Rui Chang

Interests

Research

• Bayesian Network, Graphical Model, Machine Learning• Systems/Network Biology• Regulatory Network and Signaling Pathway Reconstruction• In-silico Molecular Phenotype Prediction• Personalized Precision Medicine• Neurodegenerative Diseases• Cancer• Regenerative Medicine

Courses

No activities entered.

Scholarly Contributions

Journals/Publications

• Chang, R. (2018). Divergent brain gene expression patterns associate with distinct cell-specific tau neuropathology traits in progressive supranuclear palsy. Acta Neuropathol.
• Chang, R. (2020). Metabolic network analysis reveals altered bile acid synthesis and metabolism in Alzheimer’s disease. Cell Report Medicine.
• Chang, R. (2020). Sex and APOE ε4 genotype modify the Alzheimer’s disease serum metabolome. Nature Communications.
• Chang, R. (2020). Single Cell-type Integrative Network Modeling Identified Novel Microglial-specific Targets for the Phagosome in Alzheimer’s disease. BioRxiv.
• Chang, R. (2021). Automated digital TIL analysis (ADTA) adds prognostic value to standard assessment of depth and ulceration in primary melanoma. Scientific Reports.
• Chang, R. (2021). Transcriptomic analysis identifies differences in gene expression in actinic keratoses after treatment with imiquimod and between responders and non responders. Scientific Reports.
• Chang, R. (2022). Predictive metabolic networks reveal sex and APOE genotype-specific metabolic signatures and drivers for precision medicine in Alzheimer Disease. Alzheimer's & Dementia.
• Hui, K., He, Q., Tsai, S. F., Mudalige, D. M., Henrion, M. Y., Zaidi, S. S., Lau, B., Tang, A., Cadiz, M. P., Hodos-Nkhereanye, R., Moein, S., Alamprese, M. L., Bennett, D. A., De Jager, P. L., Frye, J. D., Ertekin-Taner, N., Ronaldson, P. T., Kuo, Y., & Chang, R. (2022). Novel Master Regulators of Microglial Phagocytosis and Repurposed FDA-Approved Drug for Treatment of Alzheimer's Disease. BioRxiv.
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  Microglia, the innate immune cells of the brain, are essential determinants of late-onset Alzheimer’s Disease (LOAD) neuropathology. Here, we developed an integrative computational systems biology approach to construct causal network models of genetic regulatory programs for microglia in Alzheimer’s Disease (AD). This model enabled us to identify novel key driver (KDs) genes for microglial functions that can be targeted for AD pharmacotherapy. We prioritized FCER1G, HCK, LAPTM5, ITGB2, SLC1A2, PAPLN, GSAP, NTRK2, and CIRBP as KDs of microglial phagocytosis promoting neuroprotection and/or neural repair. In vitro, shRNA knockdown of each KD significantly reduced microglial phagocytosis. We repurposed riluzole, an FDA-approved ALS drug that upregulates SLC1A2activity, and discovered that it stimulated phagocytosis of Aβ1-42 in human primary microglia and decreased hippocampal amyloid plaque burden/phosphorylated tau levels in the brain of aged 3xTg-AD mice. Taken together, these data emphasize the utlility of our integrative approach for repurposing drugs for AD therapy.
