称号及び氏名

博士（工学）Mohd Saberi Bin Mohamad

学位授与の日付

2010 年 3 月 31 日

論「Studies on Intelligent Approaches to Select Informative文

名Genes from Gene Expression Data for Cancer Classification」

論文審査委員

主査

大松繁

副査

市橋秀友

副査

松本啓之亮

論文要旨
Gene expression technology namely microarrays, offers the ability to measure theexpression levels of thousands of genes simultaneously in biological organisms. Gene
expression data produced by the microarrays are expected to be of significant help inthe development of efficient cancer diagnoses and classification platforms. Many
researchers have analyzed gene expression data to select a small subset of informativegenes for cancer classification using various intelligence approaches. As a result, theselection of the small subset has improved classification accuracy. However, due to thesmall number of samples compared to the huge number of genes (high-dimension),
irrelevant genes, and noisy genes, the most approaches face difficulties to select thesmall subset. Therefore, the ultimate goal of this Ph.D. research is to propose
intelligent approaches for selecting a small (near-optimal) subset of informative genes
from gene expression data for cancer classification. Support vector machine classifiers(SVMs) were used to measure classification accuracies on the gene subsets thatproduced by all the proposed approaches. The first six proposed approaches wereproduced based on genetic algorithms (GAs), whereas the remaining approaches wereextensions of particle swarm optimization (PSO).
First, a multi-objective strategy in GAs was proposed to improve the performanceof GAs that uses a single-objective approach. It is described in Chapter 2. The strategy
has been developed based on classification accuracy maximization and gene subset size
minimization. In this strategy, multi-objective problems have been accommodated byusing specialized fitness functions in GAs. The ultimate goal of the strategy is tosearch and select a nondominated gene subset Pareto front. It was tried on fourbenchmark gene expression data sets and obtained encouraging results on those datasets as compared with an approach that used a single-objective strategy in GAs.
Second, an approach using two hybrid methods was then introduced. It is
discussed in Chapter 3. This approach includes a hybrid of GAs and SVM classifiers(GASVM) and an improved GASVM (GASVM-II). It was developed to overcome thelimitations of GASVM and GASVM-II that developed separately. In the first phase,
GASVM-II is applied to manually select genes from overall gene expression data toproduce a subset of genes. It is used to reduce the dimensionality of the data, andtherefore the complexity of the search or solution spaces can also be decreased. In thesecond phase, GASVM is used to select and optimize a small subset of informativegenes from the subset that is produced by the first phase. The approach was assessedand evaluated on four well-known gene expression data sets, showing competitive
results.
Third, a cyclic hybrid method based on GASVM-II has been proposed. Chapter 4
describes the detail of the cyclic hybrid method. It differs from other GASVM-based
methods in one major part, namely it involves a cyclic approach, whereas theGASVM-based methods did not use any cyclic approach. Basically, the cyclic hybrid
method repeats the process of GASVM-II to iteratively reduce the dimensionality ofdata and produce potential gene subsets. Five real gene expression data sets were used
to test the effectiveness of the method. Experimental results show that theperformance of the proposed method is superior to other experimental methods andprevious related works in terms of classification accuracy and the number of selectedgenes. In addition, a scatter gene graph and the list of informative genes in the best
gene subsets are also presented for biological usage.
Fourth, an iterative approach based on GASVM is then developed. Chapter 5
concerns on the discussion of the iterative approach. Generally, it is completely samewith the proposed cyclic hybrid, but it uses GASVM to replace GASVM-II in theprocess to yield potential gene subsets and reduce the dimensionality of data
iteratively. To demonstrate its effectiveness, four gene expression data sets are used.
Experimental results show that the approach is efficient in finding genes forclassifying cancer classes.
Fifth, a two-stage method was proposed to surmount the drawbacks of

