October 15, 2013, the USC Material Doping Databank is online. STEMIDB:STEM Imaging Database. It is a repository for depositing and retrieval of nanoscale images obtained by using scanning transmission electron microscopy (STEM). This is a collaboration prject with Professor Thomas Vogt of the Department of Chemistry at USC May 11, 2012. Logistic Regression based Ensemble server for protein localization prediction (yeast proteins) is online. visit LR ensemble Server

December 12, 2011. Ananda Mondal received his Ph.D. His dissertation is: NETWORK BASED PREDICTION OF PROTEIN LOCALIZATION USING DIFFUSION KERNEL.

Jan 8, 2010, Lewis Cawthorne won the University magellan Scholarship on developing text mining system for protein sorting motif analysis.

Stephanie won the University magellan Scholarship on developing webservers for protein sorting databases.

Aug 1, 2009. Dr. Hu was awarded NSF Career Award for project: Computational Analysis and Prediction of Genome-Wide Protein Targeting Signals and Localization.
This is a CAREER award to support the research of Dr. Jianjun Hu, in the Department of Computer Science and Engineering at University of South Carolina. Dr. Hu is a second-year, tenure-track Assistant Professor. A typical cell has a size of only 10 microns while it contains about a billion proteins. How these proteins are transported from their synthesis sites to their target locations within or outside of the cell is still not well understood. Experiments showed that translocation of nascent proteins are usually guided by postal code-like targeting signals encoded within the amino acid sequences of proteins. Genome-wide identification and decoding of these so-called molecular zip codes are fundamental to comprehensive understanding of the cell. Experimentally identifying protein targeting signals is labor-intensive. Computational prediction of targeting signals is still a big challenge due to their low conservation at the amino acid level. Currently, no de novo discovery algorithm is available for identifying new protein targeting signals. Also missing are appropriate models and algorithms for comparing these signals. This grant is 1) investigating novel computational algorithms for de novo discovery of new protein targeting signals; 2) developing models and algorithms for representing, detecting, and comparing targeting signals and 3) developing a protein functional network-based integrative algorithms for protein localization prediction. A transformative result of these studies will be a sequence encoding scheme based on amino acid indexes. This scheme will convert protein sequences into sequences of amino acid groups (AAGs) such that conserved patterns can be represented, modeled and discovered. Finally, protein function networks will be derived from models of protein localization prediction. With this research, computational identification and decoding of genome-wide protein targeting signals and precise protein localization predication will greatly enhance the understanding of how proteins are assembled in a cell. Tools developed during this project will be made available on the lab website: As a part of his CAREER grant, Dr. Hu will conduct short-term projects and student-run seminars to bring undergraduates into the bioinformatics research. A special effort will be made to change the perception that computer science is debugging code, as perceived by many high-school students. A novel computer game will be employed to show how bioinformatics addresses real-world problems. This will raise the public and especially the awareness and interest of K-12 students in bioinformatics. Students in the NSF STARTS Alliance program at the University of South Carolina will be targeted for students. Mini programming problems with a bioinformatics background will be developed for lower-level college students so that they will be exposed to bioinformatics early in their introductory programming courses. This project will also develop bioinformatics web services for de novo discovery, comparison, and retrieval of protein targeting signals and precise protein localization prediction.
For more detail see: Visit NSF Award page now.

July 28, 2009. AAEnrich: Identifying enriched amino acid compostions with special physichemical properties for protein sorting motifs
This server will allow one to identify whether there are over-representation of a special class of amino acids exisitng in a group of protein sequences. E.g. secretary sorting motifs will be shown to have over-represented hydrophobic and polar amino acids.!
Visit AAEnrich server now.

June 1, 2009. Stephanie Henrichs joins us for her 2-month summer REU intern.
She will be working on protein protein interaction for sorting motif analysis. She has programming experience in java, c/C++ and background knowledge in molecular biology. welcome!

March 2009. Annda Mondal joined our lab!
He will work on data mining and machine learning in virtual screening problem. He has been working on a pipeline for virtual screening 8 millions ligands for a therepeutic target of breast cancer.

January 2009. Dr. Hu's new book published: Genetic Programming and Creative Design of Mechatronic Systems, China Machine Press, Beijing China.
This book introduces the techniques of using evolutionary genetic programming for engineering design innovation.This book is published in Chinese.

2009.1 Joint paper Disease diagnosis using multi-microarray datasets published in BMC bioinformatics.

August, 2008: Fan and Jia join the MLEG lab, Welcome!
June 18, 2008. New software Online metaP: meta-server for protein localization prediction