• Ramachandran, V., Liu, Y., He, Q., Tang, A., Ronaldson, P. T., Schenten, D., & Chang, R. (2022). Novel Inhibitors against COVID-19 main protease suppressed viral infection. BioRxiv. doi:https://doi.org/10.1101/2022.11.05.515305
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  Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiologic agent of COVID-19, can cause severe disease with high mortality rates, especially among older and vulnerable populations. Despite the recent success of vaccines and approval of first-generation anti-viral inhibitor against SARS-CoV-2, an expanded arsenal of anti-viral compounds that limit viral replication and ameliorate disease severity is still urgently needed in light of the continued emergence of viral variants of concern (VOC). The main protease (Mpro) of SARS-CoV-2 is the major non-structural protein required for the processing of viral polypeptides encoded by the open reading frame 1 (ORF1) and ultimately replication. Structural conservation of Mpro among SARS-CoV-2 variants make this protein an attractive target for the anti-viral inhibition by small molecules. Here, we developed a structure-based in-silico screening of approximately 11 million compounds in ZINC15 database inhibiting Mpro, which prioritized 9 lead compounds for the subsequent in vitro validation in SARS-CoV-2 replication assays using both Vero and Calu-3 cells. We validated three of these compounds significantly inhibited SARS-CoV-2 replication in the micromolar range. In summary, our study identified novel small-molecules significantly suppressed infection and replication of SARS-CoV-2 in human cells.
• Chang, R. (2020). Predictive network modeling in human induced pluripotent stem cells identifies key driver genes for insulin responsiveness. PLoS Computational Biology. doi:https://doi.org/10.1371/journal.pcbi.1008491
• Chang, R., Brauer, W., Stetter, M., Chang, R., Brauer, W., & Stetter, M. (2020). Modeling semantics of inconsistent qualitative knowledge for quantitative Bayesian network inference. Neural networks : the official journal of the International Neural Network Society, 21(2-3), 182-92.
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  We propose a novel framework for performing quantitative Bayesian inference based on qualitative knowledge. Here, we focus on the treatment in the case of inconsistent qualitative knowledge. A hierarchical Bayesian model is proposed for integrating inconsistent qualitative knowledge by calculating a prior belief distribution based on a vector of knowledge features. Each inconsistent knowledge component uniquely defines a model class in the hyperspace. A set of constraints within each class is generated to describe the uncertainty in ground Bayesian model space. Quantitative Bayesian inference is approximated by model averaging with Monte Carlo methods. Our method is firstly benchmarked on ASIA network and is applied to a realistic biomolecular interaction modeling problem for breast cancer bone metastasis. Results suggest that our method enables consistently modeling and quantitative Bayesian inference by reconciling a set of inconsistent qualitative knowledge.
• Chang, R. (2019). Validation of Melanoma Immune Profile (MIP), a Prognostic Immune Gene Prediction Score for Stage II–III Melanoma, Clinical. Clinical Cancer Research. doi:10.1158/1078-0432.CCR-18-2847
• Wirka, R. C., Wagh, D., Paik, D. T., Pjanic, M., Nguyen, T., Miller, C. L., Kundu, R., Nagao, M., Coller, J., Koyano, T. K., Fong, R., Woo, Y. J., Liu, B., Montgomery, S. B., Wu, J. C., Zhu, K., Chang, R., Alamprese, M., Tallquist, M. D., , Kim, J. B., et al. (2019). Atheroprotective roles of smooth muscle cell phenotypic modulation and the TCF21 disease gene as revealed by single-cell analysis. Nature medicine, 25(8), 1280-1289.
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  In response to various stimuli, vascular smooth muscle cells (SMCs) can de-differentiate, proliferate and migrate in a process known as phenotypic modulation. However, the phenotype of modulated SMCs in vivo during atherosclerosis and the influence of this process on coronary artery disease (CAD) risk have not been clearly established. Using single-cell RNA sequencing, we comprehensively characterized the transcriptomic phenotype of modulated SMCs in vivo in atherosclerotic lesions of both mouse and human arteries and found that these cells transform into unique fibroblast-like cells, termed 'fibromyocytes', rather than into a classical macrophage phenotype. SMC-specific knockout of TCF21-a causal CAD gene-markedly inhibited SMC phenotypic modulation in mice, leading to the presence of fewer fibromyocytes within lesions as well as within the protective fibrous cap of the lesions. Moreover, TCF21 expression was strongly associated with SMC phenotypic modulation in diseased human coronary arteries, and higher levels of TCF21 expression were associated with decreased CAD risk in human CAD-relevant tissues. These results establish a protective role for both TCF21 and SMC phenotypic modulation in this disease.
