- Assistant Professor, Biosystems Informatics
- Assistant Professor, Statistics-GIDP
- Member of the Graduate Faculty
Haiquan Li received his Ph.D. in Bioinformatics and Data Mining from the National University of Singapore where he developed algorithms for pattern mining (e.g. protein binding motif pairs) and network motif mining (e.g. bipartite mining). After graduation, he joined Noble Foundation and built machine learning models for prediction and functional characterization of membrane transporters for various sequencing projects. He switched to translational bioinformatics after working at The University of Chicago as a research faculty in 2010.
His research interests focus on unveiling the cooperative biological mechanisms among single nucleotide polymorphisms associated with complex diseases, and genetic underpinning of complex disease comorbidity. His research is highly interdisciplinary, spanning personalized medicine, clinical informatics, big data, and statistics. He received a Distinguished Paper Award from the American Medical Informatics Association (AMIA) Annual Symposium 2011.
- Graduate Certificate Statistics
- University of Arizona, Tucson, Arizona, United States
- Ph.D. Computer Science in Bioinformatics and Data Mining
- National University of Singapore, Singapore, Singapore
- Efficient Discovery of Binding Motif Pairs from Protein-protein Interactions
- M. Eng. Computer Software
- Huazhong University of Science and Technology, Wuhan, Hubei, China
- B. Eng. Computer Software
- Huazhong University of Science and Technology, Wuhan, China
- University of Arizona, Tucson, Arizona (2013 - 2016)
- University of Illinois at Chicago, Chicago, Illinois (2011 - 2013)
- University of Chicago, Chicago, Illinois (2010 - 2011)
- The Samuel Roberts Noble Foundation, Inc. (2006 - 2010)
- Ipedo Shanghai Inc. (2000 - 2001)
- Best Paper Award
- 2012 Translational Bioinformatics Conference, Fall 2012
- Distinguished Paper Award
- American Medical Informatics Association, Fall 2011
Translational Bioinformatics;Personalized and Precision Medicine;Clinical Informatics;Big Data;Genomic Medicine
DissertationBE 920 (Fall 2021)
Fundamentals of ComputingBE 502 (Fall 2021)
Independent StudyBE 499 (Fall 2021)
Independent StudyBE 599 (Fall 2021)
InternshipBE 493 (Fall 2021)
ThesisBE 910 (Fall 2021)
ThesisBE 910 (Summer I 2021)
DissertationBE 920 (Spring 2021)
Senior Capstone CourseBAT 498 (Spring 2021)
Fundamentals of ComputingBE 502 (Fall 2020)
ThesisSTAT 910 (Fall 2020)
DissertationBE 920 (Spring 2020)
Fundamentals of ComputingBE 502 (Spring 2020)
ThesisSTAT 910 (Spring 2020)
DissertationBE 920 (Fall 2019)
ThesisSTAT 910 (Fall 2019)
- Haiquan, L. i. (2013). Hypothesis Generation from Heterogeneous Datasets.
- Haiquan, L. i. (2005). A correspondence between maximal complete bipartite subgraphs and closed patterns.
- Lussier, Y. A., & Li, H. (2013). Hypothesis generation from heterogeneous datasets. In Methods in Biomedical Informatics: A Pragmatic Approach(pp 81-98). Elsevier Science.
- Baldwin, E., Han, J., Luo, W., Zhou, J., An, L., Liu, J., Zhang, H. H., & Li, H. (2020). On fusion methods for knowledge discovery from multi-omics datasets. Computational and structural biotechnology journal, 18, 509-517.More infoRecent years have witnessed the tendency of measuring a biological sample on multiple omics scales for a comprehensive understanding of how biological activities on varying levels are perturbed by genetic variants, environments, and their interactions. This new trend raises substantial challenges to data integration and fusion, of which the latter is a specific type of integration that applies a uniform method in a scalable manner, to solve biological problems which the multi-omics measurements target. Fusion-based analysis has advanced rapidly in the past decade, thanks to application drivers and theoretical breakthroughs in mathematics, statistics, and computer science. We will briefly address these methods from methodological and mathematical perspectives and categorize them into three types of approaches: data fusion (a narrowed definition as compared to the general data fusion concept), model fusion, and mixed fusion. We will demonstrate at least one typical example in each specific category to exemplify the characteristics, principles, and applications of the methods in general, as well as discuss the gaps and potential issues for future studies.
