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| 1 | A comparative study on feature selection and classification methods using gene expression profiles and proteomic patterns显示文摘 | Liu Huiqing Li Jinyan Wong Limsoon | 2002 | Genome Informatics2002，13，： | 1 |
| 2 | Optimal gene expression analysis by microarrays显示文摘 | Lance D. Miller Philip M. Long Limsoon Wong Sayan Mukherjee Lisa M. McShane Edison T. Liu | 2002 | Cancer Cell2002，，5： | 1 |
| 3 | Exploiting indirect neighbours and topological weight to predict pro- tein function from protein-protein interactions 显示文摘 | Chua Honnian Sung Wingkin Wong Limsoon | 2006 | Bioinformatics2006，22，13： | 1 |
| 4 | Complex dis- covery from weighted PPI networks 显示文摘 | Liu Guimei Wong Limsoon Chua Honnian | 2009 | Bioinformatics2009，25，15： | 1 |
| 5 | A comparative study on feature selection and classification methods using gene expression profiles and proteomic patterns 显示文摘 | Liu Huiqing Li Jinyan Wong Limsoon | 2002 | Genome Information2002，13，： | 1 |
| 6 | Identifying good diagnostic gene groups from gene expression profiles using the concept of emerging patterns显示文摘 | LI Jinyan Wong Limsoon | 2002 | Bioinformatics2002，18，5： | 1 |
| 7 | A comparative study on feature selection and classification methods using gene expression profiles and proteomic patterns显示文摘 | Huiqing L Jinyan L Limsoon W | 2002 | Genome Informatics2002，13，60： | 1 |
| 8 | Protein Interactome Analysis for Countering Pathogen Drug Resistance显示文摘Drug-resistant varieties of pathogens are now a recognized global threat. Insights into the routes for drug resistance in these pathogens are critical for developing more effective antibacterial drugs. A systems-level analysis of the genes, proteins, and interactions involved is an important step to gaining such insights. This paper discusses some of the computational challenges that must be surmounted to enable such an analysis; viz., unreliability of bacterial interactome maps, paucity of bacterial interactome maps, and identification of pathways to bacterial drug resistance. | Limsoon Wong Guimei Liu | 2010 | Journal of Computer Science & Technology2010，25，1： | 0 |
| 9 | OrthoGNC:A Software for Accurate Identification of Orthologs Based on Gene Neighborhood Conservation显示文摘Orthology relations can be used to transfer annotations from one gene(or protein) to another. Hence, detecting orthology relations has become an important task in the post-genomic era. Various genomic events, such as duplication and horizontal gene transfer, can cause erroneous assignment of orthology relations. In closely-related species, gene neighborhood information can be used to resolve many ambiguities in orthology inference. Here we present Ortho GNC, a software for accurately predicting pairwise orthology relations based on gene neighborhood conservation.Analyses on simulated and real data reveal the high accuracy of Ortho GNC. In addition to orthology detection, Ortho GNC can be employed to investigate the conservation of genomic context among potential orthologs detected by other methods. Ortho GNC is freely available online at http://gffzz63d508e13d21417eh0kupvbfwb0vk6bx5.ffgz.tsg.suse.edu.cn/softwares/orthognc and http://gffzz2665cf07b25c4056h0kupvbfwb0vk6bx5.ffgz.tsg.suse.edu.cn/ortho GNC. | Soheil Jahangiri-Tazehkand Limsoon Wong Changiz Eslahchi | 2017 | Genomics, Proteomics & Bioinformatics2017，15，6： | 0 |
| 10 | TICA: Transcriptional Interaction and Coregulation Analyzer显示文摘Transcriptional regulation is critical to cellular processes of all organisms. Regulatory mechanisms often involve more than one transcription factor(TF) from different families, binding together and attaching to the DNA as a single complex. However, only a fraction of the regulatory partners of each TF is currently known. In this paper, we present the Transcriptional Interaction and Coregulation Analyzer(TICA), a novel methodology for predicting heterotypic physical interaction of TFs. TICA employs a data-driven approach to infer interaction phenomena from chromatin immunoprecipitation and sequencing(ChIP-seq) data. Its prediction rules are based on the distribution of minimal distance couples of paired binding sites belonging to different TFs which are located closest to each other in promoter regions. Notably, TICA uses only binding site information from input ChIP-seq experiments, bypassing the need to do motif calling on sequencing data. We present our method and test it on ENCODE ChIP-seq datasets, using three cell lines as reference including HepG2, GM12878, and K562. TICA positive predictions on ENCODE ChIP-seq data are strongly enriched when compared to protein complex(CORUM) and functional interaction(BioGRID) databases. We also compare TICA against both motif/ChIP-seq based methods for physical TF–TF interaction prediction and published literature. Based on our results, TICA offers significant specificity(average 0.902) while maintaining a good recall(average 0.284) with respect to CORUM, providing a novel technique for fast analysis of regulatory effect in cell lines. Furthermore, predictions by TICA are complementary to other methods for TF–TF interaction prediction(in particular, TACO and CENTDIST). Thus, combined application of these prediction tools results in much improved sensitivity in detecting TF–TF interactions compared to TICA alone(sensitivity of 0.526 when combining TICA with TACO and 0.585 when combining with CENTDIST)with little compromise in specificity(specificity 0.760 when combining with TACO and 0.643 with CENTDIST). TICA is publicly available at http://gffzz2c35c4d9332940ach0kupvbfwb0vk6bx5.ffgz.tsg.suse.edu.cn/tica/. | Stefano Perna Pietro Pinoli Stefano Ceri Limsoon Wong | 2018 | Genomics, Proteomics & Bioinformatics2018，16，5： | 0 |
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