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      Computational Advances in Cancer Informatics (A)

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          Abstract

          Supplement Aims and Scope Cancer Informatics represents a hybrid discipline encompassing the fields of oncology, computer science, bioinformatics, statistics, computational biology, genomics, proteomics, metabolomics, pharmacology, and quantitative epidemiology. The common bond or challenge that unifies the various disciplines is the need to bring order to the massive amounts of data generated by researchers and clinicians attempting to find the underlying causes and effective means of treating cancer. The future cancer informatician will need to be well-versed in each of these fields and have the appropriate background to leverage the computational, clinical, and basic science resources necessary to understand their data and separate signal from noise. Knowledge of and the communication among these specialty disciplines, acting in unison, will be the key to success as we strive to find answers underlying the complex and often puzzling diseases known as cancer. Authors of articles in this supplement were asked to focus on computational advances, including one or more of the following topics: ▪ Gene Set Enrichment Analysis ▪ Hybrid Computing ▪ Efficient Cloud Storage and Retrieval ▪ Matching of Expression Patterns ▪ Multi-Modal Analysis ▪ Splice Variations and Chip Seq System Algorithms ▪ Rapid High-Throughput Analysis ▪ Computational Molecular Profiling ▪ Digital Gene Expression Analysis ▪ De Novo Genome Assembly and Re-Sequencing Computational Methods ▪ Computational Drug Repurposing ▪ Accelerated Next Generation Sequencing Technologies and Search Engines ▪ Post-Transcriptional Pattern Recognition ▪ Computational Advances in High-Content Platform Analysis ▪ Data visualization Software Development ▪ Machine Learning and Integrative Computer Approaches for Cancer Omics Research Computational method is playing an increasingly more important role in cancer research. The advances of text mining1–3, microRNA4,5, pathway analysis6,7, and whole genome sequencing8 shed light on improving medical practices using fine-grained information of individual patients but there is a long way to go towards personalized medical practices. We still cannot provide optimal treatment for many cancer patients9 and we have not accurately identified relationships between molecular subtypes and prognosis and treatment response. To improve treatment, we need to learn which pathways are altered in a given cancer, determine how they are changed, identify therapeutic targets on the pathways, and discover therapies that can reverse the damage. Biomedical science is entering a “big data” era, and the catalogue of genomic variants in the human population is expanding rapidly in the decades to come. There has been an explosion of new genomic and proteomic datasets, which provide us with unprecedented and rich resources to discover the underlying mechanisms. There are also abundant data concerning SNPs, somatic mutations, copy number, methylation levels, and expression levels in cancerous and noncancerous tissue. To fully exploit these data, we need advanced biomedical informatics methodology that can extract useful knowledge efficiently. The main objective of this special issue is to bring researchers together from different areas of cancer informatics to exchange ideas, disseminate novel research methodologies, and promote cross-disciplinary collaborations. The issue is therefore broad to cover various aspects of informatics and medical analyses, a unique combination that is appreciated by researchers in the field. Just to highlight a few articles, Kim et al systematically compare different feature selection and predictive models to identify a set of highly predictive features to predict novel pre-miRNAsin renal cancer study. Neapolitan et al use Bayesian networks to infer aberrant signaling pathways in ovarian cancer using The Cancer Genome Atlas (TCGA) data. Hua et al evaluate gene set enrichment analysis via a hybrid model. Lu et al integrate protein phosphorylation and gene expression data to infer signaling pathways. It is more imperative than ever to work together in cancer informatics to reveal insightful biological functions and their underlying mechanisms. We appreciate the opportunity to lead and contribute to this special issue. Lead Guest Editor Dr Xiaoqian Jiang Dr. Xiaoqian Jiang is an assistant professor of biomedical informatics at the University of California San Diego. He completed his PhD in Computer Science at Carnegie Mellon University and has previously worked at Mitsubishi Electrical Research Laboratory. He is an associate editor of BMC Medical Informatics and Decision Making. He now works primarily in health data privacy and predictive models in biomedicine. Dr. Jiang is the author or co-author of 57 published papers and has presented at 17 conferences. Guest Editors RUI CHEN Dr. Rui Chen is a research assistant professor of computer science at Hong Kong Baptist University. He completed his PhD at Concordia University and has previously worked at the University of British Columbia and INRIA Grenoble Rhone-Alpes. His primary research interests lie in databases, data mining and data privacy. Dr. Chen is the author or co-author of eight published papers and has 15 conference papers. Dr. Chen has been committee members for five conferences and served as external reviewers for numerous leading journals and conferences. SAMUEL CHENG Dr. Samuel Cheng is an associate professor of electrical and computing engineering at the University of Oklahoma. He completed his PhD at Texas A&M University and has previously worked at Microsoft Asia, Panasonic Technologies Company and Advanced Digital Imaging Research. He now works primarily in signal and image processing, and information theory. Dr. Cheng is the author or co-author of 39 published papers and has 92 conference papers. Dr. Cheng has several patent submissions and has been awarded five US patents. XIA JIANG Dr. Xia Jiang is an assistant professor of biomedical informatics at the University of Pittsburgh. She received her PhD from School of Medicine at the University of Pittsburgh. She is now a principal investigator of NIH/NLM funded projects on the development of a clinical decision support system in breast cancer, and epistasis learning of cancer genome data. Dr. Jiang is the author or co-author of 34 peer-reviewed scientific publications, and has given over 10 conference presentations and invited talks. She is the co-author of the books “Probabilistic Methods for Financial and Marketing Informatics” and “Contemporary Artificial Intelligence.” BAIRONG SHEN Dr. Bairong Shen is a professor of systems biology at Soochow University. He completed his PhD at Fudan University and has previously worked at Suzhou Medical College, Fudan University, and the University of Tampere. He now works primarily in bioinformatics. Dr. Shen is the author or co-author of 73 published papers and has presented at more than 10 conferences. RONG XU Dr. Rong Xu is an assistant professor in the Division of Medical Informatics at Case Western Reserve University. She completed her PhD at Stanford University. Her current research focusses on facilitating biomedical discovery and promoting efficient communication between biomedical researchers, physicians and patients to improve health care delivery. Dr. Xu is the author or co-author of 14 published papers and has 27 conference papers. SONG YI Dr. Song Yi is a research fellow of genetics at Harvard Medical School. He completed his PhD at the University of Iowa. He now works primarily in systems biology. Dr. Yi is the author or co-author of 15 high-impact published scientific papers in his field and has presented his work at over 20 conferences. Notably, his findings have been highlighted two times by the world-renowned journal Nature as a significant scientific breakthrough in biology. Dr. Yi’s work has been collectively cited more than 400 times, and is internationally recognized. He holds editorial appointments at the Universal Journal of Microbiology Research.