- Rajan, S. S., Amin, A. D., Li, L., Rolland, D. C., Li, H., Kwon, D., Kweh, M. F., Arumov, A., Roberts, E. R., Yan, A., Basrur, V., Elenitoba-Johnson, K. S., Chen, X. S., Puvvada, S. D., Lussier, Y. A., Bilbao, D., Lim, M. S., & Schatz, J. H. (2020). The mechanism of cancer drug addiction in ALK-positive T-Cell lymphoma. Oncogene, 39(10), 2103-2117.More infoRational new strategies are needed to treat tumors resistant to kinase inhibitors. Mechanistic studies of resistance provide fertile ground for development of new approaches. Cancer drug addiction is a paradoxical resistance phenomenon, well-described in MEK-ERK-driven solid tumors, in which drug-target overexpression promotes resistance but a toxic overdose of signaling if the inhibitor is withdrawn. This can permit prolonged control of tumors through intermittent dosing. We and others showed previously that cancer drug addiction arises also in the hematologic malignancy ALK-positive anaplastic large-cell lymphoma (ALCL) resistant to ALK-specific tyrosine kinase inhibitors (TKIs). This is driven by the overexpression of the fusion kinase NPM1-ALK, but the mechanism by which ALK overactivity drives toxicity upon TKI withdrawal remained obscure. Here we reveal the mechanism of ALK-TKI addiction in ALCL. We interrogated the well-described mechanism of MEK/ERK pathway inhibitor addiction in solid tumors and found it does not apply to ALCL. Instead, phosphoproteomics and confirmatory functional studies revealed that the STAT1 overactivation is the key mechanism of ALK-TKI addiction in ALCL. The withdrawal of TKI from addicted tumors in vitro and in vivo leads to overwhelming phospho-STAT1 activation, turning on its tumor-suppressive gene-expression program and turning off STAT3's oncogenic program. Moreover, a novel NPM1-ALK-positive ALCL PDX model showed a significant survival benefit from intermittent compared with continuous TKI dosing. In sum, we reveal for the first time the mechanism of cancer drug addiction in ALK-positive ALCL and the benefit of scheduled intermittent dosing in high-risk patient-derived tumors in vivo.
- Vitali, F., Berghout, J., Fan, J., Li, J., Li, Q., Li, H., & Lussier, Y. A. (2019). Precision drug repurposing via convergent eQTL-based molecules and pathway targeting independent disease-associated polymorphisms. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 24, 308-319.More infoRepurposing existing drugs for new therapeutic indications can improve success rates and streamline development. Use of large-scale biomedical data repositories, including eQTL regulatory relationships and genome-wide disease risk associations, offers opportunities to propose novel indications for drugs targeting common or convergent molecular candidates associated to two or more diseases. This proposed novel computational approach scales across 262 complex diseases, building a multi-partite hierarchical network integrating (i) GWAS-derived SNP-to-disease associations, (ii) eQTL-derived SNP-to-eGene associations incorporating both cis- and trans-relationships from 19 tissues, (iii) protein target-to-drug, and (iv) drug-to-disease indications with (iv) Gene Ontology-based information theoretic semantic (ITS) similarity calculated between protein target functions. Our hypothesis is that if two diseases are associated to a common or functionally similar eGene - and a drug targeting that eGene/protein in one disease exists - the second disease becomes a potential repurposing indication. To explore this, all possible pairs of independently segregating GWAS-derived SNPs were generated, and a statistical network of similarity within each SNP-SNP pair was calculated according to scale-free overrepresentation of convergent biological processes activity in regulated eGenes (ITSeGENE-eGENE) and scale-free overrepresentation of common eGene targets between the two SNPs (ITSSNP-SNP). Significance of ITSSNP-SNP was conservatively estimated using empirical scale-free permutation resampling keeping the node-degree constant for each molecule in each permutation. We identified 26 new drug repurposing indication candidates spanning 89 GWAS diseases, including a potential repurposing of the calcium-channel blocker Verapamil from coronary disease to gout. Predictions from our approach are compared to known drug indications using DrugBank as a gold standard (odds ratio=13.1, p-value=2.49x10-8). Because of specific disease-SNPs associations to candidate drug targets, the proposed method provides evidence for future precision drug repositioning to a patient's specific polymorphisms.
