• Industrial Engineering and Management Systems
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• v.16 no.1
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• pp.64-79
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• 2017

Identifying disease genes from human genome is a critical task in biomedical research. Important biological features to distinguish the disease genes from the non-disease genes have been mainly selected based on traditional feature selection approaches. However, the traditional feature selection approaches unnecessarily consider many unimportant biological features. As a result, although some of the existing classification techniques have been applied to disease gene identification, the prediction performance was not satisfactory. A small set of the most important biological features can enhance the accuracy of disease gene identification, as well as provide potentially useful knowledge for biologists or clinicians, who can further investigate the selected biological features as well as the potential disease genes. In this paper, we propose a new stepwise random forests (SRF) approach for biological feature selection and disease gene identification. The SRF approach consists of two stages. In the first stage, only important biological features are iteratively selected in a forward selection manner based on one-dimensional random forest regression, where the updated residual vector is considered as the current response vector. We can then determine a small set of important biological features. In the second stage, random forests classification with regard to the selected biological features is applied to identify disease genes. Our extensive experiments show that the proposed SRF approach outperforms the existing feature selection and classification techniques in terms of biological feature selection and disease gene identification.

• Genomics & Informatics
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• v.11 no.4
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• pp.191-199
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• 2013

High-throughput next-generation sequencing (NGS) technology produces a tremendous amount of raw sequence data. The challenges for researchers are to process the raw data, to map the sequences to genome, to discover variants that are different from the reference genome, and to prioritize/rank the variants for the question of interest. The recent development of many computational algorithms and programs has vastly improved the ability to translate sequence data into valuable information for disease gene identification. However, the NGS data analysis is complex and could be overwhelming for researchers who are not familiar with the process. Here, we outline the analysis pipeline and describe some of the most commonly used principles and tools for analyzing NGS data for disease gene identification.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.2 no.2
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• pp.164-168
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• 1999

Background: Wilson disease (WD) is an autosomal recessive disorder of copper transport and characterized by degenerative changes in the brain, liver dysfunction, and Kayser-Fleischer rings due to toxic accumulation of copper. Since the identification of Wilson disease gene (ATP7B), more than 80 mutations have been detected among the different ethnic groups. Methods: Twenty three children with Wilson disease were included in this study. They were all diagnosed by low serum ceruloplasmin and increased 24 hour urinary copper excretion with characteristic clinical findings. We analysed WD gene mutation by assessing the nucleotide sequence of exon 7, 8, 9 and 10 including intron-exon boundaries of ATP7B gene from genomic DNA. Results: Arg778Leu mutation was identified in 16 WD patients; three were homozygous and 13 were heterozygous for this mutation. Of the 46 alleles, 19 alleles had a Arg778Leu mutation (19/46=41%). Homozygote patients had neurologic forms of WD. Arg778Leu mutation was not found among 50 normal healthy persons. Conclusion: Arg778Leu mutation is a common mutation in Korean WD gene. Arg778Leu mutation screening might be used as a useful supplementary diagnostic test in some patients to confirm Wilson disease in Korea.

• Korean Journal of Biological Psychiatry
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• v.30 no.1
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• pp.24-30
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• 2023

Objectives Alzheimer's disease (AD) is the most common form of dementia in older adults, damaging the brain and resulting in impaired memory, thinking, and behavior. The identification of differentially expressed genes and related pathways among affected brain regions can provide more information on the mechanisms of AD. The aim of our study was to identify differentially expressed genes associated with AD and combined biomarkers among them to improve AD risk prediction accuracy. Methods Machine learning methods were used to compare the performance of the identified combined biomarkers. In this study, three publicly available gene expression datasets from the hippocampal brain region were used. Results We detected 31 significant common genes from two different microarray datasets using the limma package. Some of them belonged to 11 biological pathways. Combined biomarkers were identified in two microarray datasets and were evaluated in a different dataset. The performance of the predictive models using the combined biomarkers was superior to those of models using a single gene. When two genes were combined, the most predictive gene set in the evaluation dataset was ATR and PRKCB when linear discriminant analysis was applied. Conclusions Combined biomarkers showed good performance in predicting the risk of AD. The constructed predictive nomogram using combined biomarkers could easily be used by clinicians to identify high-risk individuals so that more efficient trials could be designed to reduce the incidence of AD.

• BMB Reports
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• v.37 no.1
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• pp.11-27
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• 2004

The introduction of molecular markers in genetic analysis has revolutionized medicine. These molecular markers are genetic variations associated with a predisposition to common diseases and individual variations in drug responses. Identification and genotyping a vast number of genetic polymorphisms in large populations are increasingly important for disease gene identification, pharmacogenetics and population-based studies. Among variations being analyzed, single nucleotide polymorphisms seem to be most useful in large-scale genetic analysis. This review discusses approaches for genetic analysis, use of different markers, and emerging technologies for large-scale genetic analysis where millions of genotyping need to be performed.

