CICC Atom Feed
Tuesday, July 8, 2008
David Wild wins $120k Grant from Lilly for Web Service Devlelopment
Wednesday, April 23, 2008
Two students awarded Symyx PhD Fellowships in Informatics
For full story, see http://www.informatics.indiana.edu/headline.asp.
Cheminformatics student Dazhi Jiao is accepted into the Google Summer of Code
Title | Integration of Cheminformatics Tools in Cytoscape |
---|---|
Student | Dazhi Jiao |
Mentor | John "Scooter" Morris |
Abstract | |
Biological networks that contain small molecules are of special interests in biological and chemical informatics researches. For example, there are some recent research on the relationships of protein networks and ligands networks, based on the facts that proteins with similar binding sites bind similar ligands. This project proposes an addition of cheminformatics tools to Cytoscape to facilitate such research. 1.2D depiction is a basic requirement for chemistry research. I will integrate the 2D depiction functionality of the Chemistry Development Kit (CDK) (http://cdk.sourceforge.net) into Cytoscape, so that users can view the 2D structure of any molecule that has annotation of either SMILES string or other commonly used structure annotations. CDK is an open source Java library for structural cheminformatics and bioinformatics. 2.Linkout to chemical resources that focuses on biological activities of small molecules, sush as PubChem, MACiE, and ChEBI. PubChem (http://pubchem.ncbi.nlm.nih.gov/) is one of the largest chemical databases that provide information on the biological activities of small molecules. MACiE (Machanism, Annotation and Classification in Enzymes) (http://www.ebi.ac.uk/thornton-srv/databases/MACiE/) contains fully annotated enzyme reaction mechanisms, including information on substrates. Chemical Entities of Biological Interest (ChEBI) (http://www.ebi.ac.uk/chebi/) is a dictionary of molecular entities focusing on small molecules. 3.Implementation of a plugin to calculate the Tanimoto similarity between molecules in a network. This will use the implementation of Tanimoto similarity implementation in CDK, which calculates the Tanimoto coefficients based on fingerprints of compounds.(http://www.daylight.com/dayhtml/doc/theory/theory.finger.html) 4.Extend the plugin mentioned in 3 to clustering nodes in a network based on the Tanimoto similarity of molecules that associated with each node. For example, nodes in a protein network can be used to generate a new network based on the Tanimoto similarities between the ligands that bind to the proteins. The mapping of this generated network to the original network might be of interests to researchers. | |
Monday, September 24, 2007
NSF Cyber Enabled Discovery and Innovation
- Knowledge extraction. Knowledge extraction encompasses a variety of techniques-such as data mining, visualization and using basic concepts from computation, geometry and topology that help investigators find what is most important in the nearly infinite amounts of data from sensors, telescopes, satellites, the Internet and surveys. Combining underlying concepts with different types of data from multiple sources, high-bandwidth communications, and tera- to petascale computation power, scientists and engineers can make sense of today's massive volumes of data.
- Interacting elements. Analyzing the flow of electricity or information across the power grid or Internet, describing protein folding and unfolding, and finding principles for scaling from the quantum to the nano to the macro scales, require scientists and engineers to understand interacting systems. Such systems--ranging from atomic particles to galaxies and from computer networks to societies--are at the heart of many science and engineering challenges, and their understanding and control are major sources of innovation. A large number of interacting elements, random interactions and aggregate or emergent phenomena are key factors in such systems. CDI will improve our ability to predict and deduce interactions in complex systems to better understand, design and control them.
- Computational experimentation. Computational experimentation allows insight into complex, real-world systems such as hurricanes, nerve synapse activity, or the Big Bang, by enabling the creation of a virtual description. Simulation and other dynamic modeling techniques allow us to experiment with complex systems in ways that would be unimaginable in the real world. It also lets us guide real-world operations and experimentation in cases that have potential for unforeseen or extreme events. CDI will provide new modeling techniques ranging from mathematical formulations to multiscale simulation techniques.
- Virtual environments. Virtual environments are important mechanisms to enhance discovery, learning and innovation. They permit collaboration among diverse populations spread across geographic distances and at different times. Scheduling and operation of distributed facilities and sensor arrays, data extraction and analysis, international, real-time comparisons of global climate models, and injecting discovery and innovative environments into learning and training all use virtual environments. CDI will develop new techniques for building and utilizing virtual environments, especially in the context of cyberinfrastructure.
- Educating researchers and students in computational discovery. The promise of these new technologies, as well as their diffusion into other segments of the economy, will not be realized without education. CDI will integrate computational discovery techniques into the basic education of all scientists and engineers as well as development of new techniques for using computational discovery in all areas. Special focus will be placed on using virtual environments and cyberinfrastructure at all education levels. By enhancing human cognition and perception addressing complexity, computational tools provide an essential component to workforce development.
Monday, September 17, 2007
Daylight Web Services Demo
Tuesday, September 11, 2007
Fwd: Call for Papers and Invited Sessions Proposals
Wednesday, April 4, 2007
[Fwd: FW: Jean-Claude Bradley's talk at ACS Chicago]
Gary
------------------------------------------------------------------------
*From:* Jean-Claude Bradley [mailto:jeanclaude.bradley@gmail.com]
*Sent:* Tuesday, April 03, 2007 1:57 PM
*To:* Wiggins, Gary D.
*Subject:* Jean-Claude Bradley's talk at ACS Chicago
No problem - talking with you was more interesting than the first
presentation :)
It is always nice to meet the person behind the mailing list.
Thanks for mentioning by blog at your talk!
By the way my talks were recorded and available here in case someone
wants more info:
http://drexel-coas-talks-mp3-podcast.blogspot.com/
On 4/3/07, *Wiggins, Gary D.* < wiggins@indiana.edu
<mailto:wiggins@indiana.edu>> wrote:
I' m sorry that I didn' t realize there was a CHED session on
communication that paralleled the CINF session. I hope that I didn 't
make you late by directing you to our session.
Incidentally, I' ve had a chance to look at your very interesting Useful
Chemistry blog. I plan to talk about it as part of a lectu re I 'll be
giving in Columbus, Ohio on May 8.
Gary
Gary Wiggins
Director, Program in Chemical Informatics
Adjunct Professor of Informatics
School of Informatics
Eigenmann Hall Room 1126
1900 East Tenth Street
Bloomington, Indiana 47406
Phone: 812-856-1086
Fax: 812-856-4764
E-mail: wiggins@indiana.edu <mailto:wiggins@indiana.edu>
--
Jean-Claude Bradley, Ph. D.
E-Learning Coordinator for the College of Arts and Sciences
Associate Professor of Chemistry
Drexel University
http://drexel-coas-elearning.blogspot.com
http://drexel-coas-talks-mp3-podcast.blogspot.com/
http://usefulchem.blogspot.com <http://usefulchem.blogspot.com>