Ru-Sheng Liu

Pattern classification in DNA microarray data of multiple tumor types

In this paper, we propose a genetic algorithm with silhouette statistics as discriminant function (GASS) for gene selection and pattern recognition. The proposed method evaluates gene expression patterns for discriminating heterogeneous cancers. Distance metrics and classification rules have also been analyzed to design a GASS with high classification accuracy. Moreover, the proposed method is compared to previously published methods. Various experimental results show that our method is effective for classifying the NCI60, the GCM and the SRBCTs datasets. Moreover, GASS outperforms other existing methods in both the leave-one-out cross validations and the independent test for novel data.

Spot detection for a 2-DE gel image using a slice tree with confidence evaluation

Spot detection is an essential step in 2-DE gel image analysis. The results of protein spot detection may substantially influence subsequent stages of analysis. This study presents a novel method for spot detection with the addition of confidence evaluation for each detected spot. The confidence of a spot provides useful hints for subsequent processing, such as landmark selection, spot quantification and gel image registration. The proposed method takes slices of a gel image in the gray level direction, and builds them into a slice tree, which in turn is adopted to perform spot detection and confidence evaluation. The spot detection software is implemented on Windows using the proposed slice tree. Building a slice tree for a gel image of resolution 1262x720 takes about 1.5 s on an Intel^(C)Pentium^(C)III 1.2 GHz machine with 512 MB of RAM. Spot detection takes about 43 ms after building the slice tree. The detected spots are shown by different colors based on their respective confidence values. Moreover, pointing a mouse over a detected spot shows detailed information about the spot, including the confidence value. Experimental results indicate that confidence values are close to a subjective judgment.

Classifying subtypes of acute lymphoblastic leukemia using silhouette statistics and genetic algorithms.

Correct classification and prediction of tumor cells is essential for a successful diagnosis and reliable future treatment. In this study, we aimed at using genetic algorithms for feature selection and proposed silhouette statistics as a discriminant function to distinguish between six subtypes of pediatric acute lymphoblastic leukemia by using microarray with thousands of gene expressions. Our methods have shown a better classification accuracy than previously published methods and obtained a set of genes effective to discriminate subtypes of pediatric acute lymphoblastic leukemia. Furthermore, the use of silhouette statistics, offering the advantages of measuring the classification quality by a graphical display and by an average silhouette width, has also demonstrated feasibility and novelty for more difficult multiclass tumor prediction problems.

Prediction of outer membrane proteins by support vector machines using combinations of gapped amino acid pair compositions

Discriminating outer membrane proteins from proteins with other subcellular localizations and with other folding classes are both important to predict farther their functions and structures. In this paper, we propose a method for discriminating outer membrane proteins from other proteins by support vector machines using combinations of gapped amino acid pair compositions. Using 5-fold cross-validation, the method achieves 95% precision and 92% recall on the dataset of proteins with well-annotated subcellular localizations, consisting of 471 outer membrane proteins and 1,120 other proteins. When applied on another dataset of 377 outer membrane proteins and 674 globular proteins belonging to four typical structural classes, the method reaches 96% precision and recall and correctly excludes 98% of the globular proteins. Our method outperforms the OM classifier of PSORTb v.2.0 and a method based on dipeptide composition.

Multiclass microarray data classification using GA/ANN method

This work aims to explore the use of gene expression data in discriminating heterogeneous cancers. We introduce hybrid learning methodology that integrates genetic algorithms (GA) and artificial neural networks (ANN) to find optimal subsets of genes for tissue/cancer classification. This method was tested on two published microarray datasets: (1) NCI60 cancer cell lines and (2) the GCM dataset. Experimental results on classifying both datasets show that our GA/ANN method not only outperformed many reported prediction approaches, but also reduced the number of predictive genes needed in classification analysis.

Genetic algorithms and silhouette measures applied to microarray data classification.

Microarray technology allows large-scale parallel measurements of the expression of many thousands genes and thus aiding in the development of efficient cancer diagnosis and classification platforms. In this paper, we apply the genetic algorithm and the silhouette statistic in conjunction with several distance functions to the problem of multi-class prediction. We examine two widely used sets of gene expression data, measured across sets of tumors, and present the results of classification accuracy on these two datasets by our methods. Our best success rate of tumor classification has better accuracy than many previously reported methods and it provides a useful method towards a complete tool in this domain.

Intelligent spot detection for 2-DE gel image

In this study, a novel method for spot detection is proposed with the addition of confidence evaluation for each detected spot. The confidence of a spot will give useful hints for subsequent processing such as landmark selection, spot quantification, gel image registration, etc. The proposed method takes slices of a gel image in the gray level direction and build them into a slice tree, which in turn is used to perform spot detection and confidence evaluation. Moreover, the proposed method is fast. Building slice tree for a gel image of 1262×720 take about 3.2 sec. Spot detection take about 66 ms after the slice tree was built. Experimental results show that confidence values are close to subjective judgement.