- Li, H., Fan, J., Vitali, F., Berghout, J., Aberasturi, D., Li, J., Wilson, L., Chiu, W., Pumarejo, M., Han, J., Kenost, C., Koripella, P. C., Pouladi, N., Billheimer, D., Bedrick, E. J., & Lussier, Y. A. (2018). Novel disease syndromes unveiled by integrative multiscale network analysis of diseases sharing molecular effectors and comorbidities. BMC medical genomics, 11(Suppl 6), 112.
- Gardeux, V., Berghout, J., Achour, I., Schissler, A. G., Li, Q., Kenost, C., Li, J., Shang, Y., Bosco, A., Saner, D., Halonen, M. J., Jackson, D. J., Li, H., Martinez, F. D., & Lussier, Y. A. (2017). A genome-by-environment interaction classifier for precision medicine: personal transcriptome response to rhinovirus identifies children prone to asthma exacerbations. Journal of the American Medical Informatics Association : JAMIA, 24(6), 1116-1126.More infoTo introduce a disease prognosis framework enabled by a robust classification scheme derived from patient-specific transcriptomic response to stimulation.
- Li, Q., Schissler, A. G., Gardeux, V., Achour, I., Kenost, C., Berghout, J., Li, H., Zhang, H. H., & Lussier, Y. A. (2017). N-of-1-pathways MixEnrich: advancing precision medicine via single-subject analysis in discovering dynamic changes of transcriptomes. BMC medical genomics, 10(Suppl 1), 27.More infoTranscriptome analytic tools are commonly used across patient cohorts to develop drugs and predict clinical outcomes. However, as precision medicine pursues more accurate and individualized treatment decisions, these methods are not designed to address single-patient transcriptome analyses. We previously developed and validated the N-of-1-pathways framework using two methods, Wilcoxon and Mahalanobis Distance (MD), for personal transcriptome analysis derived from a pair of samples of a single patient. Although, both methods uncover concordantly dysregulated pathways, they are not designed to detect dysregulated pathways with up- and down-regulated genes (bidirectional dysregulation) that are ubiquitous in biological systems.
- Li, Q., Schissler, A. G., Gardeux, V., Berghout, J., Achour, I., Kenost, C., Li, H., Zhang, H. H., & Lussier, Y. A. (2017). kMEn: Analyzing noisy and bidirectional transcriptional pathway responses in single subjects. Journal of biomedical informatics, 66, 32-41. doi:10.1016/j.jbi.2016.12.009More infoUnderstanding dynamic, patient-level transcriptomic response to therapy is an important step forward for precision medicine. However, conventional transcriptome analysis aims to discover cohort-level change, lacking the capacity to unveil patient-specific response to therapy. To address this gap, we previously developed two N-of-1-pathways methods, Wilcoxon and Mahalanobis distance, to detect unidirectionally responsive transcripts within a pathway using a pair of samples from a single subject. Yet, these methods cannot recognize bidirectionally (up and down) responsive pathways. Further, our previous approaches have not been assessed in presence of background noise and are not designed to identify differentially expressed mRNAs between two samples of a patient taken in different contexts (e.g. cancer vs non cancer), which we termed responsive transcripts (RTs).
- Li, H., Achour, I., Bastarache, L., Berghout, J., Gardeux, V., Li, J., Lee, Y., Pesce, L., Yang, X., Ramos, K. S., Foster, I., Denny, J. C., Moore, J. H., & Lussier, Y. A. (2016). Integrative genomics analyses unveil downstream biological effectors of disease-specific polymorphisms buried in intergenic regions. NPJ genomic medicine, 1.More infoFunctionally altered biological mechanisms arising from disease-associated polymorphisms, remain difficult to characterize when those variants are intergenic, or, fall between genes. We sought to identify shared downstream mechanisms by which inter- and intragenic single nucleotide polymorphisms (SNPs) contribute to a specific physiopathology. Using computational modeling of 2 million pairs of disease-associated SNPs drawn from genome wide association studies (GWAS), integrated with expression Quantitative Trait Loci (eQTL) and Gene Ontology functional annotations, we predicted 3,870 inter-intra and inter-intra SNP pairs with convergent biological mechanisms (FDR12). We additionally confirmed synergistic and antagonistic genetic interactions for a subset of prioritized SNP pairs in independent studies of Alzheimer's disease (entropy p=0.046), bladder cancer (entropy p=0.039), and rheumatoid arthritis (PheWAS case-control p
- Pouladi, N., Achour, I., Li, H., Berghout, J., Kenost, C., Gonzalez-Garay, M. L., & Lussier, Y. A. (2016). Biomechanisms of Comorbidity: Reviewing Integrative Analyses of Multi-omics Datasets and Electronic Health Records. Yearbook of medical informatics, 194-206. doi:10.15265/IY-2016-040More infoDisease comorbidity is a pervasive phenomenon impacting patients' health outcomes, disease management, and clinical decisions. This review presents past, current and future research directions leveraging both phenotypic and molecular information to uncover disease similarity underpinning the biology and etiology of disease comorbidity.
