Biomedical subjects

Hong-Soog Kim

Publications and source records attributed to Hong-Soog Kim.

4 recordsLinked to original sources

PubMed2005

Inter-species validation for domain combination based protein-protein interaction prediction method.

Domain Combination based Protein-Protein Interaction Prediction (DCPPIP) method is revealed to show outstanding prediction accuracy in Yeast proteins. However, it is not yet apparent whether the method is still valid and can achieve comparable prediction accuracy for the proteins in other species. In this paper, we report the validation results of applying the DCPPIP method for Fly and Human proteins. We also report the results of inter-species validation, in which protein interaction and domain data of other species are used as learning set. 10,351 interacting protein pairs are used for the validation for Fly, 2,345 protein pairs for Human. 80% of the data are used as learning sets and 20% are reserved as test sets. High prediction accuracies (Fly: sensitivity approximately 77%, specificity approximately 92%, Human: sensitivity approximately 96%, specificity approximately 95%) are achieved in both Fly and Human cases. Interactions of proteins in Human, Mouse, H. pylori, E. coli, and C. elegans are predicted and validated using the protein interaction and domain data in Yeast, Fly, and the combination of Yeast and Fly respectively. Again, good prediction accuracy is achieved when the test protein pair has common domains with the proteins in a learning set of proteins. A notion of Domain Overlapping Rate (DOR) among species is newly developed in this paper and the correlation between DOR and prediction accuracy is examined. According to out test results, there exists fairly obvious correlation between DOR and prediction accuracy.

Animals↗

PubMed2004

PreSPI: a domain combination based prediction system for protein-protein interaction.

With the accumulation of protein and its related data on the Internet, many domain-based computational techniques to predict protein interactions have been developed. However, most techniques still have many limitations when used in real fields. They usually suffer from low accuracy in prediction and do not provide any interaction possibility ranking method for multiple protein pairs. In this paper, we propose a probabilistic framework to predict the interaction probability of proteins and develop an interaction possibility ranking method for multiple protein pairs. Using the ranking method, one can discern the protein pairs that are more likely to interact with each other in multiple protein pairs. The validity of the prediction model was evaluated using an interacting set of protein pairs in yeast and an artificially generated non-interacting set of protein pairs. When 80% of the set of interacting protein pairs in the DIP (Database of Interacting Proteins) was used as a learning set of interacting protein pairs, high sensitivity (77%) and specificity (95%) were achieved for the test groups containing common domains with the learning set of proteins within our framework. The stability of the prediction model was also evident when tested over DIP CORE, HMS-PCI and TAP data. In the validation of the ranking method, we reveal that some correlations exist between the interacting probability and the accuracy of the prediction.

Computational Biology↗

PubMed2004

PreSPI: design and implementation of protein-protein interaction prediction service system.

With the recognition of the importance of computational approach for protein-protein interaction prediction, many techniques have been developed to computationally predict protein-protein interactions. However, few techniques are actually implemented and announced in service form for general users to readily access and use the techniques. In this paper, we design and implement a protein interaction prediction service system based on the domain combination based protein-protein interaction prediction technique, which is known to show superior accuracy to other conventional computational protein-protein interaction prediction methods. In the prediction accuracy test of the method, high sensitivity (77%) and specificity (95%) are achieved for test protein pairs containing common domains with learning sets of proteins in a Yeast. The stability of the method is also manifested through the testing over DIP CORE, HMS-PCI, and TAP data. The functions of the system are divided into core, subsidiary, and general service function categories. The core function category includes the functions that can be provided only by using the domain combination based protein-protein interaction prediction method. Interaction prediction for a single protein pair and visualization of interaction probability distributions are the functions in this category. The subsidiary function category includes the functions that can be derived from the core functions. Domain combination pair search with high appearance probability and construction of protein interaction network are the functions in this category. Lastly, the general service function category includes the functions that can be implemented by collecting and organizing the protein and domain data in the Internet. Performance, openness and flexibility are the major design goals and they are achieved by adopting parallel execution techniques, Web Services standards, and layered architecture respectively. In this paper, several representative user interfaces of the system are also introduced with comprehensive usage guides.

Computational Biology↗

PubMed2003

A domain combination based probabilistic framework for protein-protein interaction prediction.

In this paper, we propose a probabilistic framework to predict the interaction probability of proteins. The notion of domain combination and domain combination pair is newly introduced and the prediction model in the framework takes domain combination pair as a basic unit of protein interactions to overcome the limitations of the conventional domain pair based prediction systems. The framework largely consists of prediction preparation and service stages. In the prediction preparation stage, two appearance probability matrices are constructed. Each matrix holds information on appearance frequencies of domain combination pairs in the interacting and non-interacting sets of protein pairs, respectively. Based on the appearance probability matrix, a probability equation is devised. The equation maps a protein pair to a real number in the range of 0 to 1. Two distributions of interacting and non-interacting sets of protein pairs are obtained using the equation. In the prediction service stage, the interaction probability of a protein pair is predicted using the distributions and the equation. The validity of the prediction model is evaluated for the interacting set of protein pairs in a Yeast organism and artificially generated non-interacting set of protein pairs. When 80% of the set of interacting protein pairs in DIP (Database of Interacting Proteins) is used as a learning set of interacting protein pairs, very high sensitivity (86%) and moderate specificity (56%) are achieved within our framework.

Amino Acid Sequence↗