Discovering functional interdependence relationship in PPI networks for protein complex identification.

Protein molecules interact with each other in protein complexes to perform many vital functions, and different computational techniques have been developed to identify protein complexes in protein-protein interaction (PPI) networks. These techniques are developed to search for subgraphs of high connectivity in PPI networks under the assumption that the proteins in a protein complex are highly interconnected. While these techniques have been shown to be quite effective, it is also possible that the matching rate between the protein complexes they discover and those that are previously determined experimentally be relatively low and the "false-alarm" rate can be relatively high. This is especially the case when the assumption of proteins in protein complexes being more highly interconnected be relatively invalid. To increase the matching rate and reduce the false-alarm rate, we have developed a technique that can work effectively without having to make this assumption. The name of the technique called protein complex identification by discovering functional interdependence (PCIFI) searches for protein complexes in PPI networks by taking into consideration both the functional interdependence relationship between protein molecules and the network topology of the network. The PCIFI works in several steps. The first step is to construct a multiple-function protein network graph by labeling each vertex with one or more of the molecular functions it performs. The second step is to filter out protein interactions between protein pairs that are not functionally interdependent of each other in the statistical sense. The third step is to make use of an information-theoretic measure to determine the strength of the functional interdependence between all remaining interacting protein pairs. Finally, the last step is to try to form protein complexes based on the measure of the strength of functional interdependence and the connectivity between proteins. For performance evaluation, PCIFI was used to identify protein complexes in real PPI network data and the protein complexes it found were matched against those that were previously known in MIPS. The results show that PCIFI can be an effective technique for the identification of protein complexes. The protein complexes it found can match more known protein complexes with a smaller false-alarm rate and can provide useful insights into the understanding of the functional interdependence relationships between proteins in protein complexes.

A graph-based algorithm for mining multi-level patterns in genomic data.

Comparative genomics is concerned with the study of genome structure and function of different species. It can provide useful information for the derivation of evolutionary and functional relationships between genomes. Previous work on genome comparison focuses mainly on comparing the entire genomes for visualization without further analysis. As many interesting patterns may exist between genomes and may lead to the discovering of functional gene segments (groups of genes), we propose an algorithm called Multi-Level Genome Comparison Algorithm (MGC) that can be used to facilitate the analysis of genomes at multi-levels during the comparison process to discover sequential and regional consistency in gene segments. Different genomes may have common sub-sequences that differ from each other due to mutations, lateral gene transfers, gene rearrangements, etc., and these sub-sequences are usually not easily identified. Not all the genes can have a perfect one-to-one matching with each other. It is quite possible for one-to-many or many-to-many ambiguous relationships to exist between them. To perform the tasks effectively, MGC takes such ambiguity into consideration during genome comparison by representing genomes in a graph and then make use of a graph mining algorithm called the Multi-Level Attributed Graph Mining Algorithm (MAGMA) to build a hierarchical multi-level graph structure to facilitate genome comparison. To determine the effectiveness of these proposed algorithms, experiments were performed using intra- and inter-species of Microbial genomes. The results show that the proposed algorithms are able to discover multiple level matching patterns that show the similarities and dissimilarities among different genomes, in addition to confirming the specific role of the genes in the genomes.

Discovering interesting molecular substructures for molecular classification.

Given a set of molecular structure data preclassified into a number of classes, the molecular classification problem is concerned with the discovering of interesting structural patterns in the data so that "unseen" molecules not originally in the dataset can be accurately classified. To tackle the problem, interesting molecular substructures have to be discovered and this is done typically by first representing molecular structures in molecular graphs, and then, using graph-mining algorithms to discover frequently occurring subgraphs in them. These subgraphs are then used to characterize different classes for molecular classification. While such an approach can be very effective, it should be noted that a substructure that occurs frequently in one class may also does occur in another. The discovering of frequent subgraphs for molecular classification may, therefore, not always be the most effective. In this paper, we propose a novel technique called mining interesting substructures in molecular data for classification (MISMOC) that can discover interesting frequent subgraphs not just for the characterization of a molecular class but also for the distinguishing of it from the others. Using a test statistic, MISMOC screens each frequent subgraph to determine if they are interesting. For those that are interesting, their degrees of interestingness are determined using an information-theoretic measure. When classifying an unseen molecule, its structure is then matched against the interesting subgraphs in each class and a total interestingness measure for the unseen molecule to be classified into a particular class is determined, which is based on the interestingness of each matched subgraphs. The performance of MISMOC is evaluated using both artificial and real datasets, and the results show that it can be an effective approach for molecular classification.

Ischemic events after carotid interventions in relationship to baseline cerebrovascular reactivity.

BACKGROUND: We aimed to investigate whether baseline cerebrovascular reactivity could predict subsequent ischemic event after intervention and identify the patient group for more aggressive medical and interventional management paradigms. METHODS: Patients with more than 70% cervical carotid stenosis (from ultrasonography) were reviewed. Patients, who had baseline cerebrovascular reactivity test before intervention and had either carotid endarterectomy (CEA) or carotid angioplasty and stenting (CAS) performed, were recruited for analysis. Transcranial Doppler ultrasonography was used to examine the reactivity of the middle cerebral artery in response to 5% carbon dioxide in oxygen. The mean follow up period was 66 months. FINDINGS: Twenty-six patients had symptomatic carotid stenosis and ten patients had asymptomatic carotid stenosis. There were four subsequent ischemic events during follow up. None of the nine patients with impaired baseline ipsilateral cerebrovascular reactivity had subsequent ischemic event. CONCLUSIONS: In this current study, impaired baseline cerebrovascular reactivity did not predict the subsequent stroke risk after carotid intervention. Cerebrovascular reactivity testing may not serve as an indicator for aggressive medical and surgical treatments.