Biomedical subjects

See-Kiong Ng

Publications and source records attributed to See-Kiong Ng.

13 recordsLinked to original sources

PubMed2006

Systematic gene function prediction from gene expression data by using a fuzzy nearest-cluster method.

BACKGROUND: Quantitative simultaneous monitoring of the expression levels of thousands of genes under various experimental conditions is now possible using microarray experiments. However, there are still gaps toward whole-genome functional annotation of genes using the gene expression data. RESULTS: In this paper, we propose a novel technique called Fuzzy Nearest Clusters for genome-wide functional annotation of unclassified genes. The technique consists of two steps: an initial hierarchical clustering step to detect homogeneous co-expressed gene subgroups or clusters in each possibly heterogeneous functional class; followed by a classification step to predict the functional roles of the unclassified genes based on their corresponding similarities to the detected functional clusters. CONCLUSION: Our experimental results with yeast gene expression data showed that the proposed method can accurately predict the genes' functions, even those with multiple functional roles, and the prediction performance is most independent of the underlying heterogeneity of the complex functional classes, as compared to the other conventional gene function prediction approaches.

Algorithms↗

PubMed2006

A correlated motif approach for finding short linear motifs from protein interaction networks.

BACKGROUND: An important class of interaction switches for biological circuits and disease pathways are short binding motifs. However, the biological experiments to find these binding motifs are often laborious and expensive. With the availability of protein interaction data, novel binding motifs can be discovered computationally: by applying standard motif extracting algorithms on protein sequence sets each interacting with either a common protein or a protein group with similar properties. The underlying assumption is that proteins with common interacting partners will share some common binding motifs. Although novel binding motifs have been discovered with such approach, it is not applicable if a protein interacts with very few other proteins or when prior knowledge of protein group is not available or erroneous. Experimental noise in input interaction data can further deteriorate the dismal performance of such approaches. RESULTS: We propose a novel approach of finding correlated short sequence motifs from protein-protein interaction data to effectively circumvent the above-mentioned limitations. Correlated motifs are those motifs that consistently co-occur only in pairs of interacting protein sequences, and could possibly interact with each other directly or indirectly to mediate interactions. We adopted the (l, d)-motif model and formulate finding the correlated motifs as an (l, d)-motif pair finding problem. We present both an exact algorithm, D-MOTIF, as well as its approximation algorithm, D-STAR to solve this problem. Evaluation on extensive simulated data showed that our approach not only eliminated the need for any prior protein grouping, but is also more robust in extracting motifs from noisy interaction data. Application on two biological datasets (SH3 interaction network and TGFbeta signaling network) demonstrates that the approach can extract correlated motifs that correspond to actual interacting subsequences. CONCLUSION: The correlated motif approach outlined in this paper is able to find correlated linear motifs from sparse and noisy interaction data. This, in turn, will expedite the discovery of novel linear binding motifs, and facilitate the studies of biological pathways mediated by them.

Algorithms↗

PubMed2006

Increasing confidence of protein interactomes using network topological metrics.

MOTIVATION: Experimental limitations in high-throughput protein-protein interaction detection methods have resulted in low quality interaction datasets that contained sizable fractions of false positives and false negatives. Small-scale, focused experiments are then needed to complement the high-throughput methods to extract true protein interactions. However, the naturally vast interactomes would require much more scalable approaches. RESULTS: We describe a novel method called IRAP* as a computational complement for repurification of the highly erroneous experimentally derived protein interactomes. Our method involves an iterative process of removing interactions that are confidently identified as false positives and adding interactions detected as false negatives into the interactomes. Identification of both false positives and false negatives are performed in IRAP* using interaction confidence measures based on network topological metrics. Potential false positives are identified amongst the detected interactions as those with very low computed confidence values, while potential false negatives are discovered as the undetected interactions with high computed confidence values. Our results from applying IRAP* on large-scale interaction datasets generated by the popular yeast-two-hybrid assays for yeast, fruit fly and worm showed that the computationally repurified interaction datasets contained potentially lower fractions of false positive and false negative errors based on functional homogeneity. AVAILABILITY: The confidence indices for PPIs in yeast, fruit fly and worm as computed by our method can be found at our website http://www.comp.nus.edu.sg/~chenjin/fpfn.

Animals↗

PubMed2005

BioContrasts: extracting and exploiting protein-protein contrastive relations from biomedical literature.

