Challenges of the next decade for the Asia Pacific region: 2010 International Conference in Bioinformatics (InCoB 2010)

The 2010 annual conference of the Asia Pacific Bioinformatics Network (APBioNet), Asia’s oldest bioinformatics organisation formed in 1998, was organized as the 9th International Conference on Bioinformatics (InCoB), Sept. 26-28, 2010 in Tokyo, Japan. Initially, APBioNet created InCoB as forum to foster bioinformatics in the Asia Pacific region. Given the growing importance of interdisciplinary research, InCoB2010 included topics targeting scientists in the fields of genomic medicine, immunology and chemoinformatics, supporting translational research. Peer-reviewed manuscripts that were accepted for publication in this supplement, represent key areas of research interests that have emerged in our region. We also highlight some of the current challenges bioinformatics is facing in the Asia Pacific region and conclude our report with the announcement of APBioNet’s 100 BioDatabases (BioDB100) initiative. BioDB100 will comply with the database criteria set out earlier in our proposal for Minimum Information about a Bioinformatics and Investigation (MIABi), setting the standards for biocuration and bioinformatics research, on which we will report at the next InCoB, Nov. 27 – Dec. 2, 2011 at Kuala Lumpur, Malaysia

InCoB2010 - 9thInternational Conference on Bioinformatics at Tokyo, Japan, September 26-28, 2010

10.1186/1471-2105-11-S7-S1BMC Bioinformatics11SUPPL. 7-BBMI

InCoB celebrates its tenth anniversary as first joint conference with ISCB-Asia

In 2009 the International Society for Computational Biology (ISCB) started to roll out regional bioinformatics conferences in Africa, Latin America and Asia. The open and competitive bid for the first meeting in Asia (ISCB-Asia) was awarded to Asia-Pacific Bioinformatics Network (APBioNet) which has been running the International Conference on Bioinformatics (InCoB) in the Asia-Pacific region since 2002. InCoB/ISCB-Asia 2011 is held from November 30 to December 2, 2011 in Kuala Lumpur, Malaysia. Of 104 manuscripts submitted to BMC Genomics and BMC Bioinformatics conference supplements, 49 (47.1%) were accepted. The strong showing of Asia among submissions (82.7%) and acceptances (81.6%) signals the success of this tenth InCoB anniversary meeting, and bodes well for the future of ISCB-Asia

Bionanomedicine: A “Panacea” In Medicine?

Recent advances in nanotechnology, biotechnology, bioinformatics, and materials science have prompted novel developments in the field of nanomedicine. Enhancements in the theranostics, computational information, and management of diseases/disorders are desperately required. It may now be conceivable to accomplish checked improvements in both of these areas utilising nanomedicine. This scientific and concise review concentrates on the fundamentals and potential of nanomedicine, particularly nanoparticles and their advantages, nanoparticles for siRNA conveyance, nanopores, nanodots, nanotheragnostics, nanodrugs and targeting mechanisms, and aptamer nanomedicine. The combination of various scientific fields is quickening these improvements, and these interdisciplinary endeavours to have significant progressively outstretching influences on different fields of research. The capacities of nanomedicine are immense, and nanotechnology could give medicine a completely new standpoint

Qualitative reasoning of dynamic gene regulatory interactions from gene expression data

<p>Abstract</p> <p>Background</p> <p>A gene regulatory relation often changes over time rather than being constant. But many gene regulatory networks available in databases or literatures are static in the sense that they are either snapshots of gene regulatory relations at a time point or union of successive gene regulations over time. Such static networks cannot represent temporal aspects of gene regulatory interactions such as the order of gene regulations or the pace of gene regulations.</p> <p>Results</p> <p>We developed a new qualitative method for representing dynamic gene regulatory relations and algorithms for identifying dynamic gene regulations from the time-series gene expression data using two types of scores. The identified gene regulatory interactions and their temporal properties are visualized as a gene regulatory network. All the algorithms have been implemented in a program called GeneNetFinder (<url>http://wilab.inha.ac.kr/genenetfinder/</url>) and tested on several gene expression data.</p> <p>Conclusions</p> <p>The dynamic nature of dynamic gene regulatory interactions can be inferred and represented qualitatively without deriving a set of differential equations describing the interactions. The approach and the program developed in our study would be useful for identifying dynamic gene regulatory interactions from the large amount of gene expression data available and for analyzing the interactions.</p