Boonserm Kaewkamnerdpong

Heterogeneous ensemble approach with discriminative features and modified-SMOTEbagging for pre-miRNA classification

An ensemble classifier approach for microRNA precursor (pre-miRNA) classification was proposed based upon combining a set of heterogeneous algorithms including support vector machine (SVM), k-nearest neighbors (kNN) and random forest (RF), then aggregating their prediction through a voting system. Additionally, the proposed algorithm, the classification performance was also improved using discriminative features, self-containment and its derivatives, which have shown unique structural robustness characteristics of pre-miRNAs. These are applicable across different species. By applying preprocessing methods—both a correlation-based feature selection (CFS) with genetic algorithm (GA) search method and a modified-Synthetic Minority Oversampling Technique (SMOTE) bagging rebalancing method—improvement in the performance of this ensemble was observed. The overall prediction accuracies obtained via 10 runs of 5-fold cross validation (CV) was 96.54%, with sensitivity of 94.8% and specificity of 98.3%—this is better in trade-off sensitivity and specificity values than those of other state-of-the-art methods. The ensemble model was applied to animal, plant and virus pre-miRNA and achieved high accuracy, >93%. Exploiting the discriminative set of selected features also suggests that pre-miRNAs possess high intrinsic structural robustness as compared with other stem loops. Our heterogeneous ensemble method gave a relatively more reliable prediction than those using single classifiers. Our program is available at http://ncrna-pred.com/premiRNA.html.

Reducing bioinformatics data dimension with ABC-kNN

Abstract Analyzing a large amount of data often consumes extensive computational resources and execution time. However, sometime all data features do not equally contribute to the end results. Thus, it is plausible to identify the major contributing features and use them as representatives of the data. Other features with low contribution can be eliminated to reduce the time/resource consumption in data analysis. One of the promising application domains for such a feature selection process is bioinformatics. The need for dimension reduction, which is the process to reduce unnecessary features from the original data, arises because biological data can be massive, with tens of thousands of features to be explored. The objective of this study is to design an effective algorithm that can selectively remove irrelevant dimensions from data describing complex biological processes while preserving the semantics of the original data. This research proposes the adoption of the Artificial Bee Colony (ABC) as a novel method for data dimension reduction in classification problems. ABC, an efficient heuristic method based on swarm intelligence, is used to select the optimal subset of dimensions from the original high-dimensional data while retaining a subset that satisfies the defined objective. The k-Nearest Neighbor (kNN) method is then used for fitness evaluation within the ABC framework. In this research, ABC and kNN have been modified and bundled together to create an effective dimension reduction method. The proposed algorithm is validated in two distinct application domains: Gene expression analysis, and autistic behaviors study. The experimental results exhibit good solution quality as well as good computational performance.

Perceptive particle swarm optimisation: an investigation

Conventional particle swarm optimisation relies on exchanging information through social interaction among individuals. However for real-world problems involving control of physical agents (i.e., robot control), such detailed social interaction is not always possible. Recently, the perceptive particle swarm optimisation (PPSO) algorithm was proposed to mimic behaviours of social animals more closely through both social interaction and environmental interaction for applications such as robot control. In this study, we investigate the PPSO algorithm on complex function optimisation problems and its ability to cope with noisy environments.

Identification of non-coding RNAs with a new composite feature in the Hybrid Random Forest Ensemble algorithm

To identify non-coding RNA (ncRNA) signals within genomic regions, a classification tool was developed based on a hybrid random forest (RF) with a logistic regression model to efficiently discriminate short ncRNA sequences as well as long complex ncRNA sequences. This RF-based classifier was trained on a well-balanced dataset with a discriminative set of features and achieved an accuracy, sensitivity and specificity of 92.11%, 90.7% and 93.5%, respectively. The selected feature set includes a new proposed feature, SCORE. This feature is generated based on a logistic regression function that combines five significant features—structure, sequence, modularity, structural robustness and coding potential—to enable improved characterization of long ncRNA (lncRNA) elements. The use of SCORE improved the performance of the RF-based classifier in the identification of Rfam lncRNA families. A genome-wide ncRNA classification framework was applied to a wide variety of organisms, with an emphasis on those of economic, social, public health, environmental and agricultural significance, such as various bacteria genomes, the Arthrospira (Spirulina) genome, and rice and human genomic regions. Our framework was able to identify known ncRNAs with sensitivities of greater than 90% and 77.7% for prokaryotic and eukaryotic sequences, respectively. Our classifier is available at http://ncrna-pred.com/HLRF.htm.

