Prof. Yanjun Qi, PhD @ UVA (email: yanjun at virginia.edu)   

Selected Peer-Reviewed Publications

Year of Publication :
[2017] [2016] [2015] [2014] [2013] [2012] [2011] [2010] [2009] [2008] [2007] [2006] [2005] [2004] [2003] [Pre-2003]
Type of Publication :
[Theses] [Book & book chapters] [Journal] [Conference] [Workshop] [Patent] [BestPaper] [Poster]
Categorization wrt Method Types: Categorization wrt Application/Data Types:
[Text Mining, IR, NLP] [Biomedical Data Mining] [Video/Image/Sensing Data Mining] [System Data Mining]
Categorization wrt Code/Data Sharing:
[Show all] [ GoogleScholar Profile] [ Our GitSite]

 

2017

  • Beilun Wang, Ji Gao, Yanjun Qi, (2017)
    "A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models", Proceedings of The 20th International Conference on Artificial Intelligence and Statistics (AISTATS) (acceptance rate: 30% out of 530 )
    information fusion + Gaussian graphical model + large-scale learning
(Arxiv) (PDF) (Poster) (Code) (Rpage)
  • Beilun Wang, Ritambhara Singh, Yanjun Qi, (2017)
    "A constrained L1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models", (Machine Learning) Journal
    graphical model + interpretable model + biomedical application
(Arxiv) (PDF) (Poster) (Code) (Rpage)
  • Jack Lanchantin, Ritambhara Singh, Beilun Wang, Yanjun Qi, (2017)
    "Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks", the Proceedings of Pacific Symposium on Biocomputing (PSB-17 (Talk/Proceeding))
    deep learning + structured prediction + interpretable model + biomedical application
(PDF) (Talk) (Arxiv) (Code) (Data)
  • Ritambhara Singh, Arshdeep Sekhon, Kamran Kowsari, Jack Lanchantin, Beilun Wang, Yanjun Qi (2017)
    "GaKCo: a Fast GApped k-mer string Kernel using COunting",
    string kernel + fast/scalable/parallel machine learning + biomedical application
(Arxiv) (Code)
  • Beilun Wang, Ji Gao, Yanjun Qi, (2017)
    "A Theoretical Framework for Robustness of (Deep) Classifiers Against Adversarial Examples", the International Conference on Learning Representations , (ICLR-17 (workshop track))
    representation learning + adversarial machine learning
(Arxiv) (Poster) (PDF)
  • Weilin Xu, David Evans, Yanjun Qi, (2017)
    "Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks",
    adversarial machine learning + deep learning
(Arxiv) (Code)
  • Ji Gao, Beilun Wang, Zeming Lin, Weilin Xu, Yanjun Qi, (2017)
    "DeepCloak: Masking Deep Neural Network Models for Robustness against Adversarial Samples", the International Conference on Learning Representations , (ICLR-17 (workshop track))
    representation learning + adversarial machine learning
(Arxiv) (Poster)
  • Jack Lanchantin, Ritambhara Singh, Yanjun Qi, (2017)
    "Memory Matching Networks for Genomic Sequence Classification", the International Conference on Learning Representations , (ICLR-17 (workshop track))
    representation learning + genomic sequence mining
(Arxiv) (Poster)

 