• Journal of Microbiology and Biotechnology
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• v.19 no.3
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• pp.323-330
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• 2009

Nontuberculous mycobacteria (NTM) are a major cause of opportunistic infections in immunocompromised patients, making the reliable and rapid identification of NTM to the species level very important for the treatment of such patients. Therefore, this study evaluated the usefulness of the novel target genes tuf and tmRNA for the identification of NTM to the species level, using a PCRrestriction fragment length polymorphism analysis (PRA). A total of 44 reference strains and 17 clinical isolates of the genus Mycobacterium were used. The 741 bp or 744 bp tuf genes were amplified, restricted with two restriction enzymes (HaeIII/MboI), and sequenced. The tuf gene-PRA patterns were compared with those for the tmRNA (AvaII), hsp65 (HaeIII/HphI), rpoB (MspI/HaeIII), and 16S rRNA (HaeIII) genes. For the reference strains, the tuf gene-PRA yielded 43 HaeIII patterns, of which 35 (81.4%) showed unique patterns on the species level, whereas the tmRNA, hsp65, rpoB, and 16S rRNA-PRAs only showed 10 (23.3%), 32 (74.4%), 19 (44.2%), and 3 (7%) unique patterns after single digestion, respectively. The tuf gene-PRA produced a clear distinction between closely related NTM species, such as M. abscessus (557-84-58) and M. chelonae (477-84-80-58), and M. kansasii (141-136-80-63-58-54-51) and M. gastri (141-136-117-80-58-51). No difference was observed between the tuf-PRA patterns for the reference strains and clinical isolates. Thus, a diagnostic algorithm using a tuf gene-targeting PRA is a promising tool with more advantages than the previously used hsp65, rpoB, and 16S rRNA genes for the identification of NTM to the species level.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.113-113
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• 2017

Downy mildew (DM), caused by several species in the Peronosclerospora and Scleropthora genera, is a major maize (Zea mays L.) disease in tropical or subtropical regions. DM is an obligate parasite species in the higher plants and spreads by oospores, wind, and mycelium in seed surface, soil, and living hosts. Owing to its geographical distribution and destructive yield reduction, DM is one of the most severe maize diseases among the maize pathogens. Positional cloning in combination with phenotyping is a general approach to identify disease resistant gene candidates in plants; however, it requires several time-consuming steps including population or fine mapping. Therefore, in the present study, we suggest a new combination strategy to improve the identification of disease resistant gene candidates. Downy mildew (DM) resistant maize was selected from five cultivars using the spreader row technique. Positional cloning and bioinformatics tools identified the DM resistant QTL marker (bnlg1702) and 47 protein coding genes annotations. Eventually, 5 DM resistant gene candidates, including bZIP34, Bak1, and Ppr, were identified by quantitative RT-PCR without fine mapping of the bnlg1702 locus. Specifically, we provided DM resistant gene candidates with our new strategy, including field selection by the spreader row technique without population preparation, the DM resistance region identification by positional cloning using bioinformatics tools, and expression level profiling by quantitative RT-PCR without fine mapping. As whole genome information is available for other crops, we propose applying our novel protocol to other crops or for other diseases with suitable adjustment.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.97-100
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• 2004

The identification of the DNA structure as a double-stranded helix consting of two nucleotide chain molecules was a milestone in modern molecular biology. The DNA chip technology is based on reverse hybridization that follows the principle of complementary binding of double-stranded DNA. DNA chip can be described as the deposition of defined nucleic acid sequences, probes, on a solid substrate to form a regular array of elements that are available for hybridization to complementary nucleic acids, targets. DNA chips based on cDNA clons, oligonucleotides and genomic clons have been developed for gene expression studies, genetic variation analysis and genomic changes associated with disease including cancers and genetic diseases. DNA chips for gene expression profiling can be used for functional analysis in human eel Is and animal models, disease-related gene studies, assessment of gene therapy, assessment of genetically modified food, and research for drug discovery. DNA chips for genetic variation detection can be used for the detection of mutations or chromosomal abnormalities in cnacers, drug resistances in cancer cells or pathogenic microbes, histocompatibility analysis for transplantation, individual identification for forensic medicine, and detection and discrimination of pathogenic microbes. The DNA chip will be generalized as a useful tool in clinical diagnostics in near future. Lab-on-a chip and informatics will facilitate the development of a variety of DNA chips for diagnostic purpose.

• Mycobiology
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• v.50 no.5
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• pp.382-388
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• 2022

White mold (or Sclerotinia stem rot), caused by Sclerotinia species, is a major air, soil, or seed-transmitted disease affecting numerous crops and wild plants. Microscopic or culture-based methods currently available for their detection and identification are time-consuming, laborious, and often erroneous. Therefore, we developed a multiplex quantitative PCR (qPCR) assay for the discrimination, detection, and quantification of DNA collected from each of the three economically relevant Sclerotinia species, namely, S. sclerotiorum, S. minor, and S. nivalis. TaqMan primer/probe combinations specific for each Sclerotinia species were designed based on the gene sequences encoding aspartyl protease. High specificity and sensitivity of each probe were confirmed for sclerotium and soil samples, as well as pure cultures, using simplex and multiplex qPCRs. This multiplex assay could be helpful in detecting and quantifying specific species of Sclerotinia, and therefore, may be valuable for disease diagnosis, forecasting, and management.

• Korean Journal of Clinical Laboratory Science
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• v.38 no.3
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• pp.158-165
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• 2006

Although Mycobacterium tuberculosis complex strains remain responsible for the majority of diseases caused by mycobacterial infections worldwide, the increase in HIV infections has allowed for the emergence of other non-tuberculous mycobacteria as clinically significant pathogens. However, Mycobacterium species has a long period of incubation, and requires serious biochemical tests such as niacin, catalase, and nitrate test that are often tedious. The development of rapid and accurate diagnostics can aid in the early diagnosis of disease caused by Mycobacterium. The current DNA amplification and hybridization methods that have been developed target several genes for the detection of mycobacterial species such as hps65, 16S rDNA, rpoB, and dnaj. These methods produce rapid and accurate results. In this study, PCR-restriction fragment length polymorphism analysis(PCR-RFLP) based on the region of the rpoB gene was used to verify the identification of non-tuburculosis Mycobacterium species. A total of 8 mycobacterial reference strains and 13 clinical isolates were digested with restriction enzymes such as Msp I in this study. The results of using this process clearly demonstrated that all 13 specimens were identified by rpoB gene PRA method. The PCR-RFLP method based on the rpoB gene is a simple, rapid, and accurate test for the identification of Mycobacterium.