Information & Telecommunication Technology Center
https://hdl.handle.net/1808/284
2024-03-28T16:20:52ZKUPS: constructing datasets of interacting and non-interacting protein pairs with associated attributions
https://hdl.handle.net/1808/13489
KUPS: constructing datasets of interacting and non-interacting protein pairs with associated attributions
Chen, Xue-wen; Jeong, Jong Cheol; Dermyer, Patrick
KUPS (The University of Kansas Proteomics Service) provides high-quality protein–protein interaction (PPI) data for researchers developing and evaluating computational models for predicting PPIs by allowing users to construct ready-to-use data sets of interacting protein pairs (IPPs), non-interacting protein pairs (NIPs) and associated features. Multiple filters and options allow the user to control the make-up of the IPPs and NIPs as well as the quality of the resultant data sets. Each data set is built from the overall database, which includes 185 446 IPPs and ∼1.5 billion NIPs from five primary databases: IntAct, HPRD, MINT, UniProt and the Gene Ontology. The IPP set can be set to specific model organisms, interaction types and experimental evidence. The NIP set can be generated using four different strategies, which can alleviate biased estimation problems. Lastly, multiple features can be provided for all of the IPP and NIP pairs. Additionally, KUPS provides two benchmark data sets to help researchers compare their algorithms to existing approaches. KUPS is freely available at http://www.ittc.ku.edu/chenlab.
2010-09-30T00:00:00ZProtein Function Assignment through Mining Cross-Species Protein-Protein Interactions
https://hdl.handle.net/1808/13187
Protein Function Assignment through Mining Cross-Species Protein-Protein Interactions
Chen, Xue-wen; Liu, Mei; Ward, Robert E., IV
Background
As we move into the post genome-sequencing era, an immediate challenge is how to make best use of the large amount of high-throughput experimental data to assign functions to currently uncharacterized proteins. We here describe CSIDOP, a new method for protein function assignment based on shared interacting domain patterns extracted from cross-species protein-protein interaction data.
Methodology/Principal Findings
The proposed method is assessed both biologically and statistically over the genome of H. sapiens. The CSIDOP method is capable of making protein function prediction with accuracy of 95.42% using 2,972 gene ontology (GO) functional categories. In addition, we are able to assign novel functional annotations for 181 previously uncharacterized proteins in H. sapiens. Furthermore, we demonstrate that for proteins that are characterized by GO, the CSIDOP may predict extra functions. This is attractive as a protein normally executes a variety of functions in different processes and its current GO annotation may be incomplete.
Conclusions/Significance
It can be shown through experimental results that the CSIDOP method is reliable and practical in use. The method will continue to improve as more high quality interaction data becomes available and is readily scalable to a genome-wide application.
2008-02-06T00:00:00ZSource-to-Source Refactoring and Elimination of Global Variables in C Programs
https://hdl.handle.net/1808/11547
Source-to-Source Refactoring and Elimination of Global Variables in C Programs
Sankaranarayanan, Hemaiyer; Kulkarni, Prasad A.
A global variable in C/C++ is one that is declared outside a function, and whose scope extends the lifetime of the entire
program. Global variables cause problems for program dependability, maintainability, extensibility, verification, and
thread-safety. However, global variables can also make co
ding more convenient and improve program performance. We
have found the use of global variables to remain unabated and
extensive in real-world software. In this paper we present
a source-to-source refactoring tool to au
tomatically detect and localize global variables in a program. We implement a
compiler based transformation to find the
best location to redefine each global va
riable as a local. For each global, our
algorithm initializes the corresponding new local variable, pa
sses it as an argument to necessary functions, and updates
the source lines that used the global to now instead use th
e corresponding local or argumen
t. We also characterize the
use of global variables in standard benchmark programs. We study the effect of our transformation on static program
properties, such as change in the number of function ar
guments and program state visibility. Additionally, we quantify
dynamic program features, including memory and runtime performance, before and after our localizing transformation.
A grant from the One-University Open Access Fund at the University of Kansas was used to defray the author’s publication fees in this Open Access journal. The Open Access Fund, administered by librarians from the KU, KU Law, and KUMC libraries, is made possible by contributions from the offices of KU Provost, KU Vice Chancellor for Research & Graduate Studies, and KUMC Vice Chancellor for Research. For more information about the Open Access Fund, please see http://library.kumc.edu/authors-fund.xml.
2013-05-01T00:00:00Z