Yuji Zhang
University of Maryland Baltimore, Epidemiology and Public Health, Faculty Member
The biological role of vitamin D receptors (VDR), which are abundantly expressed in developing zebrafish (Danio rerio) as early as 48 h after fertilization, and before the development of a mineralized skeleton and mature intestine and... more
The biological role of vitamin D receptors (VDR), which are abundantly expressed in developing zebrafish (Danio rerio) as early as 48 h after fertilization, and before the development of a mineralized skeleton and mature intestine and kidney, is unknown. We probed the role of VDR in developing zebrafish biology by examining changes in expression of RNA by whole transcriptome shotgun sequencing (RNA-seq) in fish treated with picomolar concentrations of the VDR ligand and hormonal form of vitamin D(3), 1α,25-dihydroxyvitamin D(3) [1α,25(OH)(2)D(3))].We observed significant changes in RNAs of transcription factors, leptin, peptide hormones, and RNAs encoding proteins of fatty acid, amino acid, xenobiotic metabolism, receptor-activator of NFκB ligand (RANKL), and calcitonin-like ligand receptor pathways. Early highly restricted, and subsequent massive changes in more than 10% of expressed cellular RNA were observed. At days post fertilization (dpf) 2 [24 h 1α,25(OH)(2)D(3)-treatment], only four RNAs were differentially expressed (hormone vs. vehicle). On dpf 4 (72 h treatment), 77 RNAs; on dpf 6 (120 h treatment) 1039 RNAs; and on dpf 7 (144 h treatment), 2407 RNAs were differentially expressed in response to 1α,25(OH)(2)D(3). Fewer RNAs (n = 481) were altered in dpf 7 larvae treated for 24 h with 1α,25(OH)(2)D(3) vs. those treated with hormone for 144 h. At dpf 7, in 1α,25(OH)(2)D(3)-treated larvae, pharyngeal cartilage was larger and mineralization was greater. Changes in expression of RNAs for transcription factors, peptide hormones, and RNAs encoding proteins integral to fatty acid, amino acid, leptin, calcitonin-like ligand receptor, RANKL, and xenobiotic metabolism pathways, demonstrate heretofore unrecognized mechanisms by which 1α,25(OH)(2)D(3) functions in vivo in developing eukaryotes.
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Cancer is a complex and heterogeneous disease. Genetic methods have uncovered thousands of complex tissue-specific mutation-induced effects and identified multiple disease gene targets. Important associations between cancer and other... more
Cancer is a complex and heterogeneous disease. Genetic methods have uncovered thousands of complex tissue-specific mutation-induced effects and identified multiple disease gene targets. Important associations between cancer and other biological entities (eg, genes and drugs) in cancer network, however, are usually scattered in biomedical publications. Systematic analyses of these cancer-specific associations can help highlight the hidden associations between different cancer types and related genes/drugs. In this paper, we proposed a novel network-based computational framework to identify statistically over-expressed subnetwork patterns called network motifs (NMs) in an integrated cancer-specific drug-disease-gene network extracted from Semantic MEDLINE, a database containing extracted associations from MEDLINE abstracts. Eight significant NMs were identified and considered as the backbone of the cancer association network. Each NM corresponds to specific biological meanings. We dem...
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Precise regulation of the cell cycle is crucial to the growth and development of all organisms. Understanding the regulatory mechanism of the cell cycle is crucial to unraveling many complicated diseases, most notably cancer. Multiple... more
Precise regulation of the cell cycle is crucial to the growth and development of all organisms. Understanding the regulatory mechanism of the cell cycle is crucial to unraveling many complicated diseases, most notably cancer. Multiple sources of biological data are available to study the dynamic interactions among many genes that are related to the cancer cell cycle. Integrating these informative and complementary data sources can help to infer a mutually consistent gene transcriptional regulatory network with strong similarity to the underlying gene regulatory relationships in cancer cells. We propose an integrative framework that infers gene regulatory modules from the cell cycle of cancer cells by incorporating multiple sources of biological data, including gene expression profiles, gene ontology, and molecular interaction. Among 846 human genes with putative roles in cell cycle regulation, we identified 46 transcription factors and 39 gene ontology groups. We reconstructed regul...
