode-of-action network for treatment effective prediction using large scale microarray database
Chifeng Ma, Hung-I Harry Chen, Mario Flores, Yufei Huang, Yidong Chen BMC Syst Biol. 2013; 7(Suppl 5): S5. Published online 2013 December 9. doi: 10.1186/1752-0509-7-S5-S5

CrossLink:
Application for mRNA Data Exploration in Breast Cancer

Brain Computer Interface


BRAIN COMPUTER INTERFACE LAB



Predicting Serial Visual Presentation Events from EEG Signals Using Deep learning
First comprehensive investigation of deep learning (DL) methods for target prediction in time-locked rapid serial visual presentation (RSVP) experiments.
Z. Mao, V. Lawhern, L. M. Merino, K. Ball, L. Deng, B. J. Lance, K. Robbins, Y. Huang, Predicting Serial Visual Presentation Events from EEG Signals Using Deep learning, IEEE Transactions on Neural Systems & Rehabilitation Engineering, submitted.

Classification of imperfectly time-locked image RSVP events with EEG device
Classification based on EEG data in an RSVP experiment is considered. We consider here a more practical scenario that allows variation in response latency and develop a rigorous statistical formulation for modeling the uncertainty within the varying latency coupled with a likelihood ratio test (LRT) for classification.
J. Meng, L. M. Merino, K. Robbins, Y Huang, Classification of imperfectly time-locked image RSVP events with EEG device, Neuroinformatics, Sept. 2013 DOI 10.1007/s12021-013-9203-4, PMID:24037139 (IF: 3.13)

Characterization and robust classification of EEG signal from image RSVP events with independent time-frequency features
This paper considers the problem of automatic characterization and detection of target images in a rapid serial visual presentation (RSVP) task based on EEG data.
J. Meng, L. M. Merino, N. B. Shamlo, S. Makeig, K. Robbins, Y Huang Characterization and robust classification of EEG signal from image RSVP events with independent time-frequency features,” PLoS ONE, 7.9 (Sept. 2012): e44464. PMID: 23028544, PMCID: PMC3445552 (IF: 4.09)

 

Media

Publications

Book Chapters

  1. J. Meng, Y. Huang, “Biclustering of Time Series Microarray Data”,  in J. Wang (Ed) Methods in Molecular Biology, in press.
  2. J. Meng, Y. Huang “Gene Regulation,” in Encyclopedia of Systems Biology, in press
  3. Y. Huang and E. Dougherty, “Probabilistic Boolean Networks as Models for Gene Regulation,” in Dehmer, Emmert-Streib (Ed.): Analysis of Microarray Data, Wiley-VCH, 20

Journal Papers (refereed full length; * corresponding author; IF: impact factor)

