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Limsoon Wong

Publications and source records attributed to Limsoon Wong.

12 recordsLinked to original sources
PubMed2025

GCfix: a fast and accurate fragment length-specific method for correcting GC bias in cell-free DNA.

MOTIVATION: Cell-free DNA (cfDNA) analysis has wide-ranging clinical applications due to its noninvasive nature. However, cfDNA fragmentomics and copy number analysis can be complicated by GC bias. There is a lack of GC correction software based on rigorous cfDNA GC bias analysis. Furthermore, there is no standardized metric for comparing GC bias correction methods across large sample sets, nor a rigorous experiment setup to demonstrate their effectiveness on cfDNA data at various coverage levels. RESULTS: We present GCfix, a method for robust GC bias correction in cfDNA data across diverse coverages. Developed following an in-depth analysis of cfDNA GC bias at the region and fragment length levels, GCfix is both fast and accurate. It works on all reference genomes and generates correction factors, tagged BAM files, and corrected coverage tracks. We also introduce two orthogonal performance metrics for (i) comparing the fragment count density distribution of GC content between expected and corrected samples, and (ii) evaluating coverage profile improvement post-correction. GCfix outperforms existing cfDNA GC bias correction methods on these metrics. AVAILABILITY AND IMPLEMENTATION: GCfix software and code for reproducing the figures are publicly accessible on GitHub: https://github.com/Rafeed-bot/GCfix_Software.

Software↗
PubMed2003

Simple rules underlying gene expression profiles of more than six subtypes of acute lymphoblastic leukemia (ALL) patients.

MOTIVATIONS AND RESULTS: For classifying gene expression profiles or other types of medical data, simple rules are preferable to non-linear distance or kernel functions. This is because rules may help us understand more about the application in addition to performing an accurate classification. In this paper, we discover novel rules that describe the gene expression profiles of more than six subtypes of acute lymphoblastic leukemia (ALL) patients. We also introduce a new classifier, named PCL, to make effective use of the rules. PCL is accurate and can handle multiple parallel classifications. We evaluate this method by classifying 327 heterogeneous ALL samples. Our test error rate is competitive to that of support vector machines, and it is 71% better than C4.5, 50% better than Naive Bayes, and 43% better than k-nearest neighbour. Experimental results on another independent data sets are also presented to show the strength of our method. AVAILABILITY: Under http://sdmc.lit.org.sg/GEDatasets/, click on Supplementary Information.

Algorithms↗
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↗
PubMed2002

FIMM, a database of functional molecular immunology: update 2002.

FIMM database (http://sdmc.krdl.org.sg:8080/fimm) contains data relevant to functional molecular immunology, focusing on cellular immunology. It contains fully referenced data on protein antigens, major histocompatibility complex (MHC) molecules, MHC-associated peptides and relevant disease associations. FIMM has a set of search tools for extraction of information and results are presented as lists or as reports.

Antigens↗
PubMed2002

Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling.

Treatment of pediatric acute lymphoblastic leukemia (ALL) is based on the concept of tailoring the intensity of therapy to a patient's risk of relapse. To determine whether gene expression profiling could enhance risk assignment, we used oligonucleotide microarrays to analyze the pattern of genes expressed in leukemic blasts from 360 pediatric ALL patients. Distinct expression profiles identified each of the prognostically important leukemia subtypes, including T-ALL, E2A-PBX1, BCR-ABL, TEL-AML1, MLL rearrangement, and hyperdiploid >50 chromosomes. In addition, another ALL subgroup was identified based on its unique expression profile. Examination of the genes comprising the expression signatures provided important insights into the biology of these leukemia subgroups. Further, within some genetic subgroups, expression profiles identified those patients that would eventually fail therapy. Thus, the single platform of expression profiling should enhance the accurate risk stratification of pediatric ALL patients.

Algorithms↗
PubMed2002

Optimal gene expression analysis by microarrays.

