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Keynote Speakers

In order to deepen the communication in all the participants, ICCBB 2017 have invited professors from local USA and all over the world to have speeches about Computational Biology and Bioinformatics and related fields.


Keynote Speaker I

Prof. Jun F. Liang, Stevens Institute of Technology, New Jersey, USA

Recent Publications:
Traba C, Chen L, Azzam R, Liang JF, ¡°Insights into discharge argon-mediated biofilm inactivation¡±. Biofouling, 29:1205-13 (2013).
Chen L, Liang JF, ¡°Improved stability of bioactive peptides by controlling peptide assembling¡±. Biomacromolecules. 14:2326-31(2013).
Chen L, Dong S, Liang JF, ¡°The Effects of Metal Ions on the Cytotoxicity and Selectivity of a Histidine-Containing Lytic Peptides¡± Int. J. Pept Res Ther. 19: 611-623, (2013).
Traba C, Chen L., Liang JF, ¡°Low power gas discharge plasma mediated inactivation and removal of biofilms formed on biomaterials¡±. Cur. Appl. Phys, 13:12-18 (2013).
Chen L., Patrone N., Liang JF, ¡°Peptide self-assembly on cell membranes to induce cell lysis¡±, Biomacromolecules, 13(10):3327-33 (2012)
Chen L, Tu Z Voloshchuk N, Liang JF, ¡°Lytic peptides with improved stability and selectivity designed for cancer treatment¡±. J Pharm Sci. 101(4):1508-17 (2012).
Chen L, Liang JF, ¡°Metabolic monosaccharides altered cell responses to anticancer drugs¡±. Eur J Pharm Biopharm. 81(2):339-45 (2012).
Kharidia R, Tu Z., Chen L., Liang JF, ¡°Activity and Selectivity of Histidine-Containing Lytic Peptides to Antibiotic Resistant Bacteria¡±. Arch Microbiol . 194 (4) 579-685 (2012).
Recently Area:
Nano-Technology Enabled Bacteria and Cancer Cell Sensing. Recently, we are working on a nano-patterning technology which can be used in biosensor and other analytic devices in combination with specific molecules (peptides and signaling massagers) for high sensitivity molecular and cell (bacteria and tumor) sensing. Meanwhile, a novel nano-crystalization technology with targeting and controlled release properties is being studied for drugs (anticancer drugs, antibiotics) with poor solubility and limited therapeutic effectiveness.

Keynote Speaker II

Prof. Hesham H. Ali, University of Nebraska at Omaha, USA

Hesham H. Ali is a Professor of Computer Science and Lee and Wilma Seaman Distinguished Dean of the College of Information Science and Technology at the University of Nebraska at Omaha (UNO). He currently serves as the director of the UNO Bioinformatics Core Facility that supports a large number of biomedical research projects in Nebraska and surrounding region. He has published numerous articles in various IT areas including scheduling, distributed systems, data analytics, wireless networks, and Bioinformatics. He has also published two books in scheduling and graph algorithms, and several book chapters in Bioinformatics. He has been serving as the PI or Co-PI of several projects funded by NSF, NIH and Nebraska Research Initiative in the areas of data analytics, wireless networks and Bioinformatics. He has also been leading a Bioinformatics Research Group that focuses on developing innovative computational approaches to classify biological organisms and analyze big bioinformatics data. The research group is currently developing several next generation big data analytics tools for mining various types of large-scale biological and medical data. This includes the development of new graph theoretic models for assembling short reads obtained from high throughput instruments, as well as employing a novel correlation networks approach for analyzing large heterogeneous biological and health data associated with various biomedical research areas, particularly projects associated with aging and infectious diseases. He has also been leading a multi-disciplinary project for developing secure and energy-aware wireless infrastructure to address tracking and monitoring problems in medical environments, particularly to study mobility profiling of various groups and implement a population analysis approach for healthcare research.