• Allen, M., Wang, X., Serie, D. J., Strickland, S. L., Burgess, J. D., Koga, S., Younkin, C. S., Nguyen, T. T., Malphrus, K. G., Lincoln, S. J., Alamprese, M., Zhu, K., Chang, R., Carrasquillo, M. M., Kouri, N., Murray, M. E., Reddy, J. S., Funk, C., Price, N. D., , Golde, T. E., et al. (2018). Divergent brain gene expression patterns associate with distinct cell-specific tau neuropathology traits in progressive supranuclear palsy. Acta neuropathologica, 136(5), 709-727.
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  Progressive supranuclear palsy (PSP) is a neurodegenerative parkinsonian disorder characterized by tau pathology in neurons and glial cells. Transcriptional regulation has been implicated as a potential mechanism in conferring disease risk and neuropathology for some PSP genetic risk variants. However, the role of transcriptional changes as potential drivers of distinct cell-specific tau lesions has not been explored. In this study, we integrated brain gene expression measurements, quantitative neuropathology traits and genome-wide genotypes from 268 autopsy-confirmed PSP patients to identify transcriptional associations with unique cell-specific tau pathologies. We provide individual transcript and transcriptional network associations for quantitative oligodendroglial (coiled bodies = CB), neuronal (neurofibrillary tangles = NFT), astrocytic (tufted astrocytes = TA) tau pathology, and tau threads and genomic annotations of these findings. We identified divergent patterns of transcriptional associations for the distinct tau lesions, with the neuronal and astrocytic neuropathologies being the most different. We determined that NFT are positively associated with a brain co-expression network enriched for synaptic and PSP candidate risk genes, whereas TA are positively associated with a microglial gene-enriched immune network. In contrast, TA is negatively associated with synaptic and NFT with immune system transcripts. Our findings have implications for the diverse molecular mechanisms that underlie cell-specific vulnerability and disease risk in PSP.
• Petyuk, V. A., Petyuk, V. A., Chang, R., Chang, R., Ramirez-Restrepo, M., Ramirez-Restrepo, M., Beckmann, N. D., Beckmann, N. D., Henrion, M. Y., Henrion, M. Y., Piehowski, P. D., Piehowski, P. D., Zhu, K., Zhu, K., Wang, S., Wang, S., Clarke, J., Clarke, J., Huentelman, M. J., , Huentelman, M. J., et al. (2018). The human brainome: network analysis identifies HSPA2 as a novel Alzheimer’s disease target. Brain : a journal of neurology, 141(9), 2721-2739.
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  Our hypothesis is that changes in gene and protein expression are crucial to the development of late-onset Alzheimer’s disease. Previously we examined how DNA alleles control downstream expression of RNA transcripts and how those relationships are changed in late-onset Alzheimer’s disease. We have now examined how proteins are incorporated into networks in two separate series and evaluated our outputs in two different cell lines. Our pipeline included the following steps: (i) predicting expression quantitative trait loci; (ii) determining differential expression; (iii) analysing networks of transcript and peptide relationships; and (iv) validating effects in two separate cell lines. We performed all our analysis in two separate brain series to validate effects. Our two series included 345 samples in the first set (177 controls, 168 cases; age range 65–105; 58% female; KRONOSII cohort) and 409 samples in the replicate set (153 controls, 141 cases, 115 mild cognitive impairment; age range 66–107; 63% female; RUSH cohort). Our top target is heat shock protein family A member 2 (HSPA2), which was identified as a key driver in our two datasets. HSPA2 was validated in two cell lines, with overexpression driving further elevation of amyloid-β40 and amyloid-β42 levels in APP mutant cells, as well as significant elevation of microtubule associated protein tau and phosphorylated-tau in a modified neuroglioma line. This work further demonstrates that studying changes in gene and protein expression is crucial to understanding late onset disease and further nominates HSPA2 as a specific key regulator of late-onset Alzheimer’s disease processes.10.1093/brain/awy215_video1awy215media15824729224001.
