Connecting the dots in translational bioinformatics: TBC 2014 collection

The Translational Bioinformatics Conference (TBC) has been one of the most successful multi-disciplinary conferences in the rapidly emerging fields of bioinformatics and clinical genomics for their bidirectional translations. The Fourth Annual TBC 2014 jointly held with the 8th International Conference on Systems Biology meeting for four days at the Huiquan Dynasty Hotel, Qingdao, China, improved our understanding of novel diagnostics and therapeutics in the era of biomedical big data. While TBC is organized as an international forum for translational bioinformatics, the first three annual meetings of TBC have been held in Korea since 2011. We appreciate the Chinese Academy of Sciences for hosting TBC 2014 and making TBC a truly international one. Japanese Association of Medical Informatics (JAMI) has unanimously approved to host TBC 2015 in Tokyo in early November, 2015. TBC 2016 will either be held in India or United States. It is a great pleasure to see the real growth of TBC. NIH Director Francis S. Collins said, "Data creation in today's research is exponentially more rapid than anything we anticipated even a decade ago." The ability to connecting the dots in the wealth biomedical big data will bring us the 'big picture' in a mass of genes, drugs, diseases, and diagnostic, therapeutic and prognostic markers. Steve Jobs said, "You can't connect the dots looking forward; you can only connect them looking backwards. So you have to trust that the dots will somehow connect in your future." Personalized medicine attempts to determine individual solutions based on the genomic and clinical profiles of each individual, providing opportunity to incorporate individual molecular data into patient care. While a plethora of genomic signatures have successfully demonstrated their predictive power, they are merely statistically-significant differences between dichotomized phenotypes that are in fact severely heterogeneous. Despite many translational barriers, connecting the molecular world to the clinical world and vice versa will undoubtedly benefit human health in the near future.

[1]  Je-Gun Joung,et al.  RCARE: RNA Sequence Comparison and Annotation for RNA Editing , 2015, BMC Medical Genomics.

[2]  Bin Chen,et al.  Relating hepatocellular carcinoma tumor samples and cell lines using gene expression data in translational research , 2015, BMC Medical Genomics.

[3]  Jason Y. Liu,et al.  Analysis of genome-wide association study data using the protein knowledge base , 2011, BMC Genetics.

[4]  T. Akutsu,et al.  Parallelization of enumerating tree-like chemical compounds by breadth-first search order , 2015, BMC Medical Genomics.

[5]  Zhaohui S. Qin,et al.  PDEGEM: Modeling non-uniform read distribution in RNA-Seq data , 2015, BMC Medical Genomics.

[6]  M. Carson,et al.  Network-based prediction and knowledge mining of disease genes , 2015, BMC Medical Genomics.

[7]  S. Henikoff,et al.  Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm , 2009, Nature Protocols.

[8]  Yang Liu,et al.  SeedsGraph: an efficient assembler for next-generation sequencing data , 2015, BMC Medical Genomics.

[9]  Sun Kim,et al.  Combined analysis of gene regulatory network and SNV information enhances identification of potential gene markers in mouse knockout studies with small number of samples , 2015, BMC Medical Genomics.

[10]  Jingxue Xin,et al.  Identifying network biomarkers based on protein-protein interactions and expression data , 2015, BMC Medical Genomics.

[11]  G. Yi,et al.  Detection and analysis of disease-associated single nucleotide polymorphism influencing post-translational modification , 2015, BMC Medical Genomics.

[12]  J. Cunningham,et al.  Identification of epigenetic modifications that contribute to pathogenesis in therapy-related AML: Effective integration of genome-wide histone modification with transcriptional profiles , 2015, BMC Medical Genomics.

[13]  J. Choi,et al.  Graph pyramids for protein function prediction , 2015, BMC Medical Genomics.

[14]  Fuxi Zhu,et al.  Identification association of drug-disease by using functional gene module for breast cancer , 2015, BMC Medical Genomics.

[15]  Ju Han Kim,et al.  Interpretation of personal genome sequencing data in terms of disease ranks based on mutual information , 2015, BMC Medical Genomics.

[16]  Yves A Lussier,et al.  Concordance of deregulated mechanisms unveiled in underpowered experiments: PTBP1 knockdown case study , 2014, BMC Medical Genomics.

[17]  Xiao Sun,et al.  Mechanism-anchored profiling derived from epigenetic networks predicts outcome in acute lymphoblastic leukemia , 2009, BMC Bioinformatics.

[18]  Lin Gao,et al.  Inferring drug-disease associations based on known protein complexes , 2015, BMC Medical Genomics.

[19]  Sara Ballouz,et al.  Novel therapeutics for coronary artery disease from genome-wide association study data , 2015, BMC Medical Genomics.