- Schissler, A. G., Li, Q., Chen, J. L., Kenost, C., Achour, I., Billheimer, D. D., Li, H., Piegorsch, W. W., & Lussier, Y. A. (2016). Analysis of aggregated cell-cell statistical distances within pathways unveils therapeutic-resistance mechanisms in circulating tumor cells. Bioinformatics (Oxford, England), 32(12), i80-i89.More infoAs 'omics' biotechnologies accelerate the capability to contrast a myriad of molecular measurements from a single cell, they also exacerbate current analytical limitations for detecting meaningful single-cell dysregulations. Moreover, mRNA expression alone lacks functional interpretation, limiting opportunities for translation of single-cell transcriptomic insights to precision medicine. Lastly, most single-cell RNA-sequencing analytic approaches are not designed to investigate small populations of cells such as circulating tumor cells shed from solid tumors and isolated from patient blood samples.
- Li, H., Pouladi, N., Achour, I., Gardeux, V., Li, J., Li, Q., Zhang, H. H., Martinez, F. D., Garcia, J. G., & Lussier, Y. A. (2015). eQTL networks unveil enriched mRNA master integrators downstream of complex disease-associated SNPs. Journal of Biomedical Informatics, 58, 226-34.More infoThe causal and interplay mechanisms of Single Nucleotide Polymorphisms (SNPs) associated with complex diseases (complex disease SNPs) investigated in genome-wide association studies (GWAS) at the transcriptional level (mRNA) are poorly understood despite recent advancements such as discoveries reported in the Encyclopedia of DNA Elements (ENCODE) and Genotype-Tissue Expression (GTex). Protein interaction network analyses have successfully improved our understanding of both single gene diseases (Mendelian diseases) and complex diseases. Whether the mRNAs downstream of complex disease genes are central or peripheral in the genetic information flow relating DNA to mRNA remains unclear and may be disease-specific. Using expression Quantitative Trait Loci (eQTL) that provide DNA to mRNA associations and network centrality metrics, we hypothesize that we can unveil the systems properties of information flow between SNPs and the transcriptomes of complex diseases. We compare different conditions such as naïve SNP assignments and stringent linkage disequilibrium (LD) free assignments for transcripts to remove confounders from LD. Additionally, we compare the results from eQTL networks between lymphoblastoid cell lines and liver tissue. Empirical permutation resampling (p
- Schissler, A. G., Gardeux, V., Li, Q., Achour, I., Li, H., Piegorsch, W. W., & Lussier, Y. A. (2015). Dynamic changes of RNA-sequencing expression for precision medicine: N-of-1-pathways Mahalanobis distance within pathways of single subjects predicts breast cancer survival. Bioinformatics (Oxford, England), 31(12), i293-302.More infoThe conventional approach to personalized medicine relies on molecular data analytics across multiple patients. The path to precision medicine lies with molecular data analytics that can discover interpretable single-subject signals (N-of-1). We developed a global framework, N-of-1-pathways, for a mechanistic-anchored approach to single-subject gene expression data analysis. We previously employed a metric that could prioritize the statistical significance of a deregulated pathway in single subjects, however, it lacked in quantitative interpretability (e.g. the equivalent to a gene expression fold-change).
