• Genomics & Informatics
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• v.11 no.4
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• pp.174-179
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• 2013

The large amount of data on cancer genome research has contributed to our understanding of cancer biology. Indeed, the genomics approach has a strong advantage for analyzing multi-factorial and complicated problems, such as cancer. It is time to think about the actual usage of cancer genomics in the clinical field. The clinical cancer field has lots of unmet needs in the management of cancer patients, which has been defined in the pre-genomic era. Unmet clinical needs are not well known to bioinformaticians and even non-clinician cancer scientists. A personalized approach in the clinical field will bring potential additional challenges to cancer genomics, because most data to now have been population-based rather than individualbased. We can maximize the use of cancer genomics in the clinical field if cancer scientists, bioinformaticians, and clinicians think and work together in solving unmet clinical needs. In this review, we present one imaginary case of a cancer patient, with which we can think about unmet clinical needs to solve with cancer genomics in the diagnosis, prediction of prognosis, monitoring the status of cancer, and personalized treatment decision.

• Journal of Life Science
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• v.18 no.3
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• pp.318-322
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• 2008

This paper deals with an efficient management and automatic update sub-system for WASPIFA (Web-based Assistant System for Protein-protein Interaction and Function Analysis) system that had been developed in the past and now provides the comprehensive information on protein-protein interaction and protein function. Protein interacting data has increased exponentially, so that it costs enormous time and effort. In other words, it is actually impossible to manually update and manage an analysis system based on protein interacting data. Even though there exists a good analysis system, it could be useless if it was able to be updated timely and managed properly. Unfortunately, in most cases, biologists without professional knowledge on their analysis systems have to cope with a great difficulty in running them. In this respect, the efficient management and automatic update subsystem of protein interacting and its related data has been developed to facilitate experimental biologists as well as bioinformaticians to update and manage the WASPIFA system.

• IMMUNE NETWORK
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• v.14 no.2
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• pp.73-80
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• 2014

Because autoimmune diseases (AIDs) result from a complex combination of genetic and epigenetic factors, as well as an altered immune response to endogenous or exogenous antigens, systems biology approaches have been widely applied. The use of multi-omics approaches, including blood transcriptomics, genomics, epigenetics, proteomics, and metabolomics, not only allow for the discovery of a number of biomarkers but also will provide new directions for further translational AIDs applications. Systems biology approaches rely on high-throughput techniques with data analysis platforms that leverage the assessment of genes, proteins, metabolites, and network analysis of complex biologic or pathways implicated in specific AID conditions. To facilitate the discovery of validated and qualified biomarkers, better-coordinated multi-omics approaches and standardized translational research, in combination with the skills of biologists, clinicians, engineers, and bioinformaticians, are required.

• Genomics & Informatics
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• v.12 no.4
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• pp.145-150
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• 2014

Recent technical advances, such as chromatin immunoprecipitation combined with DNA microarrays (ChIp-chip) and chromatin immunoprecipitation-sequencing (ChIP-seq), have generated large quantities of high-throughput data. Considering that epigenomic datasets are arranged over chromosomes, their analysis must account for spatial or temporal characteristics. In that sense, simple clustering or classification methodologies are inadequate for the analysis of multi-track ChIP-chip or ChIP-seq data. Approaches that are based on hidden Markov models (HMMs) can integrate dependencies between directly adjacent measurements in the genome. Here, we review three HMM-based studies that have contributed to epigenetic research, from a computational perspective. We also give a brief tutorial on HMM modelling-targeted at bioinformaticians who are new to the field.

• Journal of KIISE:Databases
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• v.32 no.2
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• pp.120-128
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• 2005

Recently main Bioinformatics databases(DDBJ, ENSEMBL, KEGG, etc.) provide analysis tools and data using web services for the convenience of bioinformaticians. Thus, defining SOAP server objects and their methods are very important to provide services for web services. We define SOAP server objects for interaction databases such as BIND, MINT and DIP.