  1. Xiao, T. Zhang, XN Dong, Y. Han, Y. Huang*, X. Wang* (2020) Prediction of trabecular bone architectural features by deep learning models using simulated DXA images, Bone reports, 100295
  2. Panwar, S., Rad, P., Jung, T. P., & Huang, Y.* (2020). Modeling EEG data distribution with a Wasserstein Generative Adversarial Network to predict RSVP Events. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2020/7/1, Vol. 28-8, p1720-1730.
  3. Salekin S, Mostavi M, Chiu Y, Chen Y, Zhang J*, Huang Y* (2020). Predicting sites of epitranscriptome modifications using unsupervised representation learning based on generative adversarial networks. Front Phys – Computational Physics; Machine Learning Applications for Physics-Based Computational Models of Biological Systems, 8, p. 196
  4. Ramirez, R., Chiu, Y.C., Hererra, A., Mostavi, M., Ramirez, J., Chen, Y., Huang, Y. and Jin, Y.F., 2020. Classification of Cancer Types Using Graph Convolutional Neural Networks. Frontiers in Physics8, p.203.
  5. Mostavi, M., Chiu, Y. C., Huang, Y.*, & Chen, Y.* (2020). Convolutional neural network models for cancer type prediction based on gene expression. BMC Medical Genomics, 13, 1-13.
  6. Gruffaz, M., Zhang, T., Marshall, V., Goncalves, P., Ramaswami, R., Labo, N., Whitby, D., Uldrick, T. S., Yarchoan, R., Huang, Y., & Gao, S.-J.* (2020). Signatures of oral microbiome in HIV-infected individuals with oral Kaposi’s sarcoma and cell-associated KSHV DNA. PLOS PATHOGENS, 16(1).
  7. Chiu YC, Chen HI, Gorthi A, Mostavi M, Zheng S, Huang Y*, Chen Y*. Deep learning of pharmacogenomics resources: moving towards precision oncology. Briefings in Bioinformatics. 2019 Dec 8. bbz144, https://doi.org/10.1093/bib/bbz144
  8. Zhang S-Y, Zhang S-W, Fan X-N, Zhang T, Meng J, Huang Y*. FunDMDeep-m6A: identification and prioritization of functional differential m6A methylation genes. Bioinformatics. 2019;35(14):i90-i8. doi: 10.1093/bioinformatics/btz316 *
  9. Gruffaz, M., Yuan, H., Meng, W., Liu, H., Bae, S., Kim, J.-S., Lu, C., Huang, Y., & Gao, S.-J. (2019). CRISPR-Cas9 Screening of Kaposi’s Sarcoma-Associated Herpesvirus-Transformed Cells Identifies XPO1 as a Vulnerable Target of Cancer Cells. MBIO, 10(3).
  10. Zhang, L., He, Y., Wang, H., Liu, H., Huang, Y., Wang, X., & Meng, J. (2019). Clustering Count-based RNA Methylation Data Using a Nonparametric Generative Model. CURRENT BIOINFORMATICS, 14(1), 11-23.
  11. Chen, J.X., Zhang, P.W., Mao, Z.J., Huang, Y.F., Jiang, D.M. and Zhang, Y.N., 2019. Accurate EEG-Based Emotion Recognition on Combined Features Using Deep Convolutional Neural Networks. IEEE Access7, pp.44317-44328
  12. Tang, Y., Chen, K., Wu, X., Wei, Z., Song, B., Zhang, S., Huang, Y. and Meng, J.*, 2019. DRUM: Inference of disease-associated m6A RNA methylation sites from a multi-layer heterogeneous network. Frontiers in genetics10, p.266.
  13. Chiu YC, Chen HH, Zhang T, Zhang S, Gorthi A, Wang LJ, Huang Y*, Chen Y*. Predicting drug response of tumors from integrated genomic profiles by deep neural networks. BMC Med Genomics. 2019 Jan 31;12(Suppl 1):18. PubMed PMID: 30704458; PubMed Central PMCID: PMC6357352.
  14. Zhang S-Y, Zhang S-W *, Fan X, Meng J, Chen Y, Huang Y*. Global analysis of N6-methyladenosine functions and its disease association using deep learning and network-based methods. PLoS Computational Biology, Machine Learning in Health and Biomedicine Collection, 2019 Jan 2nd. *corresponding authors
  15. Chen H-IH, Chiu Y-C, Zhang T, Zhang S, Huang Y*, Chen Y*. GSAE: an autoencoder with embedded gene-set nodes for genomics functional characterization. BMC Systems Biology 12(S8), December 2018, DOI: 10.1186/s12918-018-0642-2, * corresponding authors
  16. Panneerdoss S, Eedunuri VE, Timilsina S, Rajamanickam S, Suryavathi V, Abdelfattah S, Onyeagucha BC, Cui X, Mohammad TA, Huang THM, Huang Y*, Chen Y*, Rao MK*. Cross-talk among writers, readers, and erasers of m6A regulates cancer growth and progression, Science Advances, 4, no. 10, eaar8263. (IF: 11.51)
  17. Salekin S, Zhang JM, Huang Y*. Base-pair resolution detection of transcription factor binding site by deep deconvolutional network. Bioinformatics. 2018 May 10. doi: 10.1093/bioinformatics/bty383. (IF: 5.48)
  18. Wei Z, Panneerdoss S, Timilsina S, Zhu J, Mohammad TA, Lu ZL, de Magalhães JP, Chen Y, Rong R, Huang Y, Rao MK. Topological Characterization of Human and Mouse m5C Epitranscriptome Revealed by Bisulfite Sequencing. International Journal of Genomics. 2018; (IF: 1.9)
  19. Nayak T, Zhang T, Mao Z, Xu X, Zhang L, Pack DJ, Dong B, Huang Y. Prediction of Human Performance Using Electroencephalography under Different Indoor Room Temperatures. Brain sciences. 2018 Apr;8(4). (IF: 2.57)
  20. Liu H, Wang H, Wei Z, Zhang S, Hua G, Zhang S, Zhang L, Gao S-J, Meng* J, Chen* X, Huang Y* (2018). MeT-DB V2.0: Elucidating context-specific functions of N6-methyl-adenosine methyltranscriptome. Nucleic Acids Res, Jan 2018, 4;46(D1):D281-D287. doi: 10.1093/nar/gkx1080, (IF: 10.162)