- Gardeux, V., Achour, I., Li, J., Maienschein-Cline, M., Li, H., Pesce, L., Parinandi, G., Bahroos, N., Winn, R., Foster, I., Garcia, J. G., & Lussier, Y. A. (2014). 'N-of-1-pathways' unveils personal deregulated mechanisms from a single pair of RNA-Seq samples: towards precision medicine. Journal of the American Medical Informatics Association : JAMIA, 21(6), 1015-25.More infoThe emergence of precision medicine allowed the incorporation of individual molecular data into patient care. Indeed, DNA sequencing predicts somatic mutations in individual patients. However, these genetic features overlook dynamic epigenetic and phenotypic response to therapy. Meanwhile, accurate personal transcriptome interpretation remains an unmet challenge. Further, N-of-1 (single-subject) efficacy trials are increasingly pursued, but are underpowered for molecular marker discovery.
- Haiquan, L. i. (2014). 'N-of-1-pathways' unveils personal deregulated mechanisms from a single pair of RNA-seq samples: Towards precision medicine. Journal of the American Medical Informatics Association.
- Haiquan, L. i. (2014). Accelerating precision biology and medicine with computational biology and bioinformatics. Genome Biology.
- Haiquan, L. i. (2014). COPD Hospitalization Risk Increased with Distinct Patterns of Multiple Systems Comorbidities Unveiled by Network Modeling. AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium.
- Haiquan, L. i. (2014). In Silico cancer cell versus stroma cellularity index computed from species-specific human and mouse transcriptome of xenograft models: Towards accurate stroma targeting therapy assessment. BMC Medical Genomics.
- Haiquan, L. i. (2014). Prognostic implication of pulmonary function at the beginning of postoperative radiotherapy in non-small cell lung cancer. Radiotherapy and Oncology.
- Haiquan, L. i. (2014). The mitochondrial cardiolipin remodeling enzyme lysocardiolipin acyltransferase is a novel target in pulmonary fibrosis.. American journal of respiratory and critical care medicine.
- Huang, L. S., Mathew, B., Li, H., Zhao, Y., Ma, S., Noth, I., Reddy, S. P., Harijith, A., Usatyuk, P. V., Berdyshev, E. V., Kaminski, N., Zhou, T., Zhang, W., Zhang, Y., Rehman, J., Kotha, S. R., Gurney, T. O., Parinandi, N. L., Lussier, Y. A., , Garcia, J. G., et al. (2014). The mitochondrial cardiolipin remodeling enzyme lysocardiolipin acyltransferase is a novel target in pulmonary fibrosis. American Journal of Respiratory and Critical Care Medicine, 189(11), 1402-15.More infoLysocardiolipin acyltransferase (LYCAT), a cardiolipin-remodeling enzyme regulating the 18:2 linoleic acid pattern of mammalian mitochondrial cardiolipin, is necessary for maintaining normal mitochondrial function and vascular development. We hypothesized that modulation of LYCAT expression in lung epithelium regulates development of pulmonary fibrosis.
- Kim, H., Lussier, Y. A., Noh, O. K., Li, H., Oh, Y., & Heo, J. (2014). Prognostic implication of pulmonary function at the beginning of postoperative radiotherapy in non-small cell lung cancer. Radiotherapy and Oncology, 113(3), 374-8.More infoThe purpose of this study was to investigate the prognostic effect of pulmonary function at the beginning of postoperative radiotherapy (PORT) in non-small cell lung cancer (NSCLC).
- Lussier, Y. A., Li, H., Pouladi, N., & Li, Q. (2014). Accelerating precision biology and medicine with computational biology and bioinformatics. Genome Biology, 15(9), 450.More infoA report on the 22nd Annual International Conference on Intelligent Systems for Molecular Biology, held in Boston, Massachusetts, USA, July 11-15, 2014.
- Yang, X., Huang, Y., Lee, Y., Gardeux, V., Achour, I., Regan, K., Rebman, E., Li, H., & Lussier, Y. A. (2014). In Silico cancer cell versus stroma cellularity index computed from species-specific human and mouse transcriptome of xenograft models: towards accurate stroma targeting therapy assessment. BMC Medical Genomics, 7 Suppl 1, S2.More infoThe current state of the art for measuring stromal response to targeted therapy requires burdensome and rate limiting quantitative histology. Transcriptome measures are increasingly affordable and provide an opportunity for developing a stromal versus cancer ratio in xenograft models. In these models, human cancer cells are transplanted into mouse host tissues (stroma) and together coevolve into a tumour microenvironment. However, profiling the mouse or human component separately remains problematic. Indeed, laser capture microdissection is labour intensive. Moreover, gene expression using commercial microarrays introduces significant and underreported cross-species hybridization errors that are commonly overlooked by biologists.