2016

  • Ritambhara Singh, Jack Lanchantin, Yanjun Qi, (2016)
    "DeepChrome: Deep-learning for predicting gene expression from histone modifications", 15th European Conference on Computational Biology , (ECCB-16 ) (Bioinformatics (2016) 32 (17): i639-i648.)
    deep learning + epigenomics + interpretable model + biomedical application
(PDF) (Slide) (Arxiv) (Online) (Code)
(Web)
  • Zeming Lin, Jack Lanchantin, Yanjun Qi, (2016)
    "MUST-CNN: A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-based Protein Structure Prediction", the 30th AAAI Conference on Artificial Intelligence , ((AAAI-16))   (Talk / Paper acceptance rate: 26% )
    deep learning + structured prediction + biomedical application
(PDF) (Talk) (online) (Arxiv) (Code)
(Data)
  • Ritambhara Singh, Jack Lanchantin, Gabriel Robins, Yanjun Qi, (2016)
    "Transfer String Kernel for Cross-Context DNA-Protein Binding Prediction", IEEE/ACM Transactions on Computational Biology and Bioinformatics (Journal), (TCBB)
    string kernel + transfer learning + biomedical application
(PDF) (Talk) (online) (Arxiv) (Code)
  • Feiyu Xiong; Moshe Kam ; Leon Hrebien; Beilun Wang; Yanjun Qi, (2016)
    "Kernelized Information-Theoretic Metric Learning for Cancer Diagnosis using High-Dimensional Molecular Profiling Data", ACM Transactions on Knowledge Discovery from Data (Journal), (TKDD)
    metric learning + Kernel method + biomedical application
(PDF) (Online) (Code)
(Data)
  • Ritambhara Singh, Yanjun Qi, (2016)
    "Character based String Kernels for Bio-Entity Relation Detection", the Proceedings of ACL - 15th Workshop on Biomedical Natural Language Processing , (BIONLP 2016)
    character-based representation + string kernel + text classification + biomedical application
(PDF) (Talk) (Online) (Code)
  • Jack Lanchantin, Ritambhara Singh, Zeming Lin, Yanjun Qi, (2016)
    "Deep Motif: Visualizing Genomic Sequence Classifications", the International Conference on Learning Representations , (ICLR-16 (workshop track))
    deep learning + structured prediction + interpretable model + biomedical application
(PDF) (Arxiv) (Online) (Code) (Data)
  • Beilun Wang, Ritambhara Singh, Yanjun Qi, (2016)
    "A constrained L1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models", the International Conference on Machine Learning , (Computational Biology Workshop )
    graphical model + interpretable model + biomedical application
(Arxiv) (Code) (Data)
  • Weilin Xu, Yanjun Qi, David Evans (2016)
    "Automatically Evading Classifiers", Proceedings of the Network and Distributed System Security Symposium, , ((NDSS-16))   (Talk / Paper acceptance rate: 60/389=15%)
    machine learning + security
(PDF) (Talk) (Code)
  • Sarah Mohamed, Nick Janus, Yanjun Qi, (2016)
    "SCODE: A Cytoscape app for supervised complex detection in protein-protein interaction graphs", (F1000Research)
    subgraph search + biomedical application
(Online) (Code) (Data)
  • Jiaqi Gong, Yanjun Qi, Myla Goldman, John Lach (2016)
    "Causality Analysis of Inertial Body Sensors for Multiple Sclerosis Diagnostic Enhancement", IEEE Journal of Biomedical and Health Informatics , , ((BHI))   (Journal Impact Factor 2.093)
    time series segmentation / casual analysi + sensor data
(Online)
  • Jiaqi Gong, Philip Asare, Yanjun Qi, John Lach (2016)
    "Piecewise Linear Dynamical Model for Action Clustering from Real-World Deployments of Inertial Body Sensors", IEEE Transactions on Affective Computing, , ((TAFFCSI))   (Journal Impact Factor 2.675)
    time series segmentation / clustering + sensor data
(Online)
  • InKee Kim, Wei Wang, Yanjun Qi and Marty Humphrey, (2016)
    "Empirical Evaluation of Workload Forecasting Techniques for Predictive Cloud Resource Scaling", IEEE (CLOUD)   (Talk / Paper acceptance rate: 15%)
    Supervised Local Learning approach + system-level data about cloud system
(PDF) (Talk)
  • Babiceanu, Mihaela; Qin, Fujun; Xie, Zhongqiu; Jia, Yuemeng; Lopez, Kevin; Janus, Nick; Facemire, Loryn; Kumar, Shailesh; Pang, Yuwei; Qi, Yanjun; Lazar, Luliana; Li, Hui, (2015)
    "Recurrent Chimeric Fusion RNAs in Non-Cancer Tissues and Cells", Nucleic Acid Research (Journal), (NAR) (Impact Factor 8.808)
    graph mining + novel biomedical application
(Online)

 