  21. Tan, B., Liu, H., Zhang, S., da Silva, S. R., Zhang, L., Meng, J., Cui, X., Yuan, H., Sorel, O., Zhang, S., Huang*, Y., Gao*, S-J (2018). Viral and Cellular N6-Methyladenosine (m6A) and N6, 2′-O-Dimethyladenosine (m6Am) Epitranscriptomes in KSHV Life Cycle. Nature Microbiology, 2018 Jan;3(1):108-120. doi: 10.1038/s41564-017-0056-8
  22. M. Sanchez-Castillo, D. Blanco, I. M. Tienda – Luna, M. C. Carrion, and Y. Huang, A Bayesian framework for the inference of Gene Regulatory Networks from time and pseudo-time series data. Bioinformatics, Sep 25. 2017 doi: 10.1093/bioinformatics/btx605. (IF: 7.307)
  23. Meriño, L., Nayak, T., Kolar, P., Hall, G., Mao, Z., Pack, D. J., & Huang, Y*. (2017). Asynchronous control of unmanned aerial vehicles using a steady-state visual evoked potential-based brain computer interface. Brain-Computer Interfaces, 4(1-2), 122-135.
  24. Zhang, S., Zhang, S., Liu, L., Meng, J., & Huang, Y*. (2016). m6A-Driver: Identifying Context-Specific mRNA m6A Methylation-Driven Gene Interaction Networks.PLOS Computational Biology12(12), e1005287. (IF: 4.86)
  25. Cui, X., Meng, J., Zhang, S., Chen, Y., & Huang, Y*. A novel algorithm for calling mRNA m6A peaks by modeling biological variances in MeRIP-seq data. Bioinformatics. 2016 Jun 15;32(12):i378-i385. doi: 10.1093/bioinformatics/btw281. (IF: 7.307)
  26. M. Hajinoroozi, Z. Mao, T-P Jung, C-T Lin, and Y. Huang* “EEG-based Prediction of Driver’s Cognitive Performance by Deep Convolutional Neural Network,” Elsevier Signal Processing: Image Communication, DOI: 10.1016/j.image.2016.05.018
  27. Land, William M., B. Liu, A. Cordova, M. Fang, Y. Huang, and W.X. Yao. “Effects of Physical Practice and Imagery Practice on Bilateral Transfer in Learning a Sequential Tapping Task.” PLOS One 11, no. 4 (2016): e0152228. (IF: 3.234)
  28. Cui, X., Meng, J., Zhang, S., Rao, M. K., Chen, Y., & Huang, Y. (2016). A Hierarchical Model for Clustering m6A Methylation Peaks in MeRIP-seq Data. BMC System Biology (IF: 3.10)
  29. Cui, X, Zhen, W, Zhang L, Liu H, Sun L, Zhang, S., Huang Y. Meng J, “Guitar: An R/Bioconductor Package for Gene Annotation Guided Transcriptomic Analysis of RNA-Related Genomic Features,” BioMed Research International, vol. 2016, Article ID 8367534, 8 pages, 2016. doi:10.1155/2016/8367534. (IF: 1.579)
  30. Cui, X., Zhang, L., Meng, J., Rao, M., Chen, Y., & Huang, Y. “MeTDiff: a Novel Differential RNA Methylation Analysis for MeRIP-Seq Data.” IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol:PP, 99, March , 2015, doi: 10.1109/TCBB.2015.2403355 (IF: 1.609)
  31. Cui, X., Meng, J., Rao, M. K., Chen, Y., & Huang, Y. (2015). HEPeak: an HMM-based exome peak-finding package for RNA epigenome sequencing data. BMC Genomics,16(Suppl 4), S2. doi:10.1186/1471-2164-16-S4-S2 (IF: 3.86)
  32. Zhou, Yi, Hung‐I. H. Chen, A. L. Lin, H. Dang, Karin Haack, Shelley A. Cole, Y. Huang, H. Yu, Yidong Chen, and Chih‐Ko Yeh. “Early Gene Expression in Salivary Gland After Isoproterenol Treatment.”Journal of cellular biochemistry 116, no. 3 (2015): 431-437. (IF: 3.368)
  33. Liu H, Flores MA, Meng J, Zhang L, Zhao X, Rao MK, Chen Y, Huang Y*. MeT-DB: a database of transcriptome methylation in mammalian cells. Nucleic Acids Res. 2015 Jan. 28. pii: gku1024. PubMed PMID: 25378335, doi: 10.1093/nar/gku1024 (IF: 8.378)
  34. Yao, Wan X., Jinqi Li, Zhiguo Jiang, Jia-Hong Gao, Crystal G. Franklin, Yufei Huang, Jack L. Lancaster, and Guang H. Yue. “Aging interferes central control mechanism for eccentric muscle contraction.” Frontiers in aging neuroscience 6 (2014).
  35. Liu, Lian, Shao-Wu Zhang, Yu-Chen Zhang, Hui Liu, Lin Zhang, Runsheng Chen, Yufei Huang, and Jia Meng. “Decomposition of RNA methylome reveals co-methylation patterns induced by latent enzymatic regulators of the epitranscriptome.”Molecular BioSystems 11, no. 1 (2015): 262-274. (IF:3.18)
  36. M. Flores, Y. Chen, Y. Huang*. TraceRNA: A Web Application for Competing Endogenous RNA Exploration. Circ Cardiovasc. Genet, 2014, Aug; 7(4): 548-57. doi: 10.1161/CIRCGENETICS.113.000125 (IF: 6.728)
  37. Rosalie Moody, Ying Zhu, Yufei Huang, Xiaodong Cui, Tiffany Jones, Roble Bedolla, Xiufen Lei, Zhiqiang Bai, Shou-Jiang Gao. KSHV MicroRNAs Mediate Cellular Transformation and Tumorigenesis by Redundantly Targeting Cell Growth and Survival Pathways. PLoS Pathogens, 2014; 9 (12): e1003857 DOI: 10.1371/journal.ppat.1003857, PMID:24385912 (IF: 8.13)
  38. Meng J, Lu Z, Liu H, Zhang L, Zhang S, Chen Y, Rao MK, Huang Y*. A protocol for RNA methylation differential analysis with MeRIP-Seq data and exomePeak R/Bioconductor package. Methods. 2014 Oct 1;69(3):274-81. doi: 10.1016/j.ymeth.2014.06.008. Epub 2014 Jun 27. PubMed PMID: 24979058; PubMed Central PMCID: PMC4194139. (IF:4.197)
  39. Ma, Chifeng, Hung-I. H. Chen, Mario Flores, Yufei Huang*, and Yidong Chen. “BRCA-Monet: a breast cancer specific drug treatment mode-of-action network for treatment effective prediction using large scale microarray database.” BMC Systems Biology 7, no. Suppl 5 (2013): S5. (IF:2.98)
  40. J. Meng, L. M. Merino, K. Robbins, Y Huang*, “Classification of imperfectly time-locked image RSVP events with EEG device,” Neuroinformatics, Sept. 2013 DOI 10.1007/s12021-013-9203-4, PMID:24037139 (IF: 3.13)