• Carcamo-Orive, I., Hoffman, G. E., Cundiff, P., Beckmann, N. D., D'Souza, S. L., Knowles, J. W., Patel, A., Papatsenko, D., Abbasi, F., Reaven, G. M., Whalen, S., Lee, P., Shahbazi, M., Henrion, M. Y., Zhu, K., Wang, S., Roussos, P., Schadt, E. E., Pandey, G., , Chang, R., et al. (2017). Analysis of Transcriptional Variability in a Large Human iPSC Library Reveals Genetic and Non-genetic Determinants of Heterogeneity. Cell stem cell, 20(4), 518-532.e9.
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  Variability in induced pluripotent stem cell (iPSC) lines remains a concern for disease modeling and regenerative medicine. We have used RNA-sequencing analysis and linear mixed models to examine the sources of gene expression variability in 317 human iPSC lines from 101 individuals. We found that ∼50% of genome-wide expression variability is explained by variation across individuals and identified a set of expression quantitative trait loci that contribute to this variation. These analyses coupled with allele-specific expression show that iPSCs retain a donor-specific gene expression pattern. Network, pathway, and key driver analyses showed that Polycomb targets contribute significantly to the non-genetic variability seen within and across individuals, highlighting this chromatin regulator as a likely source of reprogramming-based variability. Our findings therefore shed light on variation between iPSC lines and illustrate the potential for our dataset and other similar large-scale analyses to identify underlying drivers relevant to iPSC applications.
• Cohain, A., Divaraniya, A. A., Zhu, K., Scarpa, J. R., Kasarskis, A., Zhu, J., Chang, R., Dudley, J. T., & Schadt, E. E. (2017). EXPLORING THE REPRODUCIBILITY OF PROBABILISTIC CAUSAL MOLECULAR NETWORK MODELS. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 22, 120-131.
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  Network reconstruction algorithms are increasingly being employed in biomedical and life sciences research to integrate large-scale, high-dimensional data informing on living systems. One particular class of probabilistic causal networks being applied to model the complexity and causal structure of biological data is Bayesian networks (BNs). BNs provide an elegant mathematical framework for not only inferring causal relationships among many different molecular and higher order phenotypes, but also for incorporating highly diverse priors that provide an efficient path for incorporating existing knowledge. While significant methodological developments have broadly enabled the application of BNs to generate and validate meaningful biological hypotheses, the reproducibility of BNs in this context has not been systematically explored. In this study, we aim to determine the criteria for generating reproducible BNs in the context of transcription-based regulatory networks. We utilize two unique tissues from independent datasets, whole blood from the GTEx Consortium and liver from the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Team (STARNET) study. We evaluated the reproducibility of the BNs by creating networks on data subsampled at different levels from each cohort and comparing these networks to the BNs constructed using the complete data. To help validate our results, we used simulated networks at varying sample sizes. Our study indicates that reproducibility of BNs in biological research is an issue worthy of further consideration, especially in light of the many publications that now employ findings from such constructs without appropriate attention paid to reproducibility. We find that while edge-to-edge reproducibility is strongly dependent on sample size, identification of more highly connected key driver nodes in BNs can be carried out with high confidence across a range of sample sizes.
• Toledo, J. B., Arnold, M., Kastenmüller, G., Chang, R., Baillie, R. A., Han, X., Thambisetty, M., Tenenbaum, J. D., Suhre, K., Thompson, J. W., John-Williams, L. S., MahmoudianDehkordi, S., Rotroff, D. M., Jack, J. R., Motsinger-Reif, A., Risacher, S. L., Blach, C., Lucas, J. E., Massaro, T., , Louie, G., et al. (2017). Metabolic network failures in Alzheimer's disease: A biochemical road map. Alzheimer's & dementia : the journal of the Alzheimer's Association, 13(9), 965-984.