2015

  • Sarah Masud Preum, John Stankovic, Yanjun Qi (2015)
    "MAPer: A Multi-scale Adaptive Personalized Model for Temporal Human Behavior Prediction ", The 24th ACM International Conference on Information and Knowledge Management (CIKM 2015)  (long paper acceptance rate: 18% = 87/484)
    Feature learning + Time series prediction
(PDF) (Talk) (Data)
  • Ritambhara Singh; Gabriel Robins; Yanjun Qi; (2015)
    "Transfer String Kernel for Cross-Context Transcription Factor Binding Prediction", the 14th International Workshop on Data Mining in Bioinformatics (BioKDD'15)
    Sequence Labeling + Kernel + Transfer Learning
(PDF) (Talk) (online) (Arxiv) (Code)
  • Ke Wang, Yanjun Qi, Jeffrey J. Fox, Mircea R. Stan, Kevin Skadron, (2015)
    "Association Rule Mining with the Micron Automata Processor", 29th IEEE International Parallel & Distributed Processing Symposium (IPDPS15)
    Data mining + Hardware acceleration
(PDF) (Talk)
  • Oznur Tastan, Yanjun Qi, J.G. Carbonell, Judith Klein-Seetharaman, (2015)
    "Refining Literature Curated Protein Interactions Using Expert Opinions", The 20th Pacific Symposium on Biocomputing (PSB) , (PSB15)
    Crowd sourcing + Network Biology
(PDF) (Talk) (Data)
  • Ritambhara Singh; Cem Kuscu; Aaron Quinlan; Yanjun Qi; Mazhar Adli, (2015)
    "Cas9-Chromatin binding information enables more accurate CRISPR Off-target Prediction", Nucleic Acid Research (Journal), (NAR) (Impact Factor 8.808)
    sequence labeling + novel biomedical application
(Online) (PDF)
  • Jiaqi Gong, John Lach, Yanjun Qi, MD. Goldman, (2015)
    "Causal Analysis of Inertial Body Sensors for Enhancing Gait Assessment Separability towards Multiple Sclerosis Diagnosis", IEEE Body Sensor Network (BSN) 2015 ,
    Casual analysis + Temporal data
(PDF) (Talk)
  • Weilin Xu, Yanjun Qi, David Evans, (2015)
    Poster: "Automatically Evading Classifiers", 36th IEEE Symposium on Security and Privacy (SP2015)
    Secure machine learning
(PDF)
  • Chunkun Bo, Ke Wang, Yanjun Qi, Kevin Skadron (2015)
    Poster: "String kernel testing acceleration using the Micron Automata Processor", Workshop on Computer Architecture for Machine Learning
    Scaling up machine learning with hardware
(PDF)
(url)

 

2014

  • Y. He, K. Kavukcuoglu, Y. Wang, A. Szlam, Y. Qi (2014)
    "Unsupervised Feature Learning by Deep Sparse Coding", SIAM 2014 International Conference on Data Mining (SDM2014) (acceptance rate: 29% (120 out of total 389 submissions) )
    Unsupervised feature learning (Also presented and featured as the only three talks at ICLR workshop 2014.)
(PDF) (Arxiv) (Talk)
  • Y. Qi, S. Das, R. Collobort and J. Weston (2014)
    "Deep Learning for Character-based Information Extraction", The European Conference on Information Retrieval (ECIR2014)
    Deep learning + Sequence Labeling
(PDF) (Web) (Talk)
  • J. Gong, P. Asare, J. Lach, Y. Qi, (2014)
    "Piecewise Linear Dynamical Model for Actions Clustering from Inertial Body Sensors with Considerations of Human Factors", BodyNets 2014 : 9th International Conference on Body Area Networks (BodyNets2014)
    (Best Paper Award !) Temporal Data Segmentation + sensor data + Human activity Recognition
(PDF) (Talk)
  • F. Xiong, M. Kam, L. Hrebien, and Y. Qi (2014)
    "Ranking with Distance Metric Learning for Biomedical Severity Detection", SIAM 2014 International Conference on Data Mining (SDM2014), 3rd Workshop on Data Mining for Medicine andHealthcare (DMMH)
    Distance metric learning + Biomedical Severity
(PDF)
  • S. Das, Y. Qi, P. Mitra, and L. Giles (2014)
    "Extracting Metadata from Academic Homepages using Labeled Features", SIAM 2014 International Conference on Data Mining (SDM2014) (acceptance rate: 29% (120 total 389 submissions) )
    Semi-supervised feature learning + Web text mining
(PDF) (posterSlide)
  • R. Min, S.A. Chowdhury, Y. Qi, A. Stewart, R. Ostroff (2014)
    "An integrated approach to blood-based cancer diagnosis and biomarker discovery", The 19th Pacific Symposium on Biocomputing (PSB) 19:87-98 , (PSB14)
    Supervised Feature Learning + Genomic data
(PDF)
  • I.K. Kim, J. Steele, Y. Qi and M. Humphrey, (2014)
    "Comprehensive Elastic Resource Management to Ensure Predictable Performance for Scientific Applications on Public IaaS Clouds", IEEE/ACM 7th International Conference on Utility and Cloud Computing (UCC)
    Supervised Local Learning approach + system-level data about cloud system
(PDF)
  • MR Min, X. Ning, Y. Qi, C. Cheng, A. Bonner and M. Gerstein (2014)
    "Ensemble Learning Based Sparse High-Order Boltzmann Machine for Unsupervised Feature Interaction Identification", NIPS Machine Learning for Computational Biology (MLCB) workshop
    Sparse learning + Structure Learning in Graphical Model
(PDF)