MOTIVATION: Contrasts are useful conceptual vehicles for learning processes and exploratory research of the unknown. For example, contrastive information between proteins can reveal what similarities, divergences and relations there are of the two proteins, leading to invaluable insights for better understanding about the proteins. Such contrastive information are found to be reported in the biomedical literature. However, there have been no reported attempts in current biomedical text mining work that systematically extract and present such useful contrastive information from the literature for exploitation. RESULTS: Our BioContrasts system extracts protein-protein contrastive information from MEDLINE abstracts and presents the information to biologists in a web-application for exploitation. Contrastive information are identified in the text abstracts with contrastive negation patterns such as 'A but not B'. A total of 799 169 pairs of contrastive expressions were successfully extracted from 2.5 million MEDLINE abstracts. Using grounding of contrastive protein names to Swiss-Prot entries, we were able to produce 41 471 pieces of contrasts between Swiss-Prot protein entries. These contrastive pieces of information are then presented via a user-friendly interactive web portal that can be exploited for applications such as the refinement of biological pathways. AVAILABILITY: BioContrasts can be accessed at http://biocontrasts.i2r.a-star.edu.sg. It is also mirrored at http://biocontrasts.biopathway.org. SUPPLEMENTARY INFORMATION: Supplementary materials are available at Bioinformatics online.

Artificial Intelligence↗

PubMed2005

Interaction graph mining for protein complexes using local clique merging.

While recent technological advances have made available large datasets of experimentally-detected pairwise protein-protein interactions, there is still a lack of experimentally-determined protein complex data. To make up for this lack of protein complex data, we explore the mining of existing protein interaction graphs for protein complexes. This paper proposes a novel graph mining algorithm to detect the dense neighborhoods (highly connected regions) in an interaction graph which may correspond to protein complexes. Our algorithm first locates local cliques for each graph vertex (protein) and then merge the detected local cliques according to their affinity to form maximal dense regions. We present experimental results with yeast protein interaction data to demonstrate the effectiveness of our proposed method. Compared with other existing techniques, our predicted complexes can match or overlap significantly better with the known protein complexes in the MIPS benchmark database. Novel protein complexes were also predicted to help biologists in their search for new protein complexes.

Algorithms↗

PubMed2004

InterWeaver: interaction reports for discovering potential protein interaction partners with online evidence.

InterWeaver is a web server for discovering potential protein interactions with online evidence automatically extracted from protein interaction databases, literature abstracts, domain fusion events and domain interactions. Given a new protein sequence, the server identifies potential interaction partners using two approaches. In the homology-based approach, the system performs sequence homology searches to find similar proteins in other species, and then searches the protein interaction databases and the biomedical literature for interaction partners. In the domain-based approach, the system detects the domains in the input protein sequence and searches databases of domain fusion events and putative domain interactions to suggest potential interacting partners. The results are compiled into a personalized and downloadable interaction report to aid biologists in their discovery of protein interactions. InterWeaver is freely available for academic users at http://interweaver.i2r.a-star.edu.sg/.

Computational Biology↗

PubMed2004

ADVICE: Automated Detection and Validation of Interaction by Co-Evolution.

ADVICE (Automated Detection and Validation of Interaction by Co-Evolution) is a web tool for predicting and validating protein-protein interactions using the observed co-evolution between interacting proteins. Interacting proteins are known to share similar evolutionary histories since they undergo coordinated evolutionary changes to preserve interactions and functionalities. The web tool automates a commonly adopted methodology to quantify the similarities in proteins' evolutionary histories for postulating potential protein-protein interactions. ADVICE can also be used to validate experimental data against spurious protein interactions by identifying those that have few similarities in their evolutionary histories. The web tool accepts a list of protein sequences or sequence pairs as input and retrieves orthologous sequences to compute the similarities in the proteins' evolutionary histories. To facilitate hypothesis generation, detected co-evolved proteins can be visualized as a network at the website. ADVICE is available at http://advice.i2r.a-star.edu.sg.

Evolution, Molecular↗

PubMed2004

Discovering protein-protein interactions.

The ongoing genomics and proteomics efforts have helped identify many new genes and proteins in living organisms. However, simply knowing the existence of genes and proteins does not tell us much about the biological processes in which they participate. Many major biological processes are controlled by protein interaction networks. A comprehensive description of protein-protein interactions is therefore necessary to understand the genetic program of life. In this tutorial, we provide an overview of the various current high-throughput methods for discovering protein-protein interactions, covering both the conventional experimental methods and new computational approaches.

Artificial Gene Fusion↗

PubMed2003

Integrative approach for computationally inferring protein domain interactions.

