Showing papers by "Dongsoo Han published in 2003"

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

Abstract: 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.

Exception specification and handling in workflow systems

Abstract: Various unexpected events frequently happen in workflow system supporting web-based business processes. Thus workflow system should be equipped with handlers to cope with the unexpected events. But in practical terms, we cannot expect for a workflow system to prepare all the handlers for events that might potentially occur. It is more reasonable to let process designers specify exceptional situations and define corresponding exception handlers at process build time. At that time, when exceptional events occur, the workflow system detects the exceptions and invokes corresponding exception handlers. To support this mechanism, a workflow system should provide a means of specifying exceptions and facilities to detect exceptions and invoke corresponding exception handlers. In this paper, we devise an exception specification method using an event-transition approach and its handling mechanism using a design pattern. Detecting exceptions and mechanism for invoking exception-handling routines are developed and incorporated into our research workflow system (ICU/COWS).

Hyper-BLAST: a parallelized BLAST on cluster system

Abstract: BLAST is an important tool in bioinformatics. It has been used to find biologically similar sequences to the given query sequence from the database of the annotated sequences. For high throughput processing of huge number of query sequences, there have been many studies on parallel batch processing of sequence similarity search using BLAST. As the number of sequences in the database increases at exponential rate, the search speed of BLAST itself becomes important. Although NCBI has developed a parallel BLAST using the thread on SMP machines for the speedup of BLAST, the speedup is still limited because the SMP machine has restricted the number of processors due to its architecture. In this paper, we present our parallelized BLAST on cluster systems for further speedup. The main strategy used is the exploitation of the inter-node parallelism, which can be extracted by logical partitioning of the database. For the inter-node parallelism, we have designed and implemented a logical database partitioning method, initiation and coordination of the BLAST on remote node and communication protocol for collecting remote node's result. According to our performance test with 2-way 8 node cluster system, roughly 12 times speedup has been achieved in terms of response time of similarity search for individual query sequence.