Journal of Clinical Bioinformatics 

About: Journal of Clinical Bioinformatics is an academic journal. The journal publishes majorly in the area(s): Cancer & Gene. It has an ISSN identifier of 2043-9113. Over the lifetime, 113 publications have been published receiving 2371 citations.

Clinical decision support systems for improving diagnostic accuracy and achieving precision medicine.

Abstract: As research laboratories and clinics collaborate to achieve precision medicine, both communities are required to understand mandated electronic health/medical record (EHR/EMR) initiatives that will be fully implemented in all clinics in the United States by 2015. Stakeholders will need to evaluate current record keeping practices and optimize and standardize methodologies to capture nearly all information in digital format. Collaborative efforts from academic and industry sectors are crucial to achieving higher efficacy in patient care while minimizing costs. Currently existing digitized data and information are present in multiple formats and are largely unstructured. In the absence of a universally accepted management system, departments and institutions continue to generate silos of information. As a result, invaluable and newly discovered knowledge is difficult to access. To accelerate biomedical research and reduce healthcare costs, clinical and bioinformatics systems must employ common data elements to create structured annotation forms enabling laboratories and clinics to capture sharable data in real time. Conversion of these datasets to knowable information should be a routine institutionalized process. New scientific knowledge and clinical discoveries can be shared via integrated knowledge environments defined by flexible data models and extensive use of standards, ontologies, vocabularies, and thesauri. In the clinical setting, aggregated knowledge must be displayed in user-friendly formats so that physicians, non-technical laboratory personnel, nurses, data/research coordinators, and end-users can enter data, access information, and understand the output. The effort to connect astronomical numbers of data points, including ‘-omics’-based molecular data, individual genome sequences, experimental data, patient clinical phenotypes, and follow-up data is a monumental task. Roadblocks to this vision of integration and interoperability include ethical, legal, and logistical concerns. Ensuring data security and protection of patient rights while simultaneously facilitating standardization is paramount to maintaining public support. The capabilities of supercomputing need to be applied strategically. A standardized, methodological implementation must be applied to developed artificial intelligence systems with the ability to integrate data and information into clinically relevant knowledge. Ultimately, the integration of bioinformatics and clinical data in a clinical decision support system promises precision medicine and cost effective and personalized patient care.

PROGgene: gene expression based survival analysis web application for multiple cancers

Abstract: Background Identification of prognostic mRNA biomarkers has been done for various cancer types. The data that are published from such studies are archived in public repositories. There are hundreds of such datasets available for multiple cancer types in public repositories. Wealth of such data can be utilized to study prognostic implications of mRNA in different cancers as well as in different populations or subtypes of same cancer.

Clinical bioinformatics: a new emerging science

Abstract: Welcome to the open-access journal titled Journal of Clinical Bioinformatics (JCBi), a truly international journal devoted to clinical applications of bioinformatics, medical informatics and the development of bioinformatics tools, methodologies and approaches for clinical research. JCBi aims to discover how biological and medical informatics can be applied to the development of personalized healthcare, medication and therapies. The field of clinical informatics includes the analysis of human microarray and other omics data, combination of bioinformatics and medical informatics, development of bioinformatics methodologies for clinical research, and human databases. JCBi also aims to establish the leading scientific channel to translate bioinformatics to clinical and medical application in order to better understand molecular and cellular mechanisms and therapies of diseases.

The multiple roles of microRNA-155 in oncogenesis

Abstract: The microRNA miR-155 is prominent in cancer biology. Among microRNAs that have been linked to cancer, it is the most commonly overexpressed in malignancies (PNAS 109:20047-20052, 2012). Since its discovery, miR-155 has been implicated in promoting cancers of the breast, lung, liver, and lymphatic system. As such, targeted therapies may prove beneficial to cancer treatment. This review discusses the important role of miR-155 in oncogenesis. It synthesizes information from ten recent papers on miR-155, and includes an analysis and discussion of its association with cancer, interactions with other miRNAs, mechanisms of action, and the most promising available treatment options. Current debates in the field include the importance of miRNAs in general and their utility as targets in preventing tumorigenesis (Blood 119:513-520, 2012). Most of the papers being reviewed here confirm the role of miR-155 in oncogenesis (EMBO Mol Med 1:288-295, 2009). While there is some controversy surrounding recent research that claims that miR-155 may display anti-oncogenic or pro-immunological benefits (Cell Rep 2:1697–1709, 2012), most research seems to point to the importance of anti-miRs, with anti-miR-155 in particular, for cancer therapy.

comoR: a software for disease comorbidity risk assessment

Abstract: The diagnosis of comorbidities, which refers to the coexistence of different acute and chronic diseases, is difficult due to the modern extreme specialisation of physicians. We envisage that a software dedicated to comorbidity diagnosis could result in an effective aid to the health practice. We have developed an R software comoR to compute novel estimators of the disease comorbidity associations. Starting from an initial diagnosis, genetic and clinical data of a patient the software identifies the risk of disease comorbidity. Then it provides a pipeline with different causal inference packages (e.g. pcalg, qtlnet etc) to predict the causal relationship of diseases. It also provides a pipeline with network regression and survival analysis tools (e.g. Net-Cox, rbsurv etc) to predict more accurate survival probability of patients. The input of this software is the initial diagnosis for a patient and the output provides evidences of disease comorbidity mapping. The functions of the comoR offer flexibility for diagnostic applications to predict disease comorbidities, and can be easily integrated to high–throughput and clinical data analysis pipelines.