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          Biomedical text mining and its applications in cancer research.

          Cancer is a malignant disease that has caused millions of human deaths. Its study has a long history of well over 100years. There have been an enormous number of publications on cancer research. This integrated but unstructured biomedical text is of great value for cancer diagnostics, treatment, and prevention. The immense body and rapid growth of biomedical text on cancer has led to the appearance of a large number of text mining techniques aimed at extracting novel knowledge from scientific text. Biomedical text mining on cancer research is computationally automatic and high-throughput in nature. However, it is error-prone due to the complexity of natural language processing. In this review, we introduce the basic concepts underlying text mining and examine some frequently used algorithms, tools, and data sets, as well as assessing how much these algorithms have been utilized. We then discuss the current state-of-the-art text mining applications in cancer research and we also provide some resources for cancer text mining. With the development of systems biology, researchers tend to understand complex biomedical systems from a systems biology viewpoint. Thus, the full utilization of text mining to facilitate cancer systems biology research is fast becoming a major concern. To address this issue, we describe the general workflow of text mining in cancer systems biology and each phase of the workflow. We hope that this review can (i) provide a useful overview of the current work of this field; (ii) help researchers to choose text mining tools and datasets; and (iii) highlight how to apply text mining to assist cancer systems biology research.
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            Clear cell renal cell carcinoma associated microRNA expression signatures identified by an integrated bioinformatics analysis