Perceptive Particle Swarm Optimisation

Conventional particle swarm optimisation relies on exchanging information through social interaction among individuals. However for real-world problems involving control of physical agents (i.e., robot control), such detailed social interaction is not always possible. In this study, we propose the Perceptive Particle Swarm Optimisation algorithm, in which both social interaction and environmental interaction are increased to mimic behaviours of social animals more closely.

Best-So-Far ABC Based Nanorobot Swarm

Nanotechnology has continuously advanced with many tremendous promises to offer. The coming of nanorobots seems inevitable. Medical applications would be one of the first utilization of nanorobots. Nanorobot may become a solution for many currently incurable diseases as it can be viewed as a medical instrument that can be released inside human body for drug delivery and diagnosis [1-3]. In order to effectively utilize nanorobots, the concept of swarm intelligence is extensively studied in the literature [4-8]. In this study, we proposed a framework based on swarm intelligence concept for nanorobot control in medical applications. We adopted a variation of Artificial Bee Colony (ABC) called Best-so-far ABC to regulate nanorobot behavior. Best-so-far ABC biases solutions toward the best position of current iteration. The proposed framework is demonstrated in wounds healing application, nanorobots operate inside a network of blood vessels as artificial platelets to assist in hemostasis process to stop bleeding. Nanorobots must manage to attend to all multiple wounds. The demonstration is one example of possible medical applications of the swarm-intelligence-based nanorobot framework.

Dimensional reduction based on artificial bee colony for classification problems

High dimensionality of data is a limiting factor to data processing in many fields. It causes ambiguousness in identifying significant factors for data analysis. Dimension reduction is needed to separate irrelevant data from the desired data. This research proposes a novel method for dimension reduction based on artificial bee colony (ABC). The method employs swarm intelligence based on bee foraging model in order to select features that allow us to generate subsets of dimensions from the original high-dimensional data while the resulting subsets satisfy the defined objective. Support vector machine (SVM) is used in this study as fitness evaluation of ABC in classification problems. To evaluate our method, we tested it with five datasets and compared it with other dimension reduction algorithms. The result of this study shows that using ABC and SVM is suitable for reducing the dimension of data. Moreover, this approach provides efficient classification with high accuracy.

Modelling nanorobot control using swarm intelligence: A pilot study

Advances in the development of nanotechnology gradually bring the field into its next generation involving systems of nanosystems. These bring about opportunities for computer science researchers to contribute their work as guidelines for the realisation and development of nanorobot systems in the near future. It is anticipated that an early version of future nanorobots may potentially contain only essential characteristics and exhibit only simple behaviours. It is similar to social insects in nature; collaborative behaviour among such simple individual exhibits a remarkable degree of intelligence. Hence, swarm intelligence techniques inspired by social insects could potentially be applied for nanorobot control mechanism in self-assembly. This study models an early version of future nanorobots and a control mechanism using swarm intelligence, especially PPSO (the modification of PSO for physical applications), for self-assembly and self-repair to examine the minimal characteristics and functionality for future nanorobots.

Programming Nanotechnology: Learning from Nature

For many decades, nanotechnology has been developed with cooperation from researchers in several fields of studies including physics, chemistry, biology, material science, engineering, and computer science. In this chapter, we explore the nanotechnology development community and identify the needs and opportunities of computer science research in nanotechnology. In particular we look at methods for programming future nanotechnology, examining the capabilities offered by simulations and intelligent systems. This chapter is intended to benefit computer scientists who are keen to contribute their works to the field of nanotechnology and also nanotechnologists from other fields by making them aware of the opportunities from computer science. It is hoped that this may lead to the realisation of our visions.

Thai Phoneme Segmentation using Discrete Wavelet Transform

Currently, the core of Thai speech recognition algorithms focuses on word recognition. However, such algorithms are not appropriate to construct the Speech-to-Text system since the ultimate goal in Speech-to-Text system is to recognize continuous speech states from any speaker of a given language. The meaning in each given language and its sound can be determined by phonemes which are slightly different for each language. The variability in each speakers voice, for instance, accents, gender and speech style, and the tonal language such as Thai language can create rather different speech signals for the same word. Thus, phoneme recognition is more difficult to perform. Since segmentation takes place prior to recognition in such systems, an incorrect segmentation invariably leads to incorrect recognition results. We proposed a method for phoneme segmentation that based on Discrete Wavelet Transform (DWT). To verify our method, we performed experiments on eleven speakers: five males and six females. Each speaker pronounced one hundred and thirty Thai words. Then, we evaluated the performance of our method by synthesizing the new words from the obtained phonemes. The speech synthesis of the new words was then observed by humans to compare with the natural-sounding speech. The results indicated 95% accuracy.