- Chen, J. L., Hsu, A., Yang, X., Li, J., Lee, Y., Parinandi, G., Li, H., & Lussier, Y. A. (2013). Curation-free biomodules mechanisms in prostate cancer predict recurrent disease. BMC Medical Genomics, 6 Suppl 2, S4.More infoGene expression-based prostate cancer gene signatures of poor prognosis are hampered by lack of gene feature reproducibility and a lack of understandability of their function. Molecular pathway-level mechanisms are intrinsically more stable and more robust than an individual gene. The Functional Analysis of Individual Microarray Expression (FAIME) we developed allows distinctive sample-level pathway measurements with utility for correlation with continuous phenotypes (e.g. survival). Further, we and others have previously demonstrated that pathway-level classifiers can be as accurate as gene-level classifiers using curated genesets that may implicitly comprise ascertainment biases (e.g. KEGG, GO). Here, we hypothesized that transformation of individual prostate cancer patient gene expression to pathway-level mechanisms derived from automated high throughput analyses of genomic datasets may also permit personalized pathway analysis and improve prognosis of recurrent disease.
- Haiquan, L. i. (2013). Conquering computational challenges of omics data and post-ENCODE paradigms. Genome Biology.
- Haiquan, L. i. (2013). Correction: Oligo- and polymetastatic progression in lung metastasis(es) patients is associated with specific microRNAs (PLoS ONE). PLoS ONE.
- Haiquan, L. i. (2013). Curation-free biomodules mechanisms in prostate cancer predict recurrent disease. BMC Medical Genomics.
- Haiquan, L. i. (2013). Network models of genome-wide association studies uncover the topological centrality of protein interactions in complex diseases. Journal of the American Medical Informatics Association.
- Lee, Y., Li, H., Li, J., Rebman, E., Achour, I., Regan, K. E., Gamazon, E. R., Chen, J. L., Yang, X. H., Cox, N. J., & Lussier, Y. A. (2013). Network models of genome-wide association studies uncover the topological centrality of protein interactions in complex diseases. Journal of the American Medical Informatics Association : JAMIA, 20(4), 619-29.More infoWhile genome-wide association studies (GWAS) of complex traits have revealed thousands of reproducible genetic associations to date, these loci collectively confer very little of the heritability of their respective diseases and, in general, have contributed little to our understanding the underlying disease biology. Physical protein interactions have been utilized to increase our understanding of human Mendelian disease loci but have yet to be fully exploited for complex traits.
- Lussier, Y. A., Li, H., & Maienschein-Cline, M. (2013). Conquering computational challenges of omics data and post-ENCODE paradigms. Genome Biology, 14(8), 310.More infoA report on the 21st Annual International Conference on Intelligent Systems for Molecular Biology (ISMB) and 12th European Conference on Computational Biology (ECCB), held in Berlin, Germany, July 21-23, 2013.
- Haiquan, L. i. (2012). Breakthroughs in genomics data integration for predicting clinical outcome. Journal of Biomedical Informatics.
- Haiquan, L. i. (2012). Complex-disease networks of trait-associated single-nucleotide polymorphisms (SNPs) unveiled by information theory. Journal of the American Medical Informatics Association.
- Haiquan, L. i. (2012). MET-IDEA version 2.06; improved efficiency and additional functions for mass spectrometry-based metabolomics data processing. Metabolomics.
- Haiquan, L. i. (2012). The rise of translational bioinformatics. Genome Biology.
- Haiquan, L. i. (2012). Towards mechanism classifiers: expression-anchored Gene Ontology signature predicts clinical outcome in lung adenocarcinoma patients.. AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium.
- Haiquan, L. i. (2012). Translating Mendelian and complex inheritance of Alzheimer's disease genes for predicting unique personal genome variants. Journal of the American Medical Informatics Association.
- Lei, Z., Li, H., Chang, J., Zhao, P. X., & Sumner, L. W. (2012). MET-IDEA version 2.06; improved efficiency and additional functions for mass spectrometry-based metabolomics data processing. METABOLOMICS, 8(1), S105-S110.