  41. Xuan, P., Han, K., Guo, M., Guo, Y., Li, J., Ding, J., & Huang, Y*. (2013). Prediction of microRNAs Associated with Human Diseases Based on Weighted k Most Similar Neighbors. PLoS one, 8(8), e70204. (IF:3.73)
  42. J. Meng, X. Cui, M. Rao, Y. Chen, Y. Huang*,Exome-based Analysis for RNA Epigenome Sequencing DataBioinformatics, Apr. 2013; doi: 10.1093/bioinformatics/btt171, PMID:23589649 (IF:5.32)
  43. M. Flores, T-H Hsiao, Y-C Chiu, E. Y. Chuang, Y. Huang*, Y. Chen, “Gene Regulation, Modulation and Their Applications in Gene Expression Data Analysis,” Advances in Bioinformatics, 2013: 360678, 2013 March 13. doi:  10.1155/2013/360678, PMID:23573084
  44. Sanchez-Diaz PC, Hsiao TH, Chang JC, Yue D, Tan MC, Chen HI, Tomlinson GE, Huang Y, Chen Y, Hung JY. “De-Regulated MicroRNAs in Pediatric Cancer Stem Cells Target Pathways Involved in Cell Proliferation, Cell Cycle and Development.” PLoS One. 2013 Apr 17;8(4):e61622. PMID: 23613887, PMCID: PMC3629228 (IF: 4.09)
  45. J. Meng, Y. Chen, Y. Huang*, “Uncover context-specific gene regulation by transcription factors and microRNAs using Bayesian sparse nonnegative factor regression analysis,” Journal of Biological Systems, Vol. 20, No. 4 (2012) 377–402 (IF: 0.57)
  46. X. Lei, Y. Zhu, T. Jones, Z Bai, Y. Huang, and S-J Gao, “A KSHV microRNA targets TGF-β pathway to promote cell survival” Journal of Virology, 86:11698116711, Nov. 2012, doi: 10.1128/JVI.06855-11 (IF: 5.42)
  47. J. Meng, L. M. Merino, N. B. Shamlo, S. Makeig, K. Robbins, Y Huang* “Characterization and robust classification of EEG signal from image RSVP events with independent time-frequency features,” PLoS ONE, 7.9 (Sept. 2012): e44464. PMID: 23028544, PMCID: PMC3445552 (IF: 4.09)
  48. L. Zhang, J. Meng, H. Liu, Y. Huang*, “A nonparametric Bayesian approach for clustering bisulfate-based DNA methylation profiles,” BMC Genomics, 2012;13 Suppl 6:S20.PMID: 23134689, PMCID:PMC3481479
  49. D. Yue, Y. Chen, Y. Huang*, “A Bayesian Decision Fusion Approach for microRNA Target Prediction,” BMC Genomics, 2012;13 Suppl 8:S13, Dec, 2012, PMID: 23282032, PMCID:PMC3535698
  50. Huang Y*, Zhao Z, Xu H, Shyr Y, Zhang B (2012) “Advances in Systems Biology: Computational Algorithms and Applications” BMC Systems Biology, 2012;6 Dec, 2012, Suppl 3:S1, PMID: 23281622, PMCID:PMC3524016
  51. Zhao Z, Zhang B, Shyr Y, Huang Y, Xu H, (2012) Genomics in 2012: challenges and opportunities in the next generation sequencing era. BMC Genomics, 13 Suppl 8:S1, Dec, 2012, PMID: 23281891, PMCID: PMC3535713
  52. D. Yue, J. Meng, M. Lu, P. Chen, M. Guo, Y. Huang*, “Understanding microRNA regulation: A computational perspective,” IEEE Signal Process Magazine, 29:1, 77-88, 2012. (IF:6.0)
  53. N. Nguyen, Y. Chiao, Y. Huang, S-J, Gao, M. Lindsey, Y. Chen, Y. Jin “Temporal clustering of gene expression patterns using short-time segments” Int. J. Functional Informatics and Personalised Medicine, Vol 4, No. 1, 2011
  54. O. Ghasemi, M. Lindsey, T. Yang, N. Nguyen, Y. Huang, Y. Jin, “Bayesian parameter estimation for nonlinear modeling of biological pathways,” BMC Systems Biology, 2011, 5(Suppl 3):S9  doi:10.1186/1752-0509-5-S3-S9 (IF: 3.57)
  55. Xuan, P; Guo, MZ; Huang, YC; Li, WB; Huang, Y*, “MaturePred: Efficient Identification of MicroRNAs within Novel Plant Pre-miRNAs”, PLOS ONE, 6 (11): – NOV 16 2011 (IF: 4.41)
  56. J. Meng, J. Zhang, Y. Chen, Y. Huang*, “Bayesian non-negative factor analysis for reconstructing transcription factor mediated regulatory networks,” Proteome Science, Volume 9, Supplement 1, 2011 (IF: 2.49)
  57. Boutz DR, Collins P, Suresh U, Lu M, Ramírez CM, Fernández-Hernando C, Huang Y, de Sousa Abreu R, Le SY, Shapiro BA, Liu AM, Luk JM, Aldred SF, Trinklein N, Marcotte EM, Penalva LO, A two-tiered approach identifies a network of cancer and liver diseases related genes regulated by miR-122, Journal of Biological Chemistry, 286: 18066-78, 2011 (IF:5.33)
  58. P. Xuan, M. Guo, X. Liu, Y. Huang, W. Li, Y. Huang*, “PlantMiRNAPred: efficient classification of real and pseudo plant pre-miRNAs” (2011) Bioinformatics 27: 1368-1376 (IF:4.88)
  59. J. Meng, J. Zhang, Y. Qi, Y. Chen, Y. Huang*, “Uncovering Transcriptional Regulatory Networks by Sparse Bayesian Factor Model” EURASIP Journal of Advances in Signal Processing, Vol. 2010, doi:10.1155/2010/538919 (IF: 1.01)
  60. H Liu, D Yue, Y Chen, S-J Gao, Y Huang*, “A Bayesian Approach for Identifying miRNA Targets by Combining Sequence Prediction and Gene Expression Profiling,” BMC Genomics, 2010, 11 (Suppl 3):S12 doi: 10.1186/1471-2164-11-S3-S12, [PMCID: PMC2999342] (IF: 4.21)
  61. J. Zhang, X. Zhou , H. Wang, A. Suffredini,  L. Zhang,  Y. Huang,  S. Wong ,  “Bayesian Peptide Peak Detection for High Resolution TOF Mass Spectrometry.” IEEE Transactions on Signal Processing, Nov., 2010, Vol 58:No. 11, 10.1109/TSP.2010.2065226.  (IF: 2.65)
  62. H. Liu, D. Yue, Y. Chen, S-J Gao, Y. Huang*, “Improving Performance of Mammalian MicroRNA Target Prediction,” BMC Bioinformatics, 2010; 11: 476.  doi: 10.1186/1471-2105-11-476.[PMCID: PMC2955701] (IF: 4.03)