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  The Alzheimer's Disease Research Summits of 2012 and 2015 incorporated experts from academia, industry, and nonprofit organizations to develop new research directions to transform our understanding of Alzheimer's disease (AD) and propel the development of critically needed therapies. In response to their recommendations, big data at multiple levels are being generated and integrated to study network failures in disease. We used metabolomics as a global biochemical approach to identify peripheral metabolic changes in AD patients and correlate them to cerebrospinal fluid pathology markers, imaging features, and cognitive performance.
• Chang, R., Karr, J. R., & Schadt, E. E. (2015). Causal inference in biology networks with integrated belief propagation. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 359-70.
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  Inferring causal relationships among molecular and higher order phenotypes is a critical step in elucidating the complexity of living systems. Here we propose a novel method for inferring causality that is no longer constrained by the conditional dependency arguments that limit the ability of statistical causal inference methods to resolve causal relationships within sets of graphical models that are Markov equivalent. Our method utilizes Bayesian belief propagation to infer the responses of perturbation events on molecular traits given a hypothesized graph structure. A distance measure between the inferred response distribution and the observed data is defined to assess the 'fitness' of the hypothesized causal relationships. To test our algorithm, we infer causal relationships within equivalence classes of gene networks in which the form of the functional interactions that are possible are assumed to be nonlinear, given synthetic microarray and RNA sequencing data. We also apply our method to infer causality in real metabolic network with v-structure and feedback loop. We show that our method can recapitulate the causal structure and recover the feedback loop only from steady-state data which conventional method cannot.
• Robyn, G., Qian, Y., Lopez, G., Fu, Y., White-Stern, A., Bansal, M., Sivendran, S., Califano, A., Saenger, Y., & Chang, R. (2015). Previously defined 53 immune gene panel predicts melanoma survival using The Cancer Genome Atlas (TCGA). Journal for ImmunoTherapy of Cancer. doi:10.1186/2051-1426-3-S2-P283
• de Moll, E. H., Fu, Y., Qian, Y., Perkins, S. H., Wieder, S., Gnjatic, S., Remark, R., Bernardo, S. G., Moskalenko, M., Yao, J., Ferringer, T., Chang, R., Chipuk, J., Horst, B. A., Birge, M. B., Phelps, R. G., & Saenger, Y. M. (2015). Immune biomarkers are more accurate in prediction of survival in ulcerated than in non-ulcerated primary melanomas. Cancer immunology, immunotherapy : CII, 64(9), 1193-203.
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  Ulcerated melanomas may have a unique biology and microenvironment. We test whether markers of immune infiltration correlate with clinical outcome in ulcerated compared to non-ulcerated primary melanoma tumors.
• Harcharik, S., Bernardo, S., Moskalenko, M., Pan, M., Sivendran, M., Bell, H., Hall, L. D., Castillo-Martín, M., Fox, K., Cordon-Cardo, C., Chang, R., Sivendran, S., Phelps, R. G., & Saenger, Y. (2014). Defining the role of CD2 in disease progression and overall survival among patients with completely resected stage-II to -III cutaneous melanoma. Journal of the American Academy of Dermatology, 70(6), 1036-44.
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  Accurate assessment of prognosis remains clinically challenging in stage II to III cutaneous melanoma. Studies have implicated CD2 in immune surveillance, T-cell activation, and antitumor immunity, but its role in melanoma progression warrants further investigation.