- Li, H., Lee, Y., Chen, J. L., Rebman, E., Li, J., & Lussier, Y. A. (2012). Complex-disease networks of trait-associated single-nucleotide polymorphisms (SNPs) unveiled by information theory. Journal of the American Medical Informatics Association : JAMIA, 19(2), 295-305.More infoThousands of complex-disease single-nucleotide polymorphisms (SNPs) have been discovered in genome-wide association studies (GWAS). However, these intragenic SNPs have not been collectively mined to unveil the genetic architecture between complex clinical traits. The authors hypothesize that biological annotations of host genes of trait-associated SNPs may reveal the biomolecular modularity across complex-disease traits and offer insights for drug repositioning.
- Lussier, Y. A., & Li, H. (2012). Breakthroughs in genomics data integration for predicting clinical outcome. Journal of Biomedical Informatics, 45(6), 1199-201.
- Lussier, Y. A., & Li, H. (2012). The rise of translational bioinformatics. Genome Biology, 13(8), 319.More infoA report on the 20th International Conference on Intelligent Systems for Molecular Biology (ISMB), held at Long Beach, California, USA, July 15-17, 2012.
- Lussier, Y. A., Khodarev, N. N., Regan, K., Corbin, K., Li, H., Ganai, S., Khan, S. A., Gnerlich, J. L., Gnerlich, J., Darga, T. E., Fan, H., Karpenko, O., Paty, P. B., Posner, M. C., Chmura, S. J., Hellman, S., Ferguson, M. K., & Weichselbaum, R. R. (2012). Oligo- and polymetastatic progression in lung metastasis(es) patients is associated with specific microRNAs. PloS One, 7(12), e50141.More infoStrategies to stage and treat cancer rely on a presumption of either localized or widespread metastatic disease. An intermediate state of metastasis termed oligometastasis(es) characterized by limited progression has been proposed. Oligometastases are amenable to treatment by surgical resection or radiotherapy.
- Regan, K., Wang, K., Doughty, E., Li, H., Li, J., Lee, Y., Kann, M. G., & Lussier, Y. A. (2012). Translating Mendelian and complex inheritance of Alzheimer's disease genes for predicting unique personal genome variants. Journal of the American Medical Informatics Association : JAMIA, 19(2), 306-16.More infoAlthough trait-associated genes identified as complex versus single-gene inheritance differ substantially in odds ratio, the authors nonetheless posit that their mechanistic concordance can reveal fundamental properties of the genetic architecture, allowing the automated interpretation of unique polymorphisms within a personal genome.
- Haiquan, L. i. (2010). Genomic inventory and transcriptional analysis of medicago truncatula transporters. Plant Physiology.
- Haiquan, L. i. (2009). TransportTP: A two-phase classification approach for membrane transporter prediction and characterization. BMC Bioinformatics.
- Haiquan, L. i. (2008). A nearest neighbor approach for automated transporter prediction and categorization from protein sequences. Bioinformatics.
- Haiquan, L. i. (2007). Maximal biclique subgraphs and closed pattern pairs of the adjacency matrix: A one-to-one correspondence and mining algorithms. IEEE Transactions on Knowledge and Data Engineering.
- Haiquan, L. i. (2006). Discovering motif pairs at interaction sites from protein sequences on a proteome-wide scale. Bioinformatics.
- Haiquan, L. i. (2005). Discovery of stable and significant binding motif pairs from PDB complexes and protein interaction datasets. Bioinformatics.
- Haiquan, L. i. (2005). Using fixed point theorems to model the binding in protein-protein interactions. IEEE Transactions on Knowledge and Data Engineering.
- Haiquan, L. i. (2004). Discovery of binding motif pairs from protein complex structural data and protein interaction sequence data.. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.
- Li, Q., Zaim, S., Aberasturi, D., Berghout, J., Li, H., Vitali, F., Kenost, C., Zhang, H., & Lussier, Y. A. (2019, Nov). Interpretation of ‘Omics dynamics in a single subject using local estimates of dispersion between two transcriptomes. In AMIA Annual Symposium.
- Lussier, Y. A., Butte, A., Li, H., Chen, R., & Moore, J. H. (2019, 1). Translational informatics of population Health: How large biomolecular and clinical datasets unite. In Pacific Symposium on Biocomputing.