  63. J. Meng, Y. Chen, S-J Gao, Y. Huang*, “Robust inference of the context specific structure and temporal dynamics of gene regulatory network,” BMC Genomics, 2010 11(Suppl 3): S11. doi: 10.1186/1471-2164-11-S3-S11. [PMCID: PMC2999341] (IF: 4.21)
  64. Lei XF, Ye FC, Bai ZQ, Huang Y and Gao S-J. Regulation of herpesvirus lifecycle by viral microRNAs. Virulence, 2010 September 1; 1(5): 433–435. [PMCID: PMC3003333]
  65. Lei XF, Bai ZQ, Ye FC, Huang Y and Gao S-J. MicroRNAs control herpesviral dormancy. Cell Cycle, 2010, 9, 1225-6. [PMICD: PMC2910125] (IF: 4.99),
  66. Lei XF, Bai ZQ, Xie JP, Ye FC, Zhou FC, Huang Y and Gao S-J. “Regulation of NF-κB inhibitor IκBα and viral replication by a KSHV microRNA,” Nature Cell Biology, Jan. 2010. [PMICD: PMC2815189] (IF: 19.527)
  67. D. Yue, H. Liu, and Y. Huang*, “Survey of Computational Algorithms for MicroRNA Target Prediction,” Special Issue on Special Issue on Genomic Signal Processing, Current Genomics, Volume 10, Number 7, November 2009 , pp. 478-492(15)  (IF: 2.48)
  68. J. Zhang,  Elias Gonzalez, Travis Hestilow,  William Haskins, Y. Huang*, “Review of Peak Detection and Feature Selection Algorithms in Liquid-Chromatography Mass Spectrometry” Special Issue on Special Issue on Genomic Signal Processing, Current Genomics, Volume 10, Number 8, Sept., 2009, pp. 388-401(IF: 2.48)
  69. J. Meng, S-J Gao, and Y. Huang*,  “Enrichment Constrained Time-Dependent Clustering Analysis for Finding Meaningful Temporal Transcription Modules”, Bioinformatics, Jun 15;25(12):1521-7,  2009 (IF:4.877)
  70. T. Hestilow, Y. Huang* “Clustering Of Gene Expression Data Based On Shape Similarity,” EURASIP Journal on Bioinformatics and Systems Biology, 2009:195712
  71. T. Wei, Y. Huang*, and P. Chen “Particle Filtering for Adaptive Sensor Fault Detection and Identification,” IEEE Trans. on Systems, Man, and Cybernetics, Part C,  39(2):201–213.  pp.  201-213 , Feb, 2009 (IF: 2.02)
  72. Tienda-Luna, I.M. and Perez, M.C.C. and Padillo, D.P.R. and Yin, Y. and Huang, Y., “Sensitivity and Specificity of Inferring Genetic Regulatory Interactions with the VBEM Algorithm,” IADIS International Journal on Computer Science and Information Systems, Vol4, p 54-63, 2009
  73. Y. Huang*, I. T. Luna, and Y. Wang, “A survey on statistical models for reverse engineering gene regulatory networks,” IEEE Signal Processing Magazine, Jan 2009. (IF:6.0),
  74. I. T. Luna , Y. Yin, Y. Huang*, D. P. R. Padillo, and Y. Wang, “Uncovering gene networks using variational Bayesian variable selection,” Special Issue of Computational Biology, the Artificial Life Journal, January, 2008.
  75. I. T. Luna, Y. Yin, Y. Huang*, D. P. R. Padillo, H. Cai, M. Sanchez, and Y. Wang, “Inferring the Skeleton Cell Cycle Regulatory Network in Malaria Parasite using Comparative Genomic and Bayesian Approaches,” Genetica, Vol 132, No. 2, pp 131-142, June 2007.
  76. I. T. Luna, Y. Yin, Y. Huang*, D. P. R. Padillo, and Y. Wang, “Uncovering gene regulatory networks from time series microarray data with variational Bayesian structural expectation maximization,” EURASIP Journal on Bioinformatics and Systems Biology, June, 2007.
  77. Y. Huang*, J. Wang, J. Zhang, M. Sanchez, and Y. Wang, “Bayesian Inference of Genetic Regulatory Networks from Time Series Microarray Data Using Dynamic Bayesian Networks,” Journal of Multimedia, Vol3. No 2, pp 46-56, June 2007
  78. Y. Huang*, J. Zhang, I.Tienda-Luna, P. M. Djurić, and D. P. Ruiz  “Adaptive blind multiuser detection over flat fast fading channels using particle filtering,” EURASIP Journal on Wireless Communications and Networking, 2005:2, 130-140, 2005
  79. Y. Huang*, J. Zhang, and P. M. Djurić, “Bayesian Detection for BLAST,” IEEE Transactions on Signal Processing, vol. 53, no. 3, pp. 1086-1096, March, 2005.
  80. Y. Huang* and P. M. Djurić, “A blind particle filtering detector of signals transmitted over flat fading channels,” vol. 52, no. 7, pp. 1891-1900, July, IEEE Transactions on Signal Processing, 2004.
  81. Y. Huang and P. M. Djurić, “A hybrid importance function for particle filtering,” IEEE Signal Processing Letters, Feb., 2004.
  82. P. M. Djurić, J. M. Kotecha, J. Zhang, Y. Huang, T. Ghirmai, M. F. Bugallo and J. Míguez, “Particle filter,” IEEE Signal Processing Magazine, 19-38, September, 2003.
  83. Y. Huang and P. M. Djurić, “Variable selection by perfect sampling,” EURASIP Journal of Applied Signal Processing, no. 1, pp. 38-45, January, 2002.
  84. Y. Huang and P. M. Djurić, “Multiuser detection of synchronous Code-Division Multiple-Access signals by perfect sampling,” IEEE Transactions on Signal Processing, vol. 50, no. 7, pp. 1724-1734, July, 2002
  85. P. M. Djurić, Y. Huang, and T. Ghirmai, “Perfect Sampling: A review and its applications to signal processing,” IEEE Transactions on Signal Processing, vol. 50, no. 2, pp. 345-356, February, 2002.
  86. P. M. Djurić and Y. Huang, “Estimation of a Bernoulli parameter p from imperfect trials,” IEEE Signal Processing Letters, vol. 7, no. 6, pp. 160–163, 2000.