 

2013

  • R. Min and Y. Qi (2013)
    Sparse high-order boltzmann machine for identifying combinatorial interactions between transcription factors, US Patent App. 13/908,715, 2013 ,
    Learning Conditional Dependency among Variables + Network biology
(URL)
  • Y. He, Y. Qi, K. Kavukcuoglu (2013)
    Latent Factor Dependency Structure Determination, US Patent 20,130,091,081, 2013
    Learning Conditional Dependency among Latent Variables + Image/Text data
  • Y. Qi and B. Bai (2013)
    Document Classification with Weighted Supervized n-gram Embedding, US Patent 20,120,310,627, 2013
    Deep learning for n-gram embedding + Text Language data
  • X. Chen, Y. Qi and B. Bai (2013)
    System and methods for finding hidden topics of documents and preference ranking documents, US Patent 20,120,323,825 (Grant)
    Sparse Learning + Text Ranking

 

2012

  • Y. Qi, and P. Laquerre (2012)
    Retrieving Medical Records with sennamed: NEC Labs America at TREC 2012 Medical Record Track, Proceedings of the 2012 Text Retrieval Conference
    We won the 2nd place among all 82 TREC Medical Track Automatic submissions. Ranks at the 3rd place among all TREC Medical submissions (82 automatic + 6 manual)
(PDF), (bibTex)
  • Y. Qi, M. Osh, J. Weston, W. Noble (2012)
    A unified multitask architecture for predicting local protein properties, PLoS ONE (March 2012) (Impact Factor 4.411)
    Deep neural network architecture + Protein Sequence Labeling
(PDF), (Online), (bibTex), (Site), (Data)
  • Y. He, Y. Qi, K. Kavukcuoglu, H. Park (2012)
    "Learning the Dependency Structure of Latent Factors", NIPS 2012(online) (acceptance rate: 25% (=370/1467 submissions) )
    Latent Factor Model + Sparse Gaussian Graphical Model
    *ERRATUM*: the equation under Eq(13) has missed B^T. Issue fixed in the version here.
(PDF) (bibTex) (Talk) (Poster) (Code)
  • Y. Qi  (2012) 
    Random Forest for Bioinformatics, Invited chapter in Springer book: "Ensemble Learning: Methods and Applications" -> (book OnlineLink),
    Review of recent efforts related to random forest in bioinformatics.
(Chapter-PDF) (bibTex) (Code)
  • D. Bespalov, Y. Qi, B. Bai, A. Shokoufandeh, (2012)
    "Sentiment Classification with Supervised Sequence Encoder",The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) (ECML2012) (acceptance rate: 23% (total 443 submissions) )
    Deep Learning + ngram embedding + sentiment classification.
    *ERRATUM*: Old data sharing site "http://mst.cs.drexel.edu/datasets/ECML2012" not working anymore. Now we put data @ (DataSharing)
(PDF) (bibTex) (Talk) (Data)
  • D. Bespalov, Y. Qi, B. Bai, A. Shokoufandeh, (2012)
    "Large-scale Image Classification Using Supervised Spatial Encoder",the twenty-first conference of the International Association for Pattern Recognition (ICPR2012) (acceptance rate: )
    Deep Learning + fast large-scale spatial pooling + image classification.
(PDF) (bibTex) (Talk)
  • Ilia Nouretdinov, Alex Gammerman, Y. Qi, J. Klein-Seetharaman (2012)
    "Determining confidence of predicted interactions between HIV-1 and human proteins using conformal method", 17th Pacific Symposium on Biocomputing (PSB) Jan 2012,
    Conformal Prediction + Network biology
(PDF)(bibTex)
  • D. Bespalov, Y. Qi, B. Bai, (2012)
    Sentiment Classification Based on Supervised Latent n-gram Analysis, US Patent 20,120,253,792, 2012
    Deep Learning + ngram Embedding + Sentiment Classification
(bibTex)