Research Interests: Bioinformatics, Computational Biology, Systems Biology, Cell Cycle, Cell Signaling, and 21 moreTranscription Factors, Multidisciplinary, Recurrent Neural Network, Apoptosis and Molecular Signaling, Humans, Growth and development, Gene ontology, Data Collection, Gene Regulatory Networks, Transcription Factor, Regulatory Network, PLoS one, Data Integrity, Neoplasms, Cell Cycle regulation, Gene Set Enrichment Analysis, Binding Site, Biological Data, Molecular Interactions, Gene expression profiling, and Interaction Network
Vaccines have been one of the most successful public health interventions to date. The use of vaccination, however, sometimes comes with possible adverse events. The U.S. FDA/CDC Vaccine Adverse Event Reporting System (VAERS) currently... more
Vaccines have been one of the most successful public health interventions to date. The use of vaccination, however, sometimes comes with possible adverse events. The U.S. FDA/CDC Vaccine Adverse Event Reporting System (VAERS) currently contains more than 200,000 reports for post-vaccination events that occur after the administration of vaccines licensed in the United States. Although the data from the VAERS has been applied to many public health and vaccine safety studies, each individual report does not necessarily indicate a casuality relationship between the vaccine and the reported symptoms. Further statistical analysis and summarization needs to be done before this data can be leveraged. This paper introduces our efforts on representing the vaccine-symptom correlations and their corresponding meta-information extracted from the VAERS database using Resource Description Framework (RDF). Numbers of occurrences of vaccine-symptom pairs reported to the VAERS were summarized with co...
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The design principles of gene transcriptional regulation networks in cells have been puzzles due to their unknown dynamic and nonlinear mechanisms. Although high-throughput biotechnologies have generated unprecedented amounts of data, the... more
The design principles of gene transcriptional regulation networks in cells have been puzzles due to their unknown dynamic and nonlinear mechanisms. Although high-throughput biotechnologies have generated unprecedented amounts of data, the integration of multi-source data to better understand the process of gene regulation has been a challenge in post genomics era. Gene expression data are limited in providing information about
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We present a novel algorithm combining artificial neural networks and swarm intelligence (SI) methods to infer network interactions. The algorithm uses ant colony optimization (ACO) to identify the optimal architecture of a recurrent... more
We present a novel algorithm combining artificial neural networks and swarm intelligence (SI) methods to infer network interactions. The algorithm uses ant colony optimization (ACO) to identify the optimal architecture of a recurrent neural network (RNN), while the weights of the RNN are optimized using particle swarm optimization (PSO). Our goal is to construct an RNN that mimics the true structure of an unknown network and the time-series data that the network generated. We applied the proposed hybrid SI-RNN algorithm to infer a simulated genetic network. The results indicate that the algorithm has a promising potential to infer complex interactions such as gene regulatory networks from time-series gene expression data.
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Identifying breast cancer susceptibility genes is one of the key challenges in breast cancer research. Conventional gene-based approaches can identify patterns of gene activity that sub-classify tumors, by which genes with known breast... more
Identifying breast cancer susceptibility genes is one of the key challenges in breast cancer research. Conventional gene-based approaches can identify patterns of gene activity that sub-classify tumors, by which genes with known breast cancer mutations are typically not detected. In this study, we present a novel network motif-based approach that integrates biological network topology and high-throughput gene expression data to identify markers not as individual genes but as network motifs. We observed that the network motifs are more reproducible than individual marker genes selected without biological network information, and that they achieve higher accuracy in the classification of metastatic versus non-metastatic tumors.
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Research Interests: Bioinformatics, Computational Biology, Systems Biology, Cell Cycle, Cell Signaling, and 21 moreTranscription Factors, Multidisciplinary, Recurrent Neural Network, Apoptosis and Molecular Signaling, Humans, Growth and development, Gene ontology, Data Collection, Gene Regulatory Networks, Transcription Factor, Regulatory Network, PLoS one, Data Integrity, Neoplasms, Cell Cycle regulation, Gene Set Enrichment Analysis, Binding Site, Biological Data, Molecular Interactions, Gene expression profiling, and Interaction Network
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Research Interests: Information Theory, Intelligence, Computational Biology, Time Series, System Biology, and 17 moreEntropy, Feature Selection, Complex network, Mutual Information, Gene Network, Time Series Data, Gene Regulatory Networks, Transfer Function, Time Factors, Network Topology, Shannon entropy, Tsallis Entropy, Large Scale, Differential equation, Theoretical Model, Gene Transfer, and Boolean function
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Ovarian cancer is a clinically and molecularly heterogeneous disease. The driving forces behind this variability are unknown. Here, we report wide variation in the expression of the DNA cytosine deaminase APOBEC3B, with elevated... more
