Volume 8 Supplement 2
Selected articles from the 4th Translational Bioinformatics Conference and the 8th International Conference on Systems Biology (TBC/ISB 2014)
Research
Edited by Ju Han Kim and Maricel Kann
Publication of this supplement has not been supported by sponsorship. Information about the source of funding for publication charges can be found in the individual articles. Articles have undergone the journal’s standard peer review process for supplements. The Supplement Editors declare that they have no competing interests.
The 4th Translational Bioinformatics Conference and the 8th International Conference on Systems Biology (TBC/ISB 2014). Go to conference site.
Qingdao, China24-27 October 2014
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Citation: BMC Medical Genomics 2015 8(Suppl 2):I1
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Novel therapeutics for coronary artery disease from genome-wide association study data
Coronary artery disease (CAD), one of the leading causes of death globally, is influenced by both environmental and genetic risk factors. Gene-centric genome-wide association studies (GWAS) involving cases and...
Citation: BMC Medical Genomics 2015 8(Suppl 2):S1 -
Inferring drug-disease associations based on known protein complexes
Inferring drug-disease associations is critical in unveiling disease mechanisms, as well as discovering novel functions of available drugs, or drug repositioning. Previous work is primarily based on drug-gene-...
Citation: BMC Medical Genomics 2015 8(Suppl 2):S2 -
Identification association of drug-disease by using functional gene module for breast cancer
In oncology drug development, it is important to develop low risk drugs efficiently. Meanwhile, computational methods have been paid more and more attention in drug discovery. However, few studies attempt to d...
Citation: BMC Medical Genomics 2015 8(Suppl 2):S3 -
Interpretation of personal genome sequencing data in terms of disease ranks based on mutual information
The rapid advances in genome sequencing technologies have resulted in an unprecedented number of genome variations being discovered in humans. However, there has been very limited coverage of interpretation of...
Citation: BMC Medical Genomics 2015 8(Suppl 2):S4 -
Relating hepatocellular carcinoma tumor samples and cell lines using gene expression data in translational research
Cancer cell lines are used extensively to study cancer biology and to test hypotheses in translational research. The relevance of cell lines is dependent on how closely they resemble the tumors being studied. ...
Citation: BMC Medical Genomics 2015 8(Suppl 2):S5 -
Identification of epigenetic modifications that contribute to pathogenesis in therapy-related AML: Effective integration of genome-wide histone modification with transcriptional profiles
Therapy-related, secondary acute myeloid leukemia (t-AML) is an increasingly frequent complication of intensive chemotherapy. This malignancy is often characterized by abnormalities of chromosome 7, including ...
Citation: BMC Medical Genomics 2015 8(Suppl 2):S6 -
Detection and analysis of disease-associated single nucleotide polymorphism influencing post-translational modification
Post-translational modification (PTM) plays a crucial role in biological functions and corresponding disease developments. Discovering disease-associated non-synonymous SNPs (nsSNPs) altering PTM sites can hel...
Citation: BMC Medical Genomics 2015 8(Suppl 2):S7 -
RCARE: RNA Sequence Comparison and Annotation for RNA Editing
The post-transcriptional sequence modification of transcripts through RNA editing is an important mechanism for regulating protein function and is associated with human disease phenotypes. The identification o...
Citation: BMC Medical Genomics 2015 8(Suppl 2):S8 -
Network-based prediction and knowledge mining of disease genes
In recent years, high-throughput protein interaction identification methods have generated a large amount of data. When combined with the results from other in vivo and in vitro experiments, a complex set of r...
Citation: BMC Medical Genomics 2015 8(Suppl 2):S9 -
Combined analysis of gene regulatory network and SNV information enhances identification of potential gene markers in mouse knockout studies with small number of samples
RNA-sequencing is widely used to measure gene expression level at the whole genome level. Comparing expression data from control and case studies provides good insight on potential gene markers for phenotypes....
Citation: BMC Medical Genomics 2015 8(Suppl 2):S10 -
Identifying network biomarkers based on protein-protein interactions and expression data
Identifying effective biomarkers to battle complex diseases is an important but challenging task in biomedical research today. Molecular data of complex diseases is increasingly abundant due to the rapid advan...
Citation: BMC Medical Genomics 2015 8(Suppl 2):S11 -
Graph pyramids for protein function prediction
Uncovering the hidden organizational characteristics and regularities among biological sequences is the key issue for detailed understanding of an underlying biological phenomenon. Thus pattern recognition fro...
Citation: BMC Medical Genomics 2015 8(Suppl 2):S12 -
SeedsGraph: an efficient assembler for next-generation sequencing data
DNA sequencing technology has been rapidly evolving, and produces a large number of short reads with a fast rising tendency. This has led to a resurgence of research in whole genome shotgun assembly algorithms...
Citation: BMC Medical Genomics 2015 8(Suppl 2):S13 -
PDEGEM: Modeling non-uniform read distribution in RNA-Seq data
RNA-Seq is a powerful new technology to comprehensively analyze the transcriptome of any given cells. An important task in RNA-Seq data analysis is quantifying the expression levels of all transcripts. Althoug...
Citation: BMC Medical Genomics 2015 8(Suppl 2):S14 -
Parallelization of enumerating tree-like chemical compounds by breadth-first search order
Enumeration of chemical compounds greatly assists designing and finding new drugs, and determining chemical structures from mass spectrometry. In our previous study, we developed efficient algorithms, BfsSimEnum ...
Citation: BMC Medical Genomics 2015 8(Suppl 2):S15
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2022 Citation Impact
2.7 - 2-year Impact Factor
3.2 - 5-year Impact Factor
0.730 - SNIP (Source Normalized Impact per Paper)
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