 

2011

  • Y. Qi , W. Noble  (2011) 
    Protein Interaction Networks: Protein Domain Interaction and   Protein Function Prediction , Invited chapter in book: Handbook of Computational Statistics: Statistical Bioinformatics -> (book OnlineLink),
    Network biology + Relational data mining
(Chapter-PDF) (bibTex)
  • X. Chen, Y. Qi, B. Bai, Q. Lin, J.G. Carbonell (2011)
    "Sparse Latent Semantic Analysis", SIAM 2011 International Conference on Data Mining (SDM2011) (acceptance rate: 25% = 86/343)
    Spare Learning + Factor Model + Text topic modeling
(PDF) (Talk) (bibTex) (Code)
  • X. Ning, Y. Qi (2011)
    "Semi-Supervised Convolution Graph Kernels for Relation Extraction", SIAM 2011 International Conference on Data Mining (SDM2011) (acceptance rate: 25% = 86/343)
    Graph Kernel + Semi-supervised Learning + Relation Extraction from Text
(PDF) (bibTex) (Talk)
  • D. Bespalov, B. Bai, Y. Qi, A. Shokoufandeh (2011)
    "Sentiment Classification Based on Supervised Latent n-gram Analysis",20th ACM Conference on Information and Knowledge Management (CIKM2011) (acceptance rate: as full paper 15% out of 917)
    Deep Learning + Latent ngram embedding + Sentiment classification
    *ERRATUM*: Old data sharing site "http://mst.cs.drexel.edu/datasets/CIKM2011" not working anymore. Now we put data @ (DataSharing)
(PDF) (bibTex) (Talk) (Data)
  • Y. He, K. Kavukcuoglu, Y. Qi, H. Park (2011)
    "Structured Latent Factor Analysis",NIPS 2011 Workshop: Challenges in Learning Hierarchical Models: Transfer Learning and Optimization
    Sparse Gaussian Graphical Modeling among Latent Factors
(online)(PDF)
  • Y. Qi, X. Ning, P. Kuksa, B. Bai, (2011)
    Systems and methods for semi-supervised relationship extraction, US Patent App. 13/078,985, 2011
    Semi-supervised learning + Relationship extraction
(bibTex)

 

2010

  • Y. Qi, O.Tastan, J. Carbonell, J. Klein-Seetharaman, J. Weston (2010)
    "Semi-Supervised Multi-Task Learning for Predicting Interactions between HIV-1 and Human Proteins", Bioinformatics 2010 (9th European Conference on Computational Biology (ECCB), Sep 2010, (Impact Factor 4.328) (acceptance rate of ECCB10: 17% = 36/215)
    Multi-task learning + Relational data + Network biology
(PDF) (Online)(Talk) (bibTex) (Code) (Data)
  • P. Kuksa,  Y. Qi (2010) 
    "Semi-Supervised Bio-Named Entity Recognition with Word-Codebook Learning" "SDM2010 (regular paper acceptance rate: 23% = 82/351)
    Feature learning + Information extraction
(PDF) (bibTex)
  • P. Kuksa, Y. Qi, B. Bai, R. Collobert, J.Weston, V. Pavlovic, X. Ning (2010)
    "Semi-Supervised Abstraction-Augmented String Kernel for Multi-Level Bio-Relation Extraction", ECML PKDD 2010 ( European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases ), (acceptance rate of ECML10: 16.7% = 110/658)
    Semi-supervisions + Mismatch string kernel framework.
(PDF)(Talk) (bibTex)
  • X. Chen, Y. Qi, B. Bai, Q. Lin, J.G. Carbonell (2010)
    "Sparse Latent Semantic Analysis", NIPS Workshop on Practical Application of Sparse Modeling: Open Issues and New Directions (2010)
    Sparse learning + Latent semantic analysis + Text data
(PDF)
  • X. Chen, B. Bai, Y. Qi, Q. Lin, J. Carbonell,(2010)
    "Learning Preferences using Millions of Features by Enforcing Sparsity" IEEE ICDM 2010 (short paper acceptance rate: 19% = (72+83)/797 )
    Sparse learning + Text ranking + Feature learning
(PDF)(Talk) (bibTex)

