Shin Ando

Evolutionary modeling and inference of gene network

This paper describes an Evolutionary Modeling (EM) approach to building causal model of differential equation system from time series data. The main target of the modeling is the gene regulatory network. A hybrid method of Genetic Programming (GP) and statistical analysis is featured in our work. GP and Least Mean Square method (LMS) were combined to identify a concise form of regulation between the variables from a given set of time series. Our approach was evaluated in several real-world problems. Further, Monte Carlo analysis is applied to indicate the robust and significant influence from the results for gene network analysis purpose.

Inference of gene regulatory model by genetic algorithms

Presents an application of genetic algorithms (GAs) to the gene network inference problem; this is one of the active topics in recent bioinformatics. The objective is to predict a regulating network structure of the interacting genes from the observed outcome, i.e. expression pattern. The task consists of modeling the rules of regulation and inferring the network structure from the observed data. The GA is applied to training the model with observed data in order to predict the regulatory pathways, represented as an influence matrix. We have implemented a reverse engineering method based on GAs in a quantitative and linear biological framework. The merit of this approach is that it can be applied with a small amount of data, it can optimize large numbers of parameters simultaneously and it can be applied to nonlinear models. The GA implementation includes multi-stage evolution and matrix chromosomes. This method has been applied to both simulated and experimentally observed gene expression patterns. In this research, we used the knowledge of designing an electric circuit by a GA.

Analog circuit design with a variable length chromosome

This paper proposes a system of evolving analog circuits based on a variable length chromosome. Methods featured are the chromosome of a component list, the multi-stage evolution, and the pressure on the circuit size. A set of experiments are described to confirm the systems robustness, the scalability of a circuit, and the efficiency of time and the memory consumption. The first experiment shows the robustness supplied by the evolutionary method. The second one compares several types of chromosome implementation schemes. We also provide experiments to evaluate the multi-stage and scaling methods.

Clustering Needles in a Haystack: An Information Theoretic Analysis of Minority and Outlier Detection

Identifying atypical objects is one of the traditional topics in machine learning. Recently, novel approaches, e.g., Minority Detection and One-class clustering, have explored further to identify clusters of atypical objects which strongly contrast from the rest of the data in terms of their distribution or density. This paper analyzes such tasks from an information theoretic perspective. Based on Information Bottleneck formalization, these tasks interpret to increasing the averaged atypicalness of the clusters while reducing the complexity of the clustering. This formalization yields a unifying view of the new approaches as well as the classic outlier detection. We also present a scalable minimization algorithm which exploits the localized form of the cost function over individual clusters. The proposed algorithm is evaluated using simulated datasets and a text classification benchmark, in comparison with an existing method.

Adaptive isolation model using data clustering for multimodal function optimization

In this paper, we propose a GA model called Adaptive Isolation Model(AIM), for multimodal optimization. It uses a data clustering algorithm to detect clusters in GA population, which identifies the attractors in the fitness landscape. Then, subpopulations which makes-up the clusters are isolated and optimized independently. Meanwhile, the region of the isolated subpopulations in the original landscape are suppressed. The isolation increases comprehensiveness, i.e., the probability of finding weaker attractors, and the overall efficiency of multimodal search. The advantage of the AIM is that it does not require distance between the optima as a presumed parameter, as it is estimated from the variance/covariance matrix of the subpopulation.Further, AIMs behavior and efficiency is equivalent to basic GA in unimodal landscape, in terms of number of evaluation. Therefore, it is applied recursively to all subpopulations until they converge to a suboptima. This makes AIM suitable for locally-multimodal landscapes, which have closely located attractors that are difficult to distinguish in the initial run.The performance of AIM is evaluated in several benchmark problems and compared to iterated hill-climbing methods.

Classification of Gene Expression Profile Using Combinatory Method of Evolutionary Computation and Machine Learning

The analysis of large amount of gene expression profiles, which became available by rapidly developed monitoring tools, is an important task in bioinformatics. The problem we address is the discrimination of gene expression profiles of different classes, such as cancerous/benign tissues.Two subtasks in such problem, feature subset selection and inductive learning has critical effect on each other. In the wrapper’ approach, combinatorial search of feature subset is done with performance of inductive learning as search criteria. This paper compares few combinations of supervised learning and combinatorial search when used in the wrapper approach. Also an extended GA implementation is introduced, which utilizes Clonal selection, a data-driven selection method. It compares very well to standard GA. The analysis of the obtained classifier reveals synergistic effect of genes in discrimination of the profiles.

Artificial immune system for classification of cancer

This paper presents a method for cancer type classification based on microarray-monitored data. The method is based on artificial immune system(AIS), which utilizes immunological recognition for classification. The system evolutionarily selects important genes; optimize their weights to derive classification rules. This system was applied to gene expression data of acute leukemia patients to classify their cancer class. The primary result found few classification rules which correctly classified all the test samples and gave some interesting implications for feature selection principles.

Evolving analog circuits by variable length chromosomes

This chapter proposes a framework of evolutionary analog circuits. This system features robustness to noise, optimized scaling, and high efficiency. These features solve the problems of the analog circuit design and manufacture. Methods utilized by this system are list-based chromosome, adjusted fitness, and two-stage evolution. Several experiments are conducted to examine the effectiveness of each of the methods. The first experiment compares other types of chromosome for the analog circuit design. The second experiment examines the robustness of evolutionary analog circuits. The other experiments are on the deduction of scaling and two-stage evolution.

Classifying imbalanced data in distance-based feature space

Class imbalance is a significant issue in practical classification problems. Important countermeasures, such as re-sampling, instance-weighting, and cost-sensitive learning have been developed, but there are limitations as well as advantages to respective approaches. The synthetic re-sampling methods have wide applicability, but require a vector representation to generate additional instances. The instance-based methods can be applied to distance space data, but are not tractable with regard to a global objective. The cost-sensitive learning can minimize the expected cost given the costs of error, but generally does not extend to nonlinear measures, such as F-measure and area under the curve. In order to address the above shortcomings, this paper proposes a nearest neighbor classification model which employs a class-wise weighting scheme to counteract the class imbalance and a convex optimization technique to learn its weight parameters. As a result, the proposed model maintains the simple instance-based rule for prediction, yet retains a mathematical support for learning to maximize a nonlinear performance measure over the training set. An empirical study is conducted to evaluate the performance of the proposed algorithm on the imbalanced distance space data and make comparison with existing methods.

Artificial immune system for classification of gene expression data

DNA microarray experiments generate thousands of gene expression measurement simultaneously. Analyzing the difference of gene expression in cell and tissue samples is useful in diagnosis of disease. This paper presents an Artificial Immune System for classifying microarray-monitored data. The system evolutionarily selects important features and optimizes their weights to derive classification rules. This system was applied to two datasets of cancerous cells and tissues. The primary result found few classification rules which correctly classified all the test samples and gave some interesting implications for feature selection.