• Sivendran, S., Chang, R., Pham, L., Phelps, R. G., Harcharik, S. T., Hall, L. D., Bernardo, S. G., Moskalenko, M. M., Sivendran, M., Fu, Y., de Moll, E. H., Pan, M., Moon, J. Y., Arora, S., Cohain, A., DiFeo, A., Ferringer, T. C., Tismenetsky, M., Tsui, C. L., , Friedlander, P. A., et al. (2014). Dissection of immune gene networks in primary melanoma tumors critical for antitumor surveillance of patients with stage II-III resectable disease. The Journal of investigative dermatology, 134(8), 2202-2211.
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  Patients with resected stage II-III cutaneous melanomas remain at high risk for metastasis and death. Biomarker development has been limited by the challenge of isolating high-quality RNA for transcriptome-wide profiling from formalin-fixed and paraffin-embedded (FFPE) primary tumor specimens. Using NanoString technology, RNA from 40 stage II-III FFPE primary melanomas was analyzed and a 53-immune-gene panel predictive of non-progression (area under the curve (AUC)=0.920) was defined. The signature predicted disease-specific survival (DSS P
• Bernardo, S. G., Moskalenko, M., Pan, M., Shah, S., Sidhu, H. K., Sicular, S., Harcharik, S., Chang, R., Friedlander, P., & Saenger, Y. M. (2013). Elevated rates of transaminitis during ipilimumab therapy for metastatic melanoma. Melanoma research, 23(1), 47-54.
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  Melanoma is the deadliest form of skin cancer. Ipilimumab, a novel immunotherapy, is the first treatment shown to improve survival in patients with metastatic melanoma in large randomized controlled studies. The most concerning side effects reported in clinical studies of ipilimumab fall into the category of immune-related adverse events, which include enterocolitis, dermatitis, thyroiditis, hepatitis, hypophysitis, uveitis, and others. During the course of routine clinical care at Mount Sinai Medical Center, frequent hepatotoxicity was noted when ipilimumab was administered at a dose of 3 mg/kg according to Food and Drug Administration (FDA) guidelines. To better characterize these adverse events, we conducted a retrospective review of the first 11 patients with metastatic melanoma treated with ipilimumab at the Mount Sinai Medical Center after FDA approval. Aspartate aminotransferase (AST) and alanine aminotransferase (ALT) elevation, as defined by the National Cancer Institute's Common Terminology Criteria for Adverse Events, each occurred in six of 11 cases (≥grade 1), a notably higher frequency than could be expected on the basis of the FDA licensing study where elevations were reported in 0.8 and 1.5% of patients for AST and ALT, respectively. Grade 3 elevations in AST occurred in three of 11 patients as compared with 0% in the licensing trial. All cases of transaminitis resolved when ipilimumab was temporarily withheld without administration of immunosuppressive medication. During routine clinical care of late-stage melanoma patients with ipilimumab, physicians should monitor patients closely for hepatotoxicity and be aware that toxicity rates may differ across populations during ipilimumab therapy.
• Cassidy, L., Choi, M., Meyer, J., Chang, R., & Seigel, G. M. (2013). Immunoreactivity of Pluripotent Markers SSEA-5 and L1CAM in Human Tumors, Teratomas, and Induced Pluripotent Stem Cells. Journal of biomarkers, 2013, 960862.
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  Pluripotent stem cell markers can be useful for diagnostic evaluation of human tumors. The novel pluripotent marker stage-specific embryonic antigen-5 (SSEA-5) is expressed in undifferentiated human induced pluripotent cells (iPSCs), but little is known about SSEA-5 expression in other primitive tissues (e.g., human tumors). We evaluated SSEA-5 immunoreactivity patterns in human tumors, cell lines, teratomas, and iPS cells together with another pluripotent cell surface marker L1 cell adhesion molecule (L1CAM). We tested two hypotheses: (1) SSEA-5 and L1CAM would be immunoreactive and colocalized in human tumors; (2) SSEA-5 and L1CAM immunoreactivity would persist in iPSCs following retinal differentiating treatment. SSEA-5 immunofluorescence was most pronounced in primitive tumors, such as embryonal carcinoma. In tumor cell lines, SSEA-5 was highly immunoreactive in Capan-1 cells, while L1CAM was highly immunoreactive in U87MG cells. SSEA-5 and L1CAM showed colocalization in undifferentiated iPSCs, with immunopositive iPSCs remaining after 20 days of retinal differentiating treatment. This is the first demonstration of SSEA-5 immunoreactivity in human tumors and the first indication of SSEA-5 and L1CAM colocalization. SSEA-5 and L1CAM warrant further investigation as potentially useful tumor markers for histological evaluation or as markers to monitor the presence of undifferentiated cells in iPSC populations prior to therapeutic use.