 

2009

  • Y. Qi, P. Kuksa, R. Collobert, K. Sadamasa, K. Kavukcuoglu, J. Weston,(2009)
    "Semi-Supervised Sequence Labeling with Self-Learned Feature" IEEE ICDM 2009 (regular paper acceptance rate: 9% = 70/786)
    Feature learning + Semi-supervised learning + Sequence labeling
(PDF)(Talk) (bibTex)
  • Y. Qi, R. Collobert, P. Kuksa, K. Kavukcuoglu, J. Weston,(2009)
    "Combining Labeled and Unlabeled Data for Word-Class Distribution" CIKM 2009  (short paper acceptance rate: 20% = (171 + 123)/847)
    Feature learning + Semi-supervised learning + Sequence labeling
(PDF) (bibTex)
  • Y. Qi, HK. Dhiman, et al, Z. Bar-Joseph, J. Klein-Seetharaman,(2009)
    "Systematic prediction of human membrane receptor interactions",x (PROTEOMICS 2009, 9, 5243-5255 (Impact Factor 5.479)
    Supervised Information integraion + Network biology + Experimental design
(Talk) (PDF) (bibTex) (Code)
(Data) (Web)
  • O. Tastan, Y. Qi, J.G. Carbonell, J. Klein-Seetharaman, (2009);
    Prediction of Interactions between HIV-1 and Human Proteins by Information Integration , Pacific Symposium on Biocomputing 14: (PSB-2009 Jan. 2009
    Supervised Information integraion + Network biology
(PDF) (bibTex) (Web) (Data)
  • B. Bai, J.Weston. D. Grangier, R. Collobert, K. Sadamasa, Y. Qi, C.  Cortes, M Mohri,(2009)
    "Polynomial Semantic Indexing"; NIPS 2009 (acceptance rate: 23% = 263/1105)
    Deep learning + Feature representation learning + Text ranking
(PDF) (bibTex)
  • B. Bai, J.Weston. D. Grangier, R. Collobert, K. Sadamasa, Y. Qi, O. Chapelle, Kilian Weinberger, (2009)
    "Supervised Semantic Indexing", CIKM 2009 (regular paper acceptance rate: 15% = 123/847)
    Deep learning + Feature representation learning + Text ranking
(PDF)(bibTex)
  • B. Bai, J.Weston. D. Grangier, R. Collobert, K. Sadamasa, Y. Qi, O. Chapelle, Kilian Weinberger, (2009)
    "Learning to Rank with (a Lot of) Word Features", the special issue on Learning to Rank of the Information Retrieval Journal.
    Deep learning + Feature representation learning + Text ranking
(PDF)(bibTex)
  • B. Bai, J.Weston. D. Grangier, R. Collobert, K. Sadamasa, Y. Qi, O. Chapelle, Kilian Weinberger,(2009)
    "Learning to Rank with Low Rank", SIGIR 09 workshop: Learning to Rank for Information Retrieval
    Deep learning + Feature representation learning + Text ranking

 

2008

  • Y. Qi, (2008) Learning of Protein Interaction Networks. Ph.D. Dissertation (also CMU Technical Report: CMU-LTI-08-06), Carnegie MellonUniversity,  School of Computer Science, May 2008
    (Dissertation Committee: Ziv Bar-Joseph, Judith Klein-Seetharaman, Christos Faloutsos, Jaime Carbonell, Baldo Oliva)
    Supervised Information integraion + Structured Prediction + Network biology
(PDF) (Talk) (bibTex)
  • Y. Qi, F. Balem, C. Faloutsos, J. Klein-Seetharaman, Z. Bar-Joseph, (2008)
    Protein Complex Identification by Supervised Graph Clustering , Bioinformatics 2008, 24(13), i250-i268 (The 16th Annual International Conference Intelligent Systems for Molecular Biology (ISMB), July 2008, (Impact Factor 4.328) (acceptance rate of ISMB08: 17% = 49/292)
    Supervised Information integraion + Structured Prediction + Network biology
(Online)(PDF) (Talk) (bibTex) (Code) (Web) (Data)
  • L. Pena-Castillo, et al, Y. Qi, et al, F.P. Roth,(2008)
    A Critical Assessment of M. Musculus Gene Function Prediction using Integrated Genomic Evidence ,  Genome Biology , 9(S1):S2, June 2008 (Impact Factor 6.15)
    Supervised Information integraion + Structured Prediction + Hierarchical Output
(Online) (bibTex)
  • H. Nozawa,G. Howell, S. Suzuki, Q. Zhang, Y. Qi, J. Klein-Seetharaman, A. Wells, J.R. Grandis, S.M. Thomas,(2008)
    Combined inhibition of PLC-1 and c-Src abrogates Epidermal Growth Factor Receptor-mediated head and neck squamous cell carcinoma invasion , Clinical Cancer Research, 14:4336-43,  2008 (Impact Factor 6.488)
    Network biology
(online) (bibTex)