MOTIVATION: The current need for high-throughput protein interaction detection has resulted in interaction data being generated en masse through such experimental methods as yeast-two-hybrids and protein chips. Such data can be erroneous and they often do not provide adequate functional information for the detected interactions. Therefore, it is useful to develop an in silico approach to further validate and annotate the detected protein interactions. RESULTS: Given that protein-protein interactions involve physical interactions between protein domains, domain-domain interaction information can be useful for validating, annotating, and even predicting protein interactions. However, large-scale, experimentally determined domain-domain interaction data do not exist. Here, we describe an integrative approach to computationally derive putative domain interactions from multiple data sources, including protein interactions, protein complexes, and Rosetta Stone sequences. We further prove the usefulness of such an integrative approach by applying the derived domain interactions to predict and validate protein-protein interactions. AVAILABILITY: A database of putative protein domain interactions derived using the method described in this paper is available at http://interdom.lit.org.sg.

Algorithms↗

PubMed2003

InterDom: a database of putative interacting protein domains for validating predicted protein interactions and complexes.

Advances in proteomics technology have enabled new proteins to be discovered at an unprecedented speed, and high throughput experimental methods have been developed to detect protein interactions and complexes en masse. Such bottom-up, data-driven approach has resulted in data that may be uninformative or potentially errorful, requiring further validation and annotation. The InterDom database focuses on providing supporting evidence for the detected protein interactions based on putative protein domain interactions. Using an integrative approach, InterDom derives potential domain interactions by combining data from multiple sources, ranging from domain fusions, protein interactions and complexes, to scientific literature. The InterDom database is available at http://InterDom.lit.org.sg.

Databases, Protein↗

PubMed2003

Discovery of significant rules for classifying cancer diagnosis data.

METHODS AND RESULTS: We introduce a new method to discover many diversified and significant rules from high dimensional profiling data. We also propose to aggregate the discriminating power of these rules for reliable predictions. The discovered rules are found to contain low-ranked features; these features are found to be sometimes necessary for classifiers to achieve perfect accuracy. The use of low-ranked but essential features in our method is in contrast to the prevailing use of an ad-hoc number of only top-ranked features. On a wide range of data sets, our method displayed highly competitive accuracy compared to the best performance of other kinds of classification models. In addition to accuracy, our method also provides comprehensible rules to help elucidate the translation between raw data and useful knowledge.

Algorithms↗

PubMed2003

On combining multiple microarray studies for improved functional classification by whole-dataset feature selection.

As microarray technologies become routinely applied in genome laboratories for studying gene expression, it is not uncommon that experiments on identical or similar sets of genes are conducted by multiple laboratories for various functional studies of these genes. Much of such data are often available to researchers for their data analysis, either through collaborators or from online gene expression databases. It will be useful to combine data from different microarray studies to improve the microarray data mining results. We show that the functional classification of genes from microarray data can be improved further by combining gene expression data from multiple microarray studies, even if the experimental focus or conditions for each experimental study may differ. However, blindly combining all available datasets may not always improve the analysis results---it is important to be selective of the datasets for inclusion. In our approach, we consider each dataset to be one feature, and then apply feature selection strategies to select appropriate datasets for training. With a simple hill-climbing method, we show that gene classification performances can be improved by whole-dataset feature selection.

Algorithms↗

PubMedDate not supplied

Discovering reliable protein interactions from high-throughput experimental data using network topology.

OBJECTIVE: Current protein-protein interaction (PPI) detection via high-throughput experimental methods, such as yeast-two-hybrid has been reported to be highly erroneous, leading to potentially costly spurious discoveries. This work introduces a novel measure called IRAP, i.e. "interaction reliability by alternative path", for assessing the reliability of protein interactions based on the underlying topology of the PPI network. METHODS AND MATERIALS: A candidate PPI is considered to be reliable if it is involved in a closed loop in which the alternative path of interactions between the two interacting proteins is strong. We devise an algorithm called AlternativePathFinder to compute the IRAP value for each interaction in a complex PPI network. Validation of the IRAP as a measure for assessing the reliability of PPIs is performed with extensive experiments on yeast PPI data. All the data used in our experiments can be downloaded from our supplementary data web site at . RESULTS: Results show consistently that IRAP measure is an effective way for discovering reliable PPIs in large datasets of error-prone experimentally-derived PPIs. Results also indicate that IRAP is better than IG2, and markedly better than the more simplistic IG1 measure. CONCLUSION: Experimental results demonstrate that a global, system-wide approach-such as IRAP that considers the entire interaction network instead of merely local neighbors-is a much more promising approach for assessing the reliability of PPIs.

Algorithms↗