• Seigel, G., Choi, M., Chang, R., Meyer, J., Ksander, B., Kolovou, P., de Waard, N., & Cassidy, L. (2013). Expression of Pluripotent Markers L1CAM and SSEA-5, Common to Human Retinoblastomas, Xenografts, Teratomas, Embryonic Tumors and Induced Pluripotent Stem Cells. Investigative Ophthalmology & Visual Science.
• Schadt, E., & Chang, R. (2012). Genetics. A GPS for navigating DNA. Science (New York, N.Y.), 337(6099), 1179-80.
• Chang, R., Shoemaker, R., & Wang, W. (2011). A novel knowledge-driven systems biology approach for phenotype prediction upon genetic intervention. IEEE/ACM transactions on computational biology and bioinformatics, 8(5), 1170-82.
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  Deciphering the biological networks underlying complex phenotypic traits, e.g., human disease is undoubtedly crucial to understand the underlying molecular mechanisms and to develop effective therapeutics. Due to the network complexity and the relatively small number of available experiments, data-driven modeling is a great challenge for deducing the functions of genes/proteins in the network and in phenotype formation. We propose a novel knowledge-driven systems biology method that utilizes qualitative knowledge to construct a Dynamic Bayesian network (DBN) to represent the biological network underlying a specific phenotype. Edges in this network depict physical interactions between genes and/or proteins. A qualitative knowledge model first translates typical molecular interactions into constraints when resolving the DBN structure and parameters. Therefore, the uncertainty of the network is restricted to a subset of models which are consistent with the qualitative knowledge. All models satisfying the constraints are considered as candidates for the underlying network. These consistent models are used to perform quantitative inference. By in silico inference, we can predict phenotypic traits upon genetic interventions and perturbing in the network. We applied our method to analyze the puzzling mechanism of breast cancer cell proliferation network and we accurately predicted cancer cell growth rate upon manipulating (anti)cancerous marker genes/proteins.
• Chang, R., Shoemaker, R., & Wang, W. (2011). Systematic search for recipes to generate induced pluripotent stem cells. PLoS computational biology, 7(12), e1002300.
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  Generation of induced pluripotent stem cells (iPSCs) opens a new avenue in regenerative medicine. One of the major hurdles for therapeutic applications is to improve the efficiency of generating iPSCs and also to avoid the tumorigenicity, which requires searching for new reprogramming recipes. We present a systems biology approach to efficiently evaluate a large number of possible recipes and find those that are most effective at generating iPSCs. We not only recovered several experimentally confirmed recipes but we also suggested new ones that may improve reprogramming efficiency and quality. In addition, our approach allows one to estimate the cell-state landscape, monitor the progress of reprogramming, identify important regulatory transition states, and ultimately understand the mechanisms of iPSC generation.
• Chang, R., & Wang, W. (2010). Novel algorithm for Bayesian network parameter learning with informative prior constraints. The 23rd International Joint Conference on Neural Networks. doi:10.1109/IJCNN.2010.5596889
• Chang, R., Stetter, M., & Brauer, W. (2008). Quantitative Inference by Qualitative Semantic Knowledge Mining with Bayesian Model Averaging. IEEE Transactions on Knowledge and Data Engineering, 20(12), 1587-1600. doi:10.1109/TKDE.2008.89