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,
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.
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).
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.
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
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.
Prof. Ovidiu Daescu, The University of Texas at Dallas, USA
CV is coming soon.
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.