Conference Papers

  1. Liu Z., Mock J, Huang Y, Golob E. Predicting Auditory Spatial Attention from EEG using Single- and Multi-task Convolutional Neural Networks. IEEE International Conference on Systems Man and Cybernetics 2019 (SMC’19).
  2. Panwar S, Rad P, Quarles J, Golob E, Huang Y*. A Semi-Supervised Wasserstein Generative Adversarial Network for Classifying Driving Fatigue from EEG signals. IEEE International Conference on Systems Man and Cybernetics 2019 (SMC’19).
  3. Panwar S, Rad P, Quarles J, Huang Y*. Generating EEG signals of an RSVP Experiment by a Class Conditioned Wasserstein Generative Adversarial Network, IEEE International Conference on Systems Man and Cybernetics 2019 (SMC’19).
  4. Liu, Z., Mock, J. R., Huang, Y., & Golob, E. (2019). Predicting Auditory Spatial Attention from EEG using Single- and Multi-task Convolutional Neural Networks. 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC). IEEE. http://dx.doi.org/10.1109/smc.2019.8913910
  5. Nayak, T., Ko, L.-W., Jung, T.-P., & Huang, Y.* (2019). Target Classification in a Novel SSVEP-RSVP Based BCI Gaming System. 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC). IEEE. http://dx.doi.org/10.1109/smc.2019.8914174
  6. Hasib, M. M., Lybrand, Z., Estevez, V. N., Hsieh, J., & Huang, Y*. (2019). Charactering hESCs Organoids from Electrical Signals with Machine Learning. 2019 IEEE EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL & HEALTH INFORMATICS (BHI’19).
  7. Mostavi, S. Salekin, Y. Huang, “Deep-2-O-Me: Predicting 2-O-methylation sites by Convolutional Neural Networks,Engineering in Medicine and Biology Society (EMBC), 2018 40th Annual International Conference of the IEEE. 2018.
  8. A. Corley, and Y. Huang “Deep EEG Super-resolution: Upsampling EEG Spatial Resolution with Generative Adversarial Networks,” IEEE Biomedical and Health Informatics and the Body Sensor Networks Conference, Las Vegas, March 4-7, 2018
  9. Lee, and Y. Huang, “Generating Target/non-Target Images of an RSVP Experiment from Brain Signals in by Conditional Generative Adversarial Network,” IEEE Biomedical and Health Informatics and the Body Sensor Networks Conference, Las Vegas, March 4-7, 2018
  10. Hasib, M.M., T. Nayak, and Huang. “A hierarchical LSTM model with attention for modeling EEG non-stationarity for human decision prediction. in Biomedical & Health Informatics (BHI)”, IEEE Biomedical and Health Informatics and the Body Sensor Networks Conference, Las Vegas, March 4-7, 2018
  11. Salekin, S., Zhang, J., & Huang, Y. (2017). A deep learning model for predicting transcription factor binding location at single nucleotide resolution. Biomedical & Health Informatics (BHI), 2017 IEEE EMBS International Conference on (pp. 57–60).
  12. M. Hajinoroozi, J. Zhang, Y. Huang, “Driver’s Fatigue Prediction by Deep Covariance Learning from EEG,”  IEEE SMC, 2017
  13. M. Hajinoroozi, J. Zhang, Y. Huang, “Prediction of Fatigue-Related Driver Performance from EEGData by Deep Riemannian Model,” Engineering in Medicine and Biology Society (EMBC), 2017 39th Annual International Conference of the IEEE. IEEE, 2017.
  14. M. Hajinoroozi, Z. Mao, Y. Huang, “Deep Transfer Learning for Cross-Subject and Cross-Experiment Prediction of Image Rapid Serial Visual Presentation Events from EEG Data,” Human Computer Interface Conference, 2017
  15. Nayak, Tapsya, et al. “Prediction of temperature induced office worker’s performance during typing task using EEG.” Engineering in Medicine and Biology Society (EMBC), 2017 39th Annual International Conference of the IEEE. IEEE, 2017.
  16. Mao, Z., Yao, W., and Huang, Y. “Design Convolutional Neural Networks for prediction of image rapid serial visual presentation events,” Engineering in Medicine and Biology Society (EMBC), 2017 39th Annual International Conference of the IEEE. IEEE, 2017.Z.
  17. Mao, W. Yao, Y. Huang, “EEG-based Biometric Identification with Deep Learning,” IEEE Neural Engineering Conference, May 2017.
  18. L. M. Meriño, T. Nayak, G. Hall, D. J. Pack, Y. Huang. Detection of control or idle state with a likelihood ratio test in asynchronous SSVEP-based brain-computer interface systems, IEEE EMBC’2016, Orlando, FL, Aug. 2016
  19. Mao, Z., Jung, T.-P., Lin, C.-T., & Huang, Y. (2016). Predicting EEG Sample Size Required for Classification Calibration. International Conference on Augmented Cognition (pp. 57–68).
  20. M. Hajinoroozi, Z. Mao, T-P. Jung, C-T Lin, and Y. Huang, Feature extraction with deep belief networks for drivers cognitive states prediction from EEG data. IEEE China Signal Processing Summit, 2015.
  21. M. Hajinoroozi, Z. Mao, and Y. Huang, Prediction of Driver’s Drowsy and Alert States From EEG Signals with Deep Learning. IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing
  22. Z. Mao, V. Lawhern, L. M. Merino, K. Ball, L. Deng, B. J. Lance, K. Robbins, Y. Huang, Classification of non-time-locked rapid serial visual presentation events for brain-computer interaction using deep learning, in 2014 IEEE China Summit & International Conference on Signal and Information Processing (ChinaSIP), Xi’an, China, 2014, pp. 520-524.
  23. X. Cui, J. Meng, M, Rao, Y. Chen, Y. Huang, “Differential Analysis of RNA Methylation Sequencing Data,” IEEE GlobalSIP, Austin, TX, Dec 3-5, 2013
  24. S. Ahmed, L. Merino, J. Meng, K. Robbins, Y. Huang, “A Deep Learning method for Classification of images RSVP events with EEG data,” IEEE GlobalSIP, Austin, TX, Dec 3-5, 2013
  25. M. Flores, Y. Huang, Y. Chen, “NETCERNA: an algorithm for construction of phenotype-specific regulation networks via competing endogenous RNAs,” 2013 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS 2013), Houston, TX, Dec, 2013
  26. X. Cui, J. Meng, M, Rao, Y. Chen, Y. Huang, “HEP: An HMM-based Exome Peak-finding Package for RNA Epigenome Sequencing Data,” 2013 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS 2013), Houston, TX, Dec, 2013
  27. Zijing Mao, Tim H-M Huang, Yidong Chen, and Yufei Huang, “BIMMER: A Bi-Layer Hidden Markov Model for Differential Methylation Analysis” IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Shanghai, China, Dec. 2013.
