Current methodologies for translational bioinformatics

Lussier, Yves A.; Butte, Atul J.; Hunter, Lawrence

doi:10.1016/j.jbi.2010.05.002

Cited by 7 publications

(2 citation statements)

References 12 publications

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“…This is often called “from bench to bedside” and defines the general idea underlying translational research and its particular realization in the form of personalized medicine. From a practical point of view, in order to accomplish such a translation of basic research results into the daily clinical routine, it is necessary to be able to generate cost-efficient patient data on the molecular and cellular level (Butte, 2008 ; Lussier et al, 2010 ). Fortunately, technological progress within the last 15 years has led to a variety of different experimental assays that provide such opportunities, even on the genomic-scale involving large portions of an organism's genes.…”

Section: Introductionmentioning

confidence: 99%

Enhancing systems medicine beyond genotype data by dynamic patient signatures: having information and using it too

Emmert‐Streib

Dehmer

2013

Front. Genet.

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In order to establish systems medicine, based on the results and insights from basic biological research applicable for a medical and a clinical patient care, it is essential to measure patient-based data that represent the molecular and cellular state of the patient's pathology. In this paper, we discuss potential limitations of the sole usage of static genotype data, e.g., from next-generation sequencing, for translational research. The hypothesis advocated in this paper is that dynOmics data, i.e., high-throughput data that are capable of capturing dynamic aspects of the activity of samples from patients, are important for enabling personalized medicine by complementing genotype data.

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Section: Introductionmentioning

confidence: 99%

Enhancing systems medicine beyond genotype data by dynamic patient signatures: having information and using it too

Emmert‐Streib

Dehmer

2013

Front. Genet.

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“…Advances in high-throughput molecular measurement technologies continued to dramatically increase; and due to this, the demand for the sharing of data and results from these technologies has intensified [1, 2]. Many journals now require the public availability of such data [3], and the National Institutes of Health requires grant applicants to include a section on data sharing if their proposals exceed a threshold amount.…”

Section: Introductionmentioning

confidence: 99%

Comparison of automated and human assignment of MeSH terms on publicly-available molecular datasets

Ruau

Mbagwu

Dudley

et al. 2011

Journal of Biomedical Informatics

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Publicly available molecular datasets can be used for independent verification or investigative repurposing, but depends on the presence, consistency and quality of descriptive annotations. Annotation and indexing of molecular datasets using well-defined controlled vocabularies or ontologies enables accurate and systematic data discovery, yet the majority of molecular datasets available through public data repositories lack such annotations. A number of automated annotation methods have been developed; however few systematic evaluations of the quality of annotations supplied by application of these methods have been performed using annotations from standing public data repositories. Here, we compared manually-assigned Medical Subject Heading (MeSH) annotations associated with experiments by data submitters in the PRoteomics IDEntification (PRIDE) proteomics data repository to automated MeSH annotations derived through the National Center for Biomedical Ontology Annotator and National Library of Medicine MetaMap programs. These programs were applied to free-text annotations for experiments in PRIDE. As many submitted datasets were referenced in publications, we used the manually curated MeSH annotations of those linked publications in MEDLINE as “gold standard”. Annotator and MetaMap exhibited recall performance 3-fold greater than that of the manual annotations. We connected PRIDE experiments in a network topology according to shared MeSH annotations and found 373 distinct clusters, many of which were found to be biologically coherent by network analysis. The results of this study suggest that both Annotator and MetaMap are capable of annotating public molecular datasets with a quality comparable, and often exceeding, that of the actual data submitters, highlighting a continuous need to improve and apply automated methods to molecular datasets in public data repositories to maximize their value and utility.

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