DNA microarrays make possible the rapid and comprehensive assessment of the transcriptional activity of a cell, and as such have proven valuable in assessing the molecular contributors to biological processes and in the classification of human cancers. The major challenge in using this technology is the analysis of its massive data output, which requires computational means for interpretation and a heightened need for quality data. The optimal analysis requires an accounting and control of the many sources of variance within the system, an understanding of the limitations of the statistical approaches, and the ability to make sense of the results through intelligent database interrogation.

Algorithms↗
PubMed2002

Technologies for integrating biological data.

The process of building a new database relevant to some field of study in biomedicine involves transforming, integrating and cleansing multiple data sources, as well as adding new material and annotations. This paper reviews some of the requirements of a general solution to this data integration problem. Several representative technologies and approaches to data integration in biomedicine are surveyed. Then some interesting features that separate the more general data integration technologies from the more specialised ones are highlighted.

Database Management Systems↗
PubMed2002

Accomplishments and challenges in literature data mining for biology.

We review recent results in literature data mining for biology and discuss the need and the steps for a challenge evaluation for this field. Literature data mining has progressed from simple recognition of terms to extraction of interaction relationships from complex sentences, and has broadened from recognition of protein interactions to a range of problems such as improving homology search, identifying cellular location, and so on. To encourage participation and accelerate progress in this expanding field, we propose creating challenge evaluations, and we describe two specific applications in this context.

Benchmarking↗
PubMed2002

Identifying good diagnostic gene groups from gene expression profiles using the concept of emerging patterns.

MOTIVATIONS AND RESULTS: Gene groups that are significantly related to a disease can be detected by conducting a series of gene expression experiments. This work is aimed at discovering special types of gene groups that satisfy the following property. In each group, its member genes are found to be one-to-one contained in pre-determined intervals of gene expression level with a large frequency in one class of cells but are never found unanimously in these intervals in the other class of cells. We call these gene groups emerging patterns, to emphasize the patterns' frequency changes between two classes of cells. We use effective discretization and gene selection methods to obtain the most discriminatory genes. We also use efficient algorithms to derive the patterns from these genes. According to our studies on the ALL/AML dataset and the colon tumor dataset, some patterns, which consist of one or more genes, can reach a high frequency of 90%, or even 100%. In other words, they nearly or fully dominate one class of cells, even though they rarely occur in the other class. The discovered patterns are used to classify new cells with a higher accuracy than other reported methods. Based on these patterns, we also conjecture the possibility of a personalized treatment plan which converts colon tumor cells into normal cells by modulating the expression levels of a few genes.

Algorithms↗
PubMed2002

A comparative study on feature selection and classification methods using gene expression profiles and proteomic patterns.

Feature selection plays an important role in classification. We present a comparative study on six feature selection heuristics by applying them to two sets of data. The first set of data are gene expression profiles from Acute Lymphoblastic Leukemia (ALL) patients. The second set of data are proteomic patterns from ovarian cancer patients. Based on features chosen by these methods, error rates of several classification algorithms were obtained for analysis. Our results demonstrate the importance of feature selection in accurately classifying new samples.

Algorithms↗
PubMed2002

Using feature generation and feature selection for accurate prediction of translation initiation sites.

Correct prediction of the translation initiation site (TIS) is an important issue in genomic research. We show that feature generation together with correlation based feature selection can be used with a variety of machine learning algorithms to give highly accurate translation initiation site prediction. Only very few features are needed and the results achieve comparable accuracy to the best existing approaches. Our approach has the advantage that it does not require one to devise a special prediction method; rather standard machine learning classifiers are shown to give very good performance on the selected features. The raw and generated features which we have found to be important are the following: positions -3 and -1 in the sequence; upstream k-grams for k=3, 4, and 5; stop-codon frequency; downstream in-frame 3-gram; and the distance of ATG to the beginning of the sequence. The best result, with an overall accuracy of 90%, is obtained by selecting only seven features from this set. The same features retrained with the use of a scanning model achieves an overall accuracy of 94% on this dataset.

Codon, Initiator↗