            Background Clear cell renal cell carcinoma (ccRCC) represents the most invasive and common adult kidney neoplasm. Mounting evidence suggests that microRNAs (miRNAs) are important regulators of gene expression. But their function in tumourigenesis in this tumour type remains elusive. With the development of high throughput technologies such as microarrays and NGS, aberrant miRNA expression has been widely observed in ccRCC. Systematic and integrative analysis of multiple microRNA expression datasets may reveal potential mechanisms by which microRNAs contribute to ccRCC pathogenesis. Methods We collected 5 public microRNA expression datasets in ccRCC versus non-matching normal renal tissues from GEO database and published literatures. We analyzed these data sets with an integrated bioinformatics framework to identify expression signatures. The framework incorporates a novel statistic method for abnormal gene expression detection and an in-house developed predictor to assess the regulatory activity of microRNAs. We then mapped target genes of DE-miRNAs to different databases, such as GO, KEGG, GeneGo etc, for functional enrichment analysis. Results Using this framework we identified a consistent panel of eleven deregulated miRNAs shared by five independent datasets that can distinguish normal kidney tissues from ccRCC. After comparison with 3 RNA-seq based microRNA profiling studies, we found that our data correlated well with the results of next generation sequencing. We also discovered 14 novel molecular pathways that are likely to play a role in the tumourigenesis of ccRCC. Conclusions The integrative framework described in this paper greatly improves the inter-dataset consistency of microRNA expression signatures. Consensus expression profile should be identified at pathway or network level to address the heterogeneity of cancer. The DE-miRNA signature and novel pathways identified herein could provide potential biomarkers for ccRCC that await further validation.
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              Identification of novel microRNA regulatory pathways associated with heterogeneous prostate cancer

              Background MicroRNAs (miRNAs) are potential regulators that contribute to the pathogenesis of cancer. Microarray technologies have been widely used to characterize aberrant miRNA expression patterns in cancer. Nevertheless, the miRNAs expression signatures identified for a same cancer differs among laboratories due to the cancer heterogeneity. In addition, how the deregulated miRNAs coordinately contribute to the tumourigenic process of prostate cancer remains elusive. Results We evaluated five outlier detection algorithms that take into account the heterogeneity of cancer samples. ORT was selected as the best method and applied to four prostate cancer associated microRNA expression datasets. After microRNA target prediction and pathway enrichment mapping, 38 Gene Ontology terms, 16 KEGG pathways and 99 GeneGO pathways are found putative prostate cancer associated. Comparison with our previous studies, we identified two putative novel pathways important in prostate cancer. The two novel pathways are 1) ligand-independent activation of ESR1 and ESR2 and 2) membrane-bound ESR1: interaction with growth factors signalling. Conclusions We proved that expression signatures of at the pathway level well address the cancer heterogeneity and are more consistent than at the miRNA/gene levels. Based on this observation, we identified putative novel microRNA regulatory pathways which will help us to elucidate the cooperative function of different microRNAs in prostate cancer.
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                Author and article information

                Journal
                Cancer Inform
                Cancer Inform
                Cancer Informatics
                Cancer Informatics
                Libertas Academica
                1176-9351
                2014
                13 October 2014
                : 13
                : Suppl 1
                : 45-48
                Affiliations
                Assistant Professor of Biomedical Informatics, University of California San Diego, La Jolla, CA, USA.
                Research Assistant Professor of Computer Science, Hong Kong Baptist University, Kowloon Tong, China.
                Associate Professor of Electrical and Computing Engineering, University of Oklahoma, Norman, OK, USA.
                Assistant Professor of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
                Professor of Systems Biology, Soochow University, Suzhou, Jiangsu, China.
                Assistant Professor, Division of Medical Informatics, Case Western Reserve University, Cleveland, OH, USA.
                Research Fellow of Genetics, Harvard Medical School, Boston, MA, USA.
                Author notes
                Article
                cin-suppl.1-2014-045
                10.4137/CIN.S19243
                4216040
                25484572
                1ccd5fc8-3ce5-494b-a24a-27c308c67eab
                © 2014 the author(s), publisher and licensee Libertas Academica Ltd.

                This is an open-access article distributed under the terms of the Creative Commons CC-BY-NC 3.0 License.

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                Oncology & Radiotherapy
                Oncology & Radiotherapy

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