 

2007

  • Y. Qi, J. Klein-Seetharaman, Z. Bar-Joseph, (2007)
    "A mixture of feature experts approach for protein-protein interaction prediction", BMC Bioinformatics 8(S10) S6, 2007 (Impact Factor 3.78)
    Supervised Information integraion + Structured Prediction + Network biology
(Online) (PDF) (bibTex) (SupportWeb)

 

2006

  • Y. Qi, Z. Bar-Joseph, J. Klein-Seetharaman,(2006) 
    "Evaluation of different biological data and computational classification methods for use in protein interaction prediction", , PROTEINS: Structure, Function, and Bioinformatics. 63(3):490-500. 2006 (Impact Factor 3.39)
    Supervised Information integraion + Structured Prediction + Network biology
((Online)) (PDF)) ((bibTex)) (Code) ((SupportWeb)

 

2005

  • Y. Qi, J. Klein-Seetharaman, Z. Bar-Joseph, (2005)
    "A mixture of experts approach for protein-protein interaction prediction", Proceedings of Neural Information Processing Systems (NIPS): The workshop on Computational Biology and the Analysis of Heterogeneous Data, Dec 2005.
    Supervised Information integraion + Structured Prediction + Network biology
(PDF), (talk PDF)(bibTex) (SupportWeb)
  • Y. Qi, J. Klein-Seetharaman, Z. Bar-Joseph, (2005)
    Random Forest Similarity for Protein-Protein Interaction Prediction from Multiple source ,Pacific Symposium on Biocomputing 10: (PSB 2005) Jan. 2005.
    Supervised Information integraion + Structured Prediction + Network biology
(PDF) (bibTex) (SupportWeb)

 

2004

  • A.G. Hauptmann, J. Gao, R. Yan, Y. Qi and J. , Yang, (2004)
    Automated Analysis of Nursing Home Observations , IEEE Pervasive Computing, Special Issue on Pervasive Computing for Successful Aging, 3(2):15-21, April-June, 2004
    Temporal data segmentation + Supervised learning + Human activity Recognition
(PDF) (bibTex)

 

2003

  • Y.Qi, A.Hauptman, T. Liu, (2003)
    "Supervised Classification for Video Shot Segmentation",  Proceeding of 2003 IEEE International Conference on Multimedia & Expo (ICME 2003), July 2003, Baltimore MD, USA.  
    Temporal data segmentation + Supervised learning
(PDF) (Talk) (bibTex)

 

Before 2003

  • Jin, R. ,Y. Qi, Hauptman,A.,(2002) 
    Probabilistic Model for Camera Zoom Detection , Proceedings of  the sixteenth conference of the International Association for Pattern Recognition (ICPR 2002 Quebec City, Canada August 11-15,2002
    UnSupervised learning + Pattern Mining in Video + density estimation + EM
(PDF) (bibTex)
  • Hauptmann, A., R.Yan, Y.Qi, Jin, R., M.Christel, M.Derthick,M.-Y.Chen, R.Baron, W.H.Lin, and T.D.Ng,(2002) 
    "Video Classification and Retrieval with the Informedia Digital Video Library System", the Eleventh Text Retrieval Conference (TREC-2002), Nov 2002 
    Supervised information integration + Video retrieval
(bibTex)
  • Hauptmann, A., Jin, R., N. Papernick, D. Ng, Y. Qi, Houghton, RThornton, S. (2001)
    Video Retrieval with the Informedia Digital Video Library System, Proceedings of the Tenth Text Retrieval Conference (TREC-2001), Nov 2001
    Supervised information integration + Video retrieval
(bibTex)

 

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