- Han, J., Li, J., Achour, I., Pesce, L., Foster, I., Li, H., & Lussier, Y. A. (2018, Jan). Convergent downstream candidate mechanisms of independent intergenic polymorphisms between co-classified diseases implicate epistasis among noncoding elements. In Pacific Symposium on Biocomputing, 23, 524-535.More infoThe first author, Jiali Han, is my Ph.D. student and graduate research assistant.
- Lee, Y. J., Boyd, A. D., Li, J. J., Gardeux, V., Kenost, C., Saner, D., Li, H., Abraham, I., Krishnan, J. A., & Lussier, Y. A. (2014, November). COPD Hospitalization Risk Increased with Distinct Patterns of Multiple Systems Comorbidities Unveiled by Network Modeling. In AMIA Annual Symposium proceedings, 2014, 855-64.More infoEarlier studies on hospitalization risk are largely based on regression models. To our knowledge, network modeling of multiple comorbidities is novel and inherently enables multidimensional scoring and unbiased feature reduction. Network modeling was conducted using an independent validation design starting from 38,695 patients, 1,446,581 visits, and 430 distinct clinical facilities/hospitals. Odds ratios (OR) were calculated for every pair of comorbidity using patient counts and compared their tendency with hospitalization rates and ED visits. Network topology analyses were performed, defining significant comorbidity associations as having OR≥5 & False-Discovery-Rate≤10(-7). Four COPD-associated comorbidity sub-networks emerged, incorporating multiple clinical systems: (i) metabolic syndrome, (ii) substance abuse and mental disorder, (iii) pregnancy-associated conditions, and (iv) fall-related injury. The latter two have not been reported yet. Features prioritized from the network are predictive of hospitalizations in an independent set (p
- Perez-Rathke, A., Li, H., & Lussier, Y. A. (2013, January). Interpreting personal transcriptomes: personalized mechanism-scale profiling of RNA-seq data. In Pacific Symposium on Biocomputing, 159-70.More infoDespite thousands of reported studies unveiling gene-level signatures for complex diseases, few of these techniques work at the single-sample level with explicit underpinning of biological mechanisms. This presents both a critical dilemma in the field of personalized medicine as well as a plethora of opportunities for analysis of RNA-seq data. In this study, we hypothesize that the "Functional Analysis of Individual Microarray Expression" (FAIME) method we developed could be smoothly extended to RNA-seq data and unveil intrinsic underlying mechanism signatures across different scales of biological data for the same complex disease. Using publicly available RNA-seq data for gastric cancer, we confirmed the effectiveness of this method (i) to translate each sample transcriptome to pathway-scale scores, (ii) to predict deregulated pathways in gastric cancer against gold standards (FDR
- Yang, X., Li, H., Regan, K., Li, J., Huang, Y., & Lussier, Y. A. (2012, November). Towards mechanism classifiers: expression-anchored Gene Ontology signature predicts clinical outcome in lung adenocarcinoma patients. In AMIA Annual Symposium proceedings, 2012, 1040-9.More infoWe aim to provide clinically applicable, reproducible, mechanistic interpretations of gene expression changes that lack in gene overlap among predictive gene-signatures. Using a method we recently developed, Functional Analysis of Individual Microarray Expression (FAIME), we provide evidence that Gene Ontology-anchored signatures (GO-signatures) show reliable prognosis in lung cancer. In order to demonstrate the biological congruence and reproducibility of FAIME-derived mechanism classifiers, we chose a disease where gene expression classifiers signatures alone had failed to significantly stratify a larger collection of samples and that exhibited poor or no genetic overlap. For each patient in the two lung adenocarcinoma studies, personalized FAIME-profiles of GO biological processes are generated from genome-wide expression profiles. For both training studies, GO-signatures significantly associated to patient mortality were identified (Prediction Analysis for Microarrays; three-fold cross-validation). These two GO-signatures could effectively stratify patients from an independent validation cohort into sub-groups that show significant differences in disease-free survival (log-rank test P=0.019; P=0.001). Importantly, significant mechanism overlaps assessed by information-theory similarity were detected between the two GO-signatures (Fischer Exact Test p=0.001). Hence, together with machine learning technologies, FAIME could be utilized to develop an ontology-driven and expression-anchored prognostic signature that is personalized for an individual patient.
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