Biomedical subjects

Kian-Lee Tan

Publications and source records attributed to Kian-Lee Tan.

5 recordsLinked to original sources

PubMed2005

Automatic 3D protein structure classification without structural alignment.

In this paper, we present a new scheme named ProtClass for automatic classification of three-dimensional (3D) protein structures. It is a dedicated and unified multiclass classification scheme. Neither detailed structural alignment nor multiple binary classifications are required in this scheme. We adopt a nearest neighbor-based classification strategy. We use a filter-and-refine scheme. In the first step, we filter out the improbable answers using the precalculated parameters from the training data. In the second, we perform a relatively more detailed nearest neighbor search on the remaining answers. We use very concise and effective encoding schemes of the 3D protein structures in both steps. We compare our proposed method against two other dedicated protein structure classification schemes, namely SGM and CPMine. The experimental results show that ProtClass is slightly better in accuracy than SGM and much faster. In comparison with CPMine, ProtClass is much more accurate, while their running times are about the same. We also compare ProtClass against a structural alignment-based classification scheme named DALI, which is found to be more accurate, but extremely slow. The software is available upon request from the authors. The supplementary information on ProtClass method can be found at: http://xena1.ddns.comp.nus.edu.sg/ approximately genesis/PClass.htm.

Algorithms↗

PubMed2004

Identifying time-lagged gene clusters using gene expression data.

MOTIVATION: Analysis of gene expression data can provide insights into the time-lagged co-regulation of genes/gene clusters. However, existing methods such as the Event Method and the Edge Detection Method are inefficient as they compare only two genes at a time. More importantly, they neglect some important information due to their scoring criterian. In this paper, we propose an efficient algorithm to identify time-lagged co-regulated gene clusters. The algorithm facilitates localized comparison and processes several genes simultaneously to generate detailed and complete time-lagged information for genes/gene clusters. RESULTS: We experimented with the time-series Yeast gene dataset and compared our algorithm with the Event Method. Our results show that our algorithm is not only efficient, but also delivers more reliable and detailed information on time-lagged co-regulation between genes/gene clusters. AVAILABILITY: The software is available upon request. CONTACT: jiliping@comp.nus.edu.sg SUPPLEMENTARY INFORMATION: Supplementary tables and figures for this paper can be found at http://www.comp.nus.edu.sg/~jiliping/p2.htm.

Cluster Analysis↗

PubMed2004

Mining gene expression data for positive and negative co-regulated gene clusters.

MOTIVATION: Analysis of gene expression data can provide insights into the positive and negative co-regulation of genes. However, existing methods such as association rule mining are computationally expensive and the quality and quantities of the rules are sensitive to the support and confidence values. In this paper, we introduce the concept of positive and negative co-regulated gene cluster (PNCGC) that more accurately reflects the co-regulation of genes, and propose an efficient algorithm to extract PNCGCs. RESULTS: We experimented with the Yeast dataset and compared our resulting PNCGCs with the association rules generated by the Apriori mining algorithm. Our results show that our PNCGCs identify some missing co-regulations of association rules, and our algorithm greatly reduces the large number of rules involving uncorrelated genes generated by the Apriori scheme. AVAILABILITY: The software is available upon request.

Algorithms↗

PubMed2004

Rapid 3D protein structure database searching using information retrieval techniques.

MOTIVATION: As the sizes of three-dimensional (3D) protein structure databases are growing rapidly nowadays, exhaustive database searching, in which a 3D query structure is compared to each and every structure in the database, becomes inefficient. We propose a rapid 3D protein structure retrieval system named 'ProtDex2', in which we adopt the techniques used in information retrieval systems in order to perform rapid database searching without having access to every 3D structure in the database. The retrieval process is based on the inverted-file index constructed on the feature vectors of the relationships between the secondary structure elements (SSEs) of all the 3D protein structures in the database. ProtDex2 is a significant improvement, both in terms of speed and accuracy, upon its predecessor system, ProtDex. RESULTS: The experimental results show that ProtDex2 is very much faster than two well-known protein structure comparison methods, DALI and CE, yet not sacrificing on the accuracy of the comparison. When comparing with a similar SSE-based method, namely TopScan, ProtDex2 is much faster with comparable degree of accuracy. AVAILABILITY: The software is available at: http://xena1.ddns.comp.nus.edu.sg/~genesis/PD2.htm

Algorithms↗

PubMed2003

BLAST++: BLASTing queries in batches.

UNLABELLED: BLAST++ is a tool that is integrated with NCBI BLAST, allowing multiple, say K, queries to be searched against a database concurrently. The results obtained by BLAST++ are identical to that obtained by executing BLAST on each of the K queries, but BLAST++ completes the processing in a much shorter time. AVAILABILITY: http://xena1.ddns.comp.nus.edu.sg/~genesis/blast++ SUPPLEMENTARY INFORMATION: http://xena1.ddns.comp.nus.edu.sg/~genesis/blast++

Algorithms↗