  28. Lin Zhang, Hui Liu, Jia Meng, Xuesong Wang, Yidong Chen, and Yufei Huang, “Integration of gene expression, genome wide DNA methylation, and gene networks for clinical outcome prediction in ovarian cancer,” IEEE BIBM, Shanghai, China, Dec. 2013
  29. L. Merino, J. Meng, S. Gordon, B. Lance, T. Johnson, V. Paul, J. M. Vettel, K. A. Robbins, and Y. Huang, “A Bag-Of-Words Model For Task-Load Prediction From EEG in Complex Environments,” The IEEE International Conference on Acoustics, Speech, and Signal Processing, May 2013.
  30. J. Meng, L. Merino, K. A. Robbins, and Y. Huang, “Classification of EEG Recordings Without Perfectly Time-Locked Events,” The IEEE International Workshop on Statistical Signal Processing, Ann Arbor, Michigan, Aug. 2012.
  31. J. Meng, L. Merino, K. A. Robbins, and Y. Huang, “Exploiting Correlated Discriminant Features in Time Frequency and Space for Characterization and Robust Classification of Image RSVP Events with EEG Data,” The IEEE International Workshop on Statistical Signal Processing, Ann Arbor, Michigan, Aug. 2012.
  32. Y. Dong, Y. Chen, S-J Gao, Y. Huang, “Computational Prediction of microRNA Regulatory Pathways,” IEEE Workshop Genomic Signal Processing and Statistics, Dec., 2011.
  33. L. Zhang, J. Meng, H. Liu, Y. Huang, “Clustering DNA methylation expressions using nonparametric beta mixture model,” IEEE Workshop Genomic Signal Processing and Statistics, Dec., 2011.
  34. J. Meng, Y. Chen, J. Zhang, Y Huang, “Uncover transcription factor mediated gene regulations using Bayesian nonnegative factor models,” (invited) IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC 2011), Xi’an China, Sept. 2011, DOI 10.1109/ICSPCC.2011.6061806
  35. J. Meng, Y. Chen, J. Zhang, Y Huang, “Bayesian Non-Negative Factor Analysis for Reconstructing Transcriptional Regulatory Network,” (invited) The IEEE International Workshop on Statistical Signal Processing 2011, Nice, France, July 2011, DOI 10.1109/SSP.2011.5967704
  36. J. Meng, Y. Chen, J. Zhang, Y Huang, “’Uncover cooperative gene regulations by microRNAs and transcription factors in glioblastoma using a nonnegative hybrid factor model,” IEEE International Conference on Acoustics, Speech, and Signal Processing, Prague, Czech, May 2011, p.6012 – 6015, DOI: 10.1109/ICASSP.2011.5947732
  37. C. Ma, H. Chen, Y. Huang, Y. Chen, “Constructing a Compound Mode-of-Action Network for Personalized Drug Effectiveness Prediction,” IEEE International Conference on Bioinformatics & Biomedicine, December, 2010.
  38. D. Yue, Y. Chen, S. Gao and Y. Huang, “Computation Prediction of microRNA Regulatory Pathways”, IEEE International Conference on Bioinformatics & Biomedicine, December, 2010.
  39. J. Meng, J. Zhang, Y. Qi, Y. Chen, Y. Huang, “Uncovering Transcription Regulatory Networks by Iterative Conditional Mode,” IEEE Workshop Genomic Signal Processing and Statistics, Nov., 2010.
  40. J. Meng, J. Zhang, Y. Qi, Y. Chen, Y. Huang, “Uncovering Transcription Regulatory Networks by Sparse Bayesian correlated factor model,” IEEE International Conference on Signal Processing, Beijing, Nov., 2010.
  41. D. Yue, H. Liu, M. Lu, P. Chen, Y. Chen, Y. Huang, “A Bayesian Decision Fusion Approach for microRNA Target Prediction,” ACM International conference on Bioinformatics and Computational Biology, August, 2010.
  42. H. Liu, S-J Gao, Y. Huang, “A Bayesian Approach for Identifying miRNA Targets by Combining Sequence Prediction and Expression Profiling,” International Joint Conferences on Bioinformatics, Systems Biology and Intelligent Computing, Shanghai, China, 2009
  43. H. Liu, D. Yue, Y. Chen, S-J Gao, Y. Huang, “Improving Performance of Mammalian MicroRNA Target Prediction,” the proceedings of IEEE Workshop Genomic Signal Processing and Statistics, June, 2009
  44. J. Zhang, W. Haskins, Y. Huang, “Statistical modeling of LC/MS peaks”, Poster presented at the 11th RCMI International Symposium on Health Disparities, December, 1-4, 2008, Honolulu, Hawaii.
  45. H. Liu, D. Yue, S-J. Gao, Y. Huang, “A machine learning algorithm for microRNA target identification,” IEEE Workshop on Genomic Signal Processing and Statistics, June, 2008.
  46. J. Meng, S-J Gao, Y. Huang “Iterative signature clustering for analyzing time series microarray data,” IEEE Workshop on Genomic Signal Processing and Statistics, June, 2008.
  47. Hui Liu, Dong Yue, Lin Zhang, Yufei Huang, “A SVM Based Approach for miRNA Target Prediction,” ICMLC, July 2008
  48. J. Meng, J. Zhang, Y. Huang, “ Finding Time Varying Transcription Modules through Time Series Microarray Data,”, ICMLC, July 2008
  49. Lin Zhang; Jianqiu Zhang; Xiao-Bo Zhou; Hong-Hui Wang; Yufei Huang; Hui Liu; Wong, S. “Feature selection and classification of prO-TOF data based on soft information, Proceedings of International Conference on Machine Learning and Cybernetics, Volume: 7 Date: 12-15 July 2008 , Page(s): 4018-4023.
  50. Jianqiu Zhang, Honghui Wang, Anthony Suffredini, Denise Gonzales, Elias Gonzalez, Yufei Huang, Xiaobo Zhou , “Bayesian Peak Detection for pro-TOF MS MALDI Data,” IEEE International Conference on Acoustics, Speech and Signal Processing, April 2008
  51. L. Zhang, J. Zhang, Y. Huang, X. Zhou, “A Novel Feature Selection and Disease Classification Algorithm Using Probabilistic Information of Peptide Peaks in MALDI proTOF data”,  Poster presented at American Society of Mass Spectrometry Conference, Denver, June 1–5, 2008
  52. Yufei Huang, Yufang Yin, and Jianqiu Zhan, Bayesian integration of Microarray data for uncovering gene networks., IEEE Workshop on Statistical Signal Processing, Sept, 2007
  53. Travis J. Hestilow, James R. Perez, Yufei Huang, Clustering Of Gene Expression Data Based On Shape Similarity,” June, International Conference on Biocomp, June, 2007
  54. Jia Meng, Jianqiu Zhang , Yufei Huang, Bayesian meta-clustering of cancer microarray data,” IEEE Workshop on Statistical Signal Processing, Sept, 2007
  55. Y. Yin, Y. Huang, V. Shanmugam, M. Brun and Edward R. Dougherty, A Bayesian Approach for Uncovering Gene Network Motifs,” IEEE Workshop on Genomic Signal Processing and Statistics, 2007.