Ovarian cancer is a clinically and molecularly heterogeneous disease. The driving forces behind this variability are unknown. Here, we report wide variation in the expression of the DNA cytosine deaminase APOBEC3B, with elevated expression in the majority of ovarian cancer cell lines (three SDs above the mean of normal ovarian surface epithelial cells) and high-grade primary ovarian cancers. APOBEC3B is active in the nucleus of several ovarian cancer cell lines and elicits a biochemical preference for deamination of cytosines in 5'-TC dinucleotides. Importantly, examination of whole-genome sequence from 16 ovarian cancers reveals that APOBEC3B expression correlates with total mutation load as well as elevated levels of transversion mutations. In particular, high APOBEC3B expression correlates with C-to-A and C-to-G transversion mutations within…
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The biological role of vitamin D receptors (VDR), which are abundantly expressed in developing zebrafish (Danio rerio) as early as 48 h after fertilization, and before the development of a mineralized skeleton and mature intestine and... more
The biological role of vitamin D receptors (VDR), which are abundantly expressed in developing zebrafish (Danio rerio) as early as 48 h after fertilization, and before the development of a mineralized skeleton and mature intestine and kidney, is unknown. We probed the role of VDR in developing zebrafish biology by examining changes in expression of RNA by whole transcriptome shotgun sequencing (RNA-seq) in fish treated with picomolar concentrations of the VDR ligand and hormonal form of vitamin D(3), 1α,25-dihydroxyvitamin D(3) [1α,25(OH)(2)D(3))].We observed significant changes in RNAs of transcription factors, leptin, peptide hormones, and RNAs encoding proteins of fatty acid, amino acid, xenobiotic metabolism, receptor-activator of NFκB ligand (RANKL), and calcitonin-like ligand receptor pathways. Early highly restricted, and subsequent massive changes in more than 10% of expressed cellular RNA were observed. At days post fertilization (dpf) 2 [24 h 1α,25(OH)(2)D(3)-treatment], only four RNAs were differentially expressed (hormone vs. vehicle). On dpf 4 (72 h treatment), 77 RNAs; on dpf 6 (120 h treatment) 1039 RNAs; and on dpf 7 (144 h treatment), 2407 RNAs were differentially expressed in response to 1α,25(OH)(2)D(3). Fewer RNAs (n = 481) were altered in dpf 7 larvae treated for 24 h with 1α,25(OH)(2)D(3) vs. those treated with hormone for 144 h. At dpf 7, in 1α,25(OH)(2)D(3)-treated larvae, pharyngeal cartilage was larger and mineralization was greater. Changes in expression of RNAs for transcription factors, peptide hormones, and RNAs encoding proteins integral to fatty acid, amino acid, leptin, calcitonin-like ligand receptor, RANKL, and xenobiotic metabolism pathways, demonstrate heretofore unrecognized mechanisms by which 1α,25(OH)(2)D(3) functions in vivo in developing eukaryotes.
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Research Interests: Mechanical Engineering, Genetics, Algorithms, Distributed Computing, Grid Computing, and 64 moreArtificial Intelligence, Statistics, Biomedical Engineering, Data Mining, Signal Processing, Statistical Signal Processing, Computational Biology, Gene regulation, Statistical Analysis, Time Use, Microarray Data Analysis, System Biology, Gene expression, Multidisciplinary, Reverse Engineering, Probability, Signal Transduction, Genetic Engineering, Biological Sciences, Saccharomyces cerevisiae, Software, Computer Simulation, Approximate Bayesian Computation, Mathematical Sciences, Escherichia coli, Markov Chain Monte Carlo, Computer Model, Animals, Regression Analysis, Plants, Quorum Sensing, BMC Bioinformatics, Central Nervous System, Gene Network, ROC Curve, Gene Regulatory Networks, Transcription Factor, Glycosylation, Clinical Sciences, Information Storage and Retrieval, Evolutionary Algorithm, Biological systems, Rats, Longitudinal data, Very high throughput, Genetics and Genomics, Gene Targeting, Computer communication networks, High Dimensionality, Statistical Model, B Lymphocytes, Repairable System, Network Model, Bayes Theorem, Protein Binding, Proteome, Normal Distribution, Electrical And Electronic Engineering, Pectobacterium, Biochemistry and cell biology, Microarray Data, Gene Expression Regulation, Gene expression profiling, and Gene Expression Data
A huge amount of associations among different biological entities (e.g., disease, drug, and gene) are scattered in millions of biomedical articles. Systematic analysis of such heterogeneous data can infer novel associations among... more
A huge amount of associations among different biological entities (e.g., disease, drug, and gene) are scattered in millions of biomedical articles. Systematic analysis of such heterogeneous data can infer novel associations among different biological entities in the context of personalized medicine and translational research. Recently, network-based computational approaches have gained popularity in investigating such heterogeneous data, proposing novel therapeutic targets and deciphering disease mechanisms. However, little effort has been devoted to investigating associations among drugs, diseases, and genes in an integrative manner. We propose a novel network-based computational framework to identify statistically over-expressed subnetwork patterns, called network motifs, in an integrated disease-drug-gene network extracted from Semantic MEDLINE. The framework consists of two steps. The first step is to construct an association network by extracting pair-wise associations between ...