GASVM-based methods in previous related works. It is discussed in Chapter 6. In thefirst stage, a filter method either gain ratio (GR) or information gain (IG) is applied on
overall gene expression data to pre-select genes and finally produces a subset of genes.
The dimensionality of data is also can be decreased. The second stage applies GASVM
to automatically optimize the gene subset that is produced by the first stage. As aresult, it yields a small (near-optimal) subset of informative genes. They wereevaluated on four publicly available gene expression data sets. The results show thatthe proposed method outperforms existing methods and other experimental methods.
Sixth, since a two-stage method does not perform well as expected, a three-stage
method that includes frequency analysis in the third stage was proposed. Chapter 7
describes this three-stage method. This frequency analysis is implemented to identify
the most frequently selected genes in near-optimal gene subsets. The most frequently
selected genes in the near-optimal gene subsets are presumed to be the most relevant
for the cancer classification. The three-stage method differs from methods in previous
works in one major part. The major difference is that it involves three stages (using afilter method, a hybrid method, and frequency analysis), whereas the previous works
usually had only one stage (using a filter method or a hybrid method) or two stages(using a filter method and a hybrid method). The proposed method has been tested andevaluated for gene selection on five gene expression data sets that contain binaryclasses and multi-classes of tumor samples. Based on the experimental results, theperformance of proposed method is better than other methods in previous relatedworks. The list of informative genes in the final gene subset is also presented forbiological usage
Seventh, a modification of binary PSO was proposed to overcome the limitationsof the conventional version of binary PSO and previous PSO-based methods. It is
introduced in Chapter 8. A scalar quantity that called particle’s speed and a novel rule
for updating particle’s positions are introduced in this modified binary PSO. Thisparticle’s speed and rule are proposed in order to reduce the probability of genes to be
selected for the cancer classification. By performing experiments on 12 different gene
expression data sets, including multi-class data sets, the modified binary PSO
outperforms other previous related works, including the conventional version of binary
PSO in terms of classification accuracy, the number of selected genes, and runningtimes.

Eighth, an enhancement of binary PSO with the constraint of particle’s velocities
was proposed. It is completely discussed in Chapter 9. The constraint is introduced in
the enhanced binary PSO to increase the probability of genes to be unselected for theclassification. Experimental results on twelve actual gene expression data sets showthat the performance of the proposed approach is superior to other previous related
works, as well as to conventional binary PSO tried in this work.
Ninth, a modified sigmoid function and the particle’s speed were introduced andimplemented in binary PSO. Chapter 10 describes this modified binary PSO. This
modified sigmoid function and particle’s speed decrease the probability of genes to be
selected for the cancer classification. The proposed method was experimentallyassessed on twelve well-known gene expression data sets. In this sense, comparisonswith the existing of binary PSO and several PSO-based methods show competitiveresults.
Finally, Chapter 11 gives the conclusion remarks and suggests interesting
directions for future researches. As a conclusion, twelve benchmark gene expression
data sets have been used to test the effectiveness of the proposed intelligent
approaches. Overall, experimental results show that the performances of the proposed
approaches are superior to previous related works as well as methods experimented inthis work in terms of classification accuracy, the number of selected genes, andrunning times.

審査結果の要旨

本論文は、遺伝子発現量データから有益な情報を持った遺伝子の小規模集団を選択するために、遺伝的アルゴリズム(GA)と粒子群最適化(PSO)に基づいた知的手法について研究したものであり、以下の成果を得ている。
（１）癌分類を行うために、遺伝子発現量データから有益な情報を持った遺伝子の小規模集団を選択するために遺伝的アルゴリズムに基づく知的手法を提案した。遺伝的アルゴリ
ズムとサポートベクターマシンにおける多目的戦略を提案し、その有効性を検証した。
（２）ハイブリッド手法の組み合わせ、巡回ハイブリッド手法、繰り返し手法、二段階法、三段階法を提案し、次元削減、無関係遺伝子のフィルタ除去、ノイズを含む遺伝子の除去を行い、癌分類のための最適に近い遺伝子集団を抽出した。5 種類の遺伝子表現データによる実験により、提案手法の有用性を分類精度と選択された遺伝子数の観点から定量的に検証した。
（３）生物の集団行動規則にヒントを得た二値 PSO を拡張した、改変 PSO、拡張改良 PSOを提案した。ここでは、粒子速度、粒子速度更新の新方式、粒子速度制約、改変シグモイ
ド関数を新たに導入し、12 個のベンチマーク遺伝子表現データセットで評価し、二値 PSOを含む関連する従来研究に比して分類精度と遺伝子選択数の観点から優れた結果を得た。
とくに、提案手法は二値 PSO に比べて所要時間の点で優位性があり、実際の遺伝子抽出への適用を可能とした。
以上の成果は、遺伝子発現量データから有益な情報を持った遺伝子の小規模集団を選択するための高精度かつ高速な手法を実現していることから、広範囲な分野の学術的、産業的な発展に貢献するところが大である。また、申請者が自立して研究活動を行うのに必要な能力と学識を有することを証したものである。