• Journal of Information Processing Systems
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• 제19권5호
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• pp.652-662
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• 2023

Identifying highly discriminating genes is a critical step in tumor recognition tasks based on microarray gene expression profile data and machine learning. Gene selection based on tree models has been the subject of several studies. However, these methods are based on a single-tree model, often not robust to ultra-highdimensional microarray datasets, resulting in the loss of useful information and unsatisfactory classification accuracy. Motivated by the limitations of single-tree-based gene selection, in this study, ensemble gene selection methods based on multiple-tree models were studied to improve the classification performance of tumor identification. Specifically, we selected the three most representative tree models: ID3, random forest, and gradient boosting decision tree. Each tree model selects top-n genes from the microarray dataset based on its intrinsic mechanism. Subsequently, three ensemble gene selection methods were investigated, namely multipletree model intersection, multiple-tree module union, and multiple-tree module cross-union, were investigated. Experimental results on five benchmark public microarray gene expression datasets proved that the multiple tree module union is significantly superior to gene selection based on a single tree model and other competitive gene selection methods in classification accuracy.

• Genomics & Informatics
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• 제2권1호
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• pp.19-29
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• 2004

Gene expression profiles may offer more information than morphology and provide an alternative to morphology- based tumor classification systems. Informative gene selection is finding gene subsets that are able to discriminate between tumor types, and may have clear biological interpretation. Gene selection is a fundamental issue in gene expression based tumor classification. In this report, techniques for selecting informative genes are illustrated and supervised shaving introduced as a gene selection method in the place of a clustering algorithm. The supervised shaving method showed good performance in gene selection and classification, even though it is a clustering algorithm. Almost selected genes are related to leukemia disease. The expression profiles of 3051 genes were analyzed in 27 acute lymphoblastic leukemia and 11 myeloid leukemia samples. Through these examples, the supervised shaving method has been shown to produce biologically significant genes of more than $94\%$ accuracy of classification. In this report, SVM has also been shown to be a practicable method for gene expression-based classification.

• 응용통계연구
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• 제24권6호
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• pp.1103-1113
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• 2011

Gene expression data is obtained through many stages of an experiment and errors produced during the process may cause missing values. Due to the distinctness of the data so called 'small n large p', genes have to be selected for statistical analysis, like classification analysis. For this reason, imputation and gene selection are important in a microarray data analysis. In the literature, imputation, gene selection and classification analysis have been studied respectively. However, imputation, gene selection and classification analysis are sequential processing. For this aspect, we compare the performance of classification methods after imputation and gene selection methods are applied to microarray data. Numerical simulations are carried out to evaluate the classification methods that use various combinations of the imputation and gene selection methods.

• 응용통계연구
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• 제35권5호
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• pp.667-684
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• 2022

유전자 발현 데이터는 각 유전자에 대해 mRNA 양의 정도를 나타내고, 그러한 유전자 발현량에 대한 분석은 질병 발생에 대한 메커니즘을 이해하고 새로운 치료제와 치료 방법을 개발하는데 중요한 아이디어를 제공해오고 있다. 오늘날 DNA 마이크로어레이와 RNA-시퀀싱과 같은 고출력 기술은 수천 개의 유전자 발현량을 동시에 측정하는 것을 가능하게 하여 고차원성이라는 유전자 발현 데이터의 특징을 발생시켰다. 이러한 고차원성으로 인해 유전자 발현 데이터를 분석하기 위한 학습 모형들은 과적합 문제에 부딪히기 쉽고, 이를 해결하기 위해 차원 축소 또는 변수 선택 기술들이 사전 분석 단계로써 보통 사용된다. 특히, 사전 분석 단계에서 우리는 유전자 선택법을 이용하여 부적절하거나 중복된 유전자를 제거할 수 있고 중요한 유전자를 찾아낼 수도 있다. 현재까지 다양한 유전자 선택 방법들이 기계학습의 맥락에서 개발되어왔다. 본 논문에서는 기계학습 접근법을 사용하는 최근의 유전자 선택 방법들을 집중적으로 살펴보고자 한다. 또한, 현재까지 개발된 유전자 선택 방법들의 근본적인 문제점과 앞으로의 연구 방향에 대해 논의하고자 한다.

• Industrial Engineering and Management Systems
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• 제16권1호
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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.

• Journal of Crop Science and Biotechnology
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• 제11권4호
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• pp.263-268
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• 2008

Bacterial leaf pustule (BLP) caused by Xanthomonas axonopodis pv. glycines is a serious disease to make pustule and chlorotic haloes in soybean [Glycine max (L). Merr.]. While inheritance mode and map positions of the BLP resistance gene, rxp are known, no sequence information of the gene was reported. In this study, we made five near isogenic lines (NILs) from separate backcrosses (BCs) of BLP-susceptible Hwangkeumkong $\times$ BLP-resistant SS2-2 (HS) and BLP-susceptible Taekwangkong$\times$ SS2-2 (TS) through foreground and background selection based on the four-stage selection strategy. First, 15 BC individuals were selected through foreground selection using the simple sequence repeat (SSR) markers Satt486 and Satt372 flanking the rxp gene. Among them, 11 BC plants showed the BLP-resistant response. The HS and TS lines chosen in foreground selection were again screened by background selection using 118 and 90 SSR markers across all chromosomes, respectively. Eventually, five individuals showing greater than 90% recurrent parent genome content were selected in both HS and TS lines. These NILs will be a unique biological material to characterize the rxp gene.