  56. Y. Jin and Y. Huang, Adaptive control and stability analysis of genetic networks with SUM regulatory,” Artificial Neural Networks In Engineering Conference, 2006.
  57. Y. Yin and Y. Huang, Turbo data integration for uncovering gene networks,” IEEE/NLM Life Science Systems and Applications Workshop, 2006
  58. I. T Luna , Y. Yin, Y. Huang, D. P. R. Padillo, M. C. C. Perez, Uncovering gene regulatory regulatory networks using variational Bayes variable selection,” IEEE International Workshop on Genomic Signal Processing and Statistics, May, 2006.
  59. J. Wang, Y. Huang, J. Zhang, M. Sanchez, and Y. Wang, Reverse engineering yeast gene regulatory networks using graphical models,” IEEE International Conference on Acoustics, Speech, and Signal Processing, 2006
  60. T. Wei, Y. Huang and P. Chen, Particle Filtering for Adaptive Sensor Fault Detection and Identification,” IEEE International Conference on Robotics and Automation, 2006
  61. T. Wei, Y. Huang and P. Chen, Sensor validation for flight control by particle filtering, (Invited), European Signal Processing Conference, Sept. 2005
  62. T. J. Hestilow, T. Wei and Y. Huang, Sensor scheduling and target tracking using expectation propagation,” IEEE Workshop on Statistical Signal Processing, July 2005
  63. M. Tienda-Luna, D. P. Ruiz, M. C. Carrion, and Y. Huang Iterative decoding in Factor Graph representation using Particle Filtering,” IEEE workshop on Signal Processing Applications in Wireless Communications, New York, June, 2005
  64. Y. Huang, Y. Wang, J. Wang and J. Zhang, Bayesian Inference of Cell Cycle Regulatory Networks,” IEEE/EURASIP International Workshop on. Genomic Signal Processing and Statistics, May, 2005
  65. Y. Huang, Y. Yin and J. Zhang, Belief-Directed Sequential Probabilistic Data Association Multiuser Detector,” IEEE International Conference on Acoustics Speech and Signal Processing, March, 2005.
  66. T. Wei, Y. Huang and Y. Qi, Symbol Detection with Time-varying Unknown Phase by Expectation Propagation,” IEEE International Conference on Acoustics Speech and Signal Processing, March, 2005.
  67. Y. Yin, Y. Huang, and J. Zhang, Joint symbol detection and timing estimation with Stochastic M-algorithm,” in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, 2004
  68. Y. Huang, and J. Zhang, A Generalized Probabilistic Data Association Multiuser Detector for CDMA systems,” in Proceedings of the IEEE International Symposium on Information Theory , June, 2004
  69. Y. Yin, Y. Huang and J. Zhang, Turbo Equalization using Probabilistic Data Association, in Proceedings of IEEE Globecom, Dec. 2004
  70. Y. Huang, J. Zhang, I. T. Luna, P. M. Djuriic and D. P. R. Padillo, Adaptive Blind Multiuser Detection over Flat Fast Fading Channels using Particle Filtering, in Proceedings of IEEE Globecom, Dec. 2004
  71. Y. Huang and J. Zhang, Lower bounds on the variance of deterministic signal parameter estimators using Bayesian inference,” in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, April, 2003.
  72. Y. Huang, J. Zhang and P. M. Djuric, Detection with particle filtering in BLAST systems,” in Proceedings of the IEEE International Conference on Communications, May, 2003.
  73. Y. Huang, P. M. Djuric, and J. Zhang, Joint velocity estimation and symbol detection in non-stationary fading channels by particle filtering,” in Proceedings of the IEEE CLOBECOM conference, Dec, 2003.
  74. Y. Huang and P. M. Djuric, A new importance function for particle ¯filtering and its application to blind detection in °at fading channels,” in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, 2002.
  75. Y. Huang and P. M. Djuric, A blind particle ¯filtering detector for joint channel estimation, tracking and data detection over at fading channels,” in Proceedings of European Signal Processing Conference, 2002.
  76. J. Zhang, Y. Huang and P. M. Djuric, Multiuser detection with particle filtering, in Proceedings of the European Signal Processing Conference, 2002.
  77. P. M. Djuric, J. Zhang, T. Ghirmai, Y. Huang, and J. Kotecha, Applications for Particle ¯filtering to communications: A review,” in Proceedings of the European Signal Processing Conference, 2002.
  78. J. Zhang, Y. Huang and P. M. Djuric, Joint channel estimation and multiuser detection using particle filtering,” in Proceedings of the Conference on Information Sciences and Systems, 2002.
  79. Y. Huang and P. M. Djuric, Multiuser detection of synchronous Code-Division Multiple-Access signals by the Gibbs coupler,” in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Salt Lake City, UT, 2001.
  80. Y. Huang and P. M. Djuric, Variable selection by perfect sampling,” in Proceedings of the IEEE – EURASIP Workshop on Nonlinear Signal and Image Processing, Baltimore, MD, 2001.
  81. Y. Huang and P. M. Djuric, A new perfect sampling algorithm on binary state spaces and its applications to signal processing (invited),” in Proceedings of the ICSA Applied Statistics Symposium, Chicago, IL, 2001.
  82. Y. Huang, T. Ghirmai and P. M. Djuric, The rejection Gibbs coupler: A perfect sampling algorithm and its application to truncated multivariate Gaussian distributions,” in Proceedings of the 11th IEEE Workshop on Statistical Signal Processing, 2001.
  83. Y. Huang and P. M. Djuric, Bayesian detection of transient signals in colored noise,” in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Istanbul, Turkey, 2000.
  84. Y. Huang and P. M. Djuric, Choosing priors for an important class of signal processing problems,” in Proceedings of the European Signal Processing Conference, Tampere, Finland, 2000.
  85. P. M. Djuric and Y. Huang, Estimation of probability of events from imperfect Bernoulli trials,” in Proceedings of the SPIE International Symposium on Mathematical Modeling, Bayesian Estimation, and Inverse Problems, Denver, CO, 1999, vol. 3816, pp. 68{76}.
  86. Y. Huang and P. M. Djuric, A Bayesian approach to direction-of-arrival estimation of coherent signals,” in Proceedings of the IEEE Signal Processing Workshop on High Order Statistics, Caesarea, Israel, 1999, pp. 371{374}.