ORCID Profile
0000-0002-8749-0473
Current Organisation
Singapore Management University
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Publisher: Springer Science and Business Media LLC
Date: 08-02-2021
DOI: 10.1186/S41039-021-00149-9
Abstract: Do my students understand? The question that lingers in every instructor’s mind after each lesson. With the focus on learner-centered pedagogy, is it feasible to provide timely and relevant guidance to in idual learners according to their levels of understanding? One of the options available is to collect reflections from learners after each lesson to extract relevant feedback so that doubts or questions can be addressed in a timely manner. In this paper, we derived a hybrid approach that leverages a novel Doubt Sentic Pattern Detection (SPD) algorithm and a machine learning model to automate the identification of doubts from students’ informal reflections. The encouraging results clearly show that the hybrid approach has the potential to be adopted in the real-world doubt detection. Using reflections as a feedback mechanism and automated doubt detection can pave the way to a promising approach for learner-centered teaching and personalized learning.
Publisher: Elsevier BV
Date: 08-2016
Publisher: Springer International Publishing
Date: 2015
Publisher: Wiley
Date: 03-2005
Abstract: Knowledge of protein-protein interaction is useful for elucidating protein function via the concept of 'guilt-by-association'. A statistical learning method, Support Vector Machine (SVM), has recently been explored for the prediction of protein-protein interactions using artificial shuffled sequences as hypothetical noninteracting proteins and it has shown promising results (Bock, J. R., Gough, D. A., Bioinformatics 2001, 17, 455-460). It remains unclear however, how the prediction accuracy is affected if real protein sequences are used to represent noninteracting proteins. In this work, this effect is assessed by comparison of the results derived from the use of real protein sequences with that derived from the use of shuffled sequences. The real protein sequences of hypothetical noninteracting proteins are generated from an exclusion analysis in combination with subcellular localization information of interacting proteins found in the Database of Interacting Proteins. Prediction accuracy using real protein sequences is 76.9% compared to 94.1% using artificial shuffled sequences. The discrepancy likely arises from the expected higher level of difficulty for separating two sets of real protein sequences than that for separating a set of real protein sequences from a set of artificial sequences. The use of real protein sequences for training a SVM classification system is expected to give better prediction results in practical cases. This is tested by using both SVM systems for predicting putative protein partners of a set of thioredoxin related proteins. The prediction results are consistent with observations, suggesting that real sequence is more practically useful in development of SVM classification system for facilitating protein-protein interaction prediction.
Publisher: Elsevier BV
Date: 05-2002
DOI: 10.1016/S1570-0232(02)00041-7
Abstract: Hepatocellular carcinoma (HCC or hepatoma) is the most common primary cancer of the liver. Persistent viral infection by the hepatic B or C virus is probably the most important cause of HCC worldwide. It is responsible for approximately one million deaths each year, predominantly in the underdeveloped and developing countries, but its incidence is also on the rise in the developed countries. For most patients suffering from HCC, long-term survival is rare, as they are presented late and are often unsuitable for curative treatment. Thus there is great interest to identify novel HCC diagnostic markers for early detection of the disease, and tumour specific associated proteins as potential therapeutic targets in the treatment of HCC. Proteome analyses of HCC cell lines and liver tumour tissues should facilitate the screening and discovery of these HCC proteins. The creation of a comprehensive HCC proteome database would be an important first step towards achieving this goal. This review presents an update of the two-dimensional electrophoresis proteome database of the cell line, HCC-M, which is also now freely accessible through the World Wide Web at proteome.btc.nus.edu.sg/hccm/.
Publisher: Springer International Publishing
Date: 2015
Publisher: IEEE
Date: 13-10-2021
Publisher: Hawaii International Conference on System Sciences
Date: 2022
Publisher: Springer Science and Business Media LLC
Date: 20-08-2016
Publisher: Springer International Publishing
Date: 2022
Publisher: Wiley
Date: 03-2006
Abstract: In the field of proteomics, the increasing difficulty to unify the data format, due to the different platforms/instrumentation and laboratory documentation systems, greatly hinders experimental data verification, exchange, and comparison. Therefore, it is essential to establish standard formats for every necessary aspect of proteomics data. One of the recently published data models is the proteomics experiment data repository [Taylor, C. F., Paton, N. W., Garwood, K. L., Kirby, P. D. et al., Nat. Biotechnol. 2003, 21, 247-254]. Compliant with this format, we developed the systematic proteomics laboratory analysis and storage hub (SPLASH) database system as an informatics infrastructure to support proteomics studies. It consists of three modules and provides proteomics researchers a common platform to store, manage, search, analyze, and exchange their data. (i) Data maintenance includes experimental data entry and update, uploading of experimental results in batch mode, and data exchange in the original PEDRo format. (ii) The data search module provides several means to search the database, to view either the protein information or the differential expression display by clicking on a gel image. (iii) The data mining module contains tools that perform biochemical pathway, statistics-associated gene ontology, and other comparative analyses for all the s le sets to interpret its biological meaning. These features make SPLASH a practical and powerful tool for the proteomics community.
Publisher: Springer International Publishing
Date: 2015
Publisher: Cold Spring Harbor Laboratory
Date: 17-02-2004
DOI: 10.1261/RNA.5890304
Abstract: Elucidation of the interaction of proteins with different molecules is of significance in the understanding of cellular processes. Computational methods have been developed for the prediction of protein–protein interactions. But insufficient attention has been paid to the prediction of protein–RNA interactions, which play central roles in regulating gene expression and certain RNA-mediated enzymatic processes. This work explored the use of a machine learning method, support vector machines (SVM), for the prediction of RNA-binding proteins directly from their primary sequence. Based on the knowledge of known RNA-binding and non-RNA-binding proteins, an SVM system was trained to recognize RNA-binding proteins. A total of 4011 RNA-binding and 9781 non-RNA-binding proteins was used to train and test the SVM classification system, and an independent set of 447 RNA-binding and 4881 non-RNA-binding proteins was used to evaluate the classification accuracy. Testing results using this independent evaluation set show a prediction accuracy of 94.1%, 79.3%, and 94.1% for rRNA-, mRNA-, and tRNA-binding proteins, and 98.7%, 96.5%, and 99.9% for non-rRNA-, non-mRNA-, and non-tRNA-binding proteins, respectively. The SVM classification system was further tested on a small class of snRNA-binding proteins with only 60 available sequences. The prediction accuracy is 40.0% and 99.9% for snRNA-binding and non-snRNA-binding proteins, indicating a need for a sufficient number of proteins to train SVM. The SVM classification systems trained in this work were added to our Web-based protein functional classification software SVMProt, at jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi . Our study suggests the potential of SVM as a useful tool for facilitating the prediction of protein–RNA interactions.
Publisher: Public Library of Science (PLoS)
Date: 13-04-2015
Publisher: Elsevier BV
Date: 09-2017
Publisher: Elsevier BV
Date: 05-2016
No related grants have been discovered for Siaw Ling Lo.