• 한국정보과학회논문지:시스템및이론
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• 제35권8호
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• pp.372-377
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• 2008

최근 활발히 연구가 진행 중인 유전발현 데이타를 이용한 다중클래스 암 분류는 DNA 마이크로어레이로부터 획득된 대규모의 유전자 정보를 분석하여 암의 종류를 판단한다. 수집된 유전발현 데이타에는 대상 암과 관련이 없는 유전자도 포함되어 있기 때문에 높은 성능의 분류 결과를 얻기 위해서 유용한 유전자를 선택하는 것이 필요하다. 기존의 순위기반 유전자 선택은 이진클래스를 대상으로 고안되었고 이상표식 유전자(Ideal marker gene)를 이용하기 때문에 다중클래스 암 분류에 직접 적용하기에는 한계가 있다. 본 논문에서는 이상표식 유전자를 사용하지 않고 유전발현 수준의 분포를 직접 분석하는 순위기반 다중클래스 유전자 선택 기법을 제안한다. 유전발현 수준을 이산화하고 학습 데이타로부터 빈도를 계산하여 클래스 간 분별력을 측정한 후, 선택된 유전자를 이용하여 나이브 베이즈 분류기를 사용해 다중 암 분류를 수행한다. 제안하는 방법을 다수의 다중클래스 암 분류 데이타에 적용하여 기존 유전자 선택 방법에 비해 우수함을 확인하였다.

• Asian-Australasian Journal of Animal Sciences
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• 제21권3호
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• pp.320-324
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• 2008

We investigated the effect of ESR gene-based selection on an improvement of litter size in the herds in real (non-experimental) conditions. The pigs were selected for three years. In the tested population the pigs were mated according to a breeding scheme where the individuals with at least one ESR-B allele were preferred in the selection. In the control group (CP; n = 140) the pigs were mated just according to a breeding scheme without knowledge of the ESR genotype. We observed a significant increase in litter size (total number of born, number of born alive and number of weaned piglets per litter) in the final tested ESR-selected population (LP; n = 184) and an insignificant increase in CP as compared with the original population (OP; n = 155). After the selection we could observe a significant increase in the frequency of allele B in LP. Frequency of the genotypes AB and BB increased in both LP and CP; the distribution of the genotypes changed significantly only in LP. An association analysis of the ESR gene effects on reproductive traits in LP showed no significant differences between the genotypes. The results of our study suggest that ESR gene-based selection can be successful also in small herds, under real (non-experimental) conditions with a respect for general breeding principles and limitations and during a short period. An examination of a larger sample population as well as an analysis of selection consequences on other traits (meat and carcass quality) could bring a more conclusive evaluation of ESR-based selection. Nevertheless, the results are encouraging especially for small breeding farms taking a perspective of better litter size improvement.

• Journal of the Korean Data and Information Science Society
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• 제25권5호
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• pp.1079-1094
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• 2014

상호정보 (mutual information)를 이용한 변수 선택법은 반응변수와 설명변수간의 선형적인 연관성뿐만 아니라 비선형적인 연관성을 감지하며, 설명변수 사이의 연관성도 고려하는 좋은 변수선택 방법이다. 하지만 고차원 자료에서 상호정보를 추정하기가 쉽지 않아 이에 대한 연구가 필요하다. Cai 등 (2009)은 조건부 상호정보를 이용한 전진선택법과 가지치기법을 이용하여 이러한 문제를 해결하였으며, 마이크로어레이 자료와 같은 고차원 자료에서 조건부 상호정보를 이용한 변수 선택법으로 선택된 변수들로 구성된 SVM의 분류 성능이 SVM-RFE 및 기존의 필터링 방법으로 선택된 변수들로 구성된 SVM의 분류 성능보다 뛰어남을 보였다. 하지만 조건부 상호정보를 추정할 때 사용된 Parzen window 방법은 변수의 수가 많아질수록 변수 선택 시간이 길어지는 단점으로 인해 이에 대한 보완이 필요하다. 본 논문에서는 조건부 상호정보 계산 시 필요한 설명변수의 분포를 다변량 정규분포로 가정함으로써 변수선택을 위한 계산시간을 단축시키며 동시에 변수선택의 성능을 향상시키고자 한다. 반면, 설명변수의 분포를 다변량 정규분포로 가정한다는 것은 강한 제약이 될 수 있으므로 이를 완화시킨 Edgeworth 근사를 이용한 조건부 상호정보 기반의 변수 선택법을 제안한다. 실증분석을 통해 본 논문에서 제안한 방법의 효율성을 살펴보았으며, 기존의 조건부 상호정보 기반 변수 선택법에 비해 계산 속도나 분류 성능 면에서 우수함을 보였다.

• Journal of Computing Science and Engineering
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• 제11권4호
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• pp.121-129
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• 2017

Feature selection has been widely established as an efficient technique for microarray data analysis. Feature selection aims to search for the most important feature/gene subset of a given dataset according to its relevance to the current target. Unsupervised feature selection is considered to be challenging due to the lack of label information. In this paper, we propose a novel method for unsupervised feature selection, which incorporates embedded learning and $l_{2,1}-norm$ sparse regression into a framework to select genes in microarray data analysis. Local tangent space alignment is applied during embedded learning to preserve the local data structure. The $l_{2,1}-norm$ sparse regression acts as a constraint to aid in learning the gene weights correlatively, by which the proposed method optimizes for selecting the informative genes which better capture the interesting natural classes of samples. We provide an effective algorithm to solve the optimization problem in our method. Finally, to validate the efficacy of the proposed method, we evaluate the proposed method on real microarray gene expression datasets. The experimental results demonstrate that the proposed method obtains quite promising performance.