Speech Title: "Next Generation Tools for Big Data Analytics in Bioinformatics"

With the increasing number and sophistication of biomedical instruments and data generation devices, there is an increasing pressure on researchers to develop advanced data analytics tools to extract useful knowledge out of the massive collected data. This includes advanced sequencing technologies responsible for the generation of huge amounts of genomics data as well as wearable devices and Internet of Things systems responsible for collecting different types of health and mobility related data. The currently available data is not only massive in size but it also exhibits all the features associated with big data systems such as high degree of variability, veracity and velocity. Such big biomedical data systems represent great challenges as well as unlimited opportunities to advance biomedical research. Developing innovative data integration, analysis and mining techniques along with clever parallel computational methods to efficiently implement them will be critical in meeting those challenges and take advantage of the potential opportunities. In particular, the use of graph modeling and network analysis as the backbone of big data analytics algorithms promises to play an important role in developing data-driven decision support systems in the next generation of biomedical research. In this talk, we propose new population analysis models and data analytics tools using graph modeling and network analysis along with how to effectively utilize High Performance Computing in implementing such tools. Case studies illustrating how the proposed models and tools are used to analyze data associated with infectious diseases leading to new biological discoveries will also be presented.

Plenary Speaker I

Prof. Ovidiu Daescu, The University of Texas at Dallas, USA

CV is coming soon.

Plenary Speaker II

Assoc. Prof. David E. Breen, Drexel University, USA

David E. Breen is currently an Associate Professor of Computer Science in the College of Computing and Informatics of Drexel University. He has held research positions at the Max Planck Institute for the Physics of Complex Systems, the California Institute of Technology, the European Computer-Industry Research Centre, the Fraunhofer Institute for Computer Graphics, and the Rensselaer Design Research Center. His research interests include computer-aided design, biomedical image informatics, geometric modeling, self-organization and biological simulation. He has authored or co-authored over 100 technical papers, articles and book chapters on these and other subjects. He is the co-editor of the book "Cloth Modeling and Animation" and is a recipient of the prestigious NSF CAREER Award. Breen received a BA in Physics from Colgate University in 1982. He received MS and PhD degrees in Computer and Systems Engineering from Rensselaer Polytechnic Institute in 1985 and 1993.

Speech Title: "Automated Categorization of Drosophila Learning and Memory Using Video Analysis"

The fruit fly, Drosophila melanogaster, is a well established model organism used to study the mechanisms of both learning and memory in vivo. The techniques used to assess these attributes in flies, while powerful, suffer from a lack of speed and quantification. This talk will described an automated method for characterizing this behavior in fruit flies based on analyzing video of their movements. A method is developed to replace and improve a labor-intensive, subjective evaluation process with one that is automated, consistent and reproducible; thus allowing for robust, high-throughput analysis of large quantities of video data.
The method includes identifying individual flies in a video and tracking their motion. Once the flies are identified and tracked, various geometric and dynamic measures may be computed. These data are computed for numerous experimental videos and produce low-dimensional feature vectors that quantify the behavior of the flies. Clustering techniques, e.g., k-means clustering, may then be applied to the feature vectors in order to computationally group each specimen by genotype. Our results show that we are able to automatically differentiate between normal and defective flies. We also generated a Computed Courtship Index (CCI), a computational equivalent of the existing Courtship Index (CI), and compared CCI with CI. These results demonstrate that our automated analysis provides a numerical scoring of fly behavior that is similar to the scoring produced by human observers.




Contact Us

The secretary office of ICCBB 2017 will collect contributions and finish daily organizing work. All paper review process will be completed by Program Committee and Technical Committee Members.

If you have any question, please feel free to contact our conference secretary.

Ms. Amy Lee

Email: iccbb@cbees.net 

In order to deepen the communication in all the participants, ICCBB 2017 is welcomed experts and scholars from all over the world to join in the conference committee.


Important Date

Extended Round

Paper Submission (Full Paper) Before August 20, 2017
Notification of Acceptance September 05, 2017
Final Paper Submission September 15, 2017
Authors' Registration September 15, 2017
Conference Dates October 18-19, 2017
One Day Visit and Tour October 20, 2017

Round III

Paper Submission (Full Paper) Before July 25, 2017
Notification of Acceptance August 15, 2017
Final Paper Submission August 30, 2017
Authors' Registration August 30, 2017
Conference Dates October 18-19, 2017
One Day Visit and Tour October 20, 2017

Round II

Paper Submission (Full Paper) Before June 25, 2017
Notification of Acceptance July 15, 2017
Final Paper Submission August 5, 2017
Authors' Registration August 5, 2017
Conference Dates October 18-19, 2017
One Day Visit and Tour October 20, 2017

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