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ieee symposium Series on computational intelligence (ieee SSCI)

作者： Esterly, Elizabeth E. McCreery, Helen Nagpal, Radhika Univ New Mexico Dept Comp Sci Albuquerque NM 87131 USA Michigan State Univ Dept Integrat Biol E Lansing MI 48824 USA Harvard Univ Dept Comp Sci Cambridge MA 02138 USA

ISBN: (纸本)9781538627266

Cooperative transport is an impressive example of collective behavior in ants, where groups of ants work together to move heavy food objects back to their nest over heterogeneous terrain. This behavior also serves as a model for bio-inspired robotics. While many studies have considered the mechanisms by which ants transport objects in simple settings, few have looked at how they deal with obstacles and heterogeneous terrain. A recent study on Paratrechina longicornis (crazy ants) demonstrated that groups of these ants implement a stochastic, adaptive, and robust cooperative transport strategy that allows them to succeed at navigating challenging obstacles that require moving away from their goal. In this paper, we use group-level computational models to investigate the significance and implications of this biological strategy. We develop an algorithm that reproduces important elements of the strategy, and compare it to several benchmark algorithms for a range of obstacle sizes and shapes. Our results show that, for smaller obstacles, the ant-inspired adaptive stochastic strategy is adept at efficient navigation, due to its ability to match the level of randomness it uses to unknown object size and shape. We also find some unexpected differences between our results and the original ant transport behavior, suggesting new biological experiments.

关键词： Stochastic processes Navigation biology computational modeling Robots Shape Trajectory

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Use Chou's 5-Steps Rule With Different Word Embedding Types to Boost Performance of Electron Transport Protein Prediction Model

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ieee/ACM Transactions on computational biology and bioinformatics 2020年第2期19卷

作者： Trinh-Trung-Duong Nguyen Quang-Thai Ho Nguyen-Quoc-Khanh Le Van-Dinh Phan Yu-Yen Ou Department of Computer Science and Engineering Yuan Ze University Chung-Li Taiwan Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical Univeristy Taipei City Taiwan University of Economics The University of Danang Da Nang Vietnam

Living organisms receive necessary energy substances directly from cellular respiration. The completion of electron storage and transportation requires the process of cellular respiration with the aid of electron transport chains. Therefore, the work of deciphering electron transport proteins is inevitably needed. The identification of these proteins with high performance has a prompt dependence on the choice of methods for feature extraction and machine learning algorithm. In this study, protein sequences served as natural language sentences comprising words. The nominated word embedding-based feature sets, hinged on the word embedding modulation and protein motif frequencies, were useful for feature choosing. Five word embedding types and a variety of conjoint features were examined for such feature selection. The support vector machine algorithm consequentially was employed to perform classification. The performance statistics within the 5-fold cross-validation including average accuracy, specificity, sensitivity, as well as MCC rates surpass 0.95. Such metrics in the independent test are 96.82, 97.16, 95.76 percent, and 0.9, respectively. Compared to state-of-the-art predictors, the proposed method can generate more preferable performance above all metrics indicating the effectiveness of the proposed method in determining electron transport proteins. Furthermore, this study reveals insights about the applicability of various word embeddings for understanding surveyed sequences.

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A Cooperative Framework for Fireworks Algorithm

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2017年第1期14卷 27-41页

作者： Zheng, Shaoqiu Li, Junzhi Janecek, Andreas Tan, Ying Peking Univ Dept Machine Intelligence Key Lab Machine Percept Minist Educ Nanjing 210007 Jiangsu Peoples R China China Elect Technol Grp Corp Res Inst 28 Nanjing 210007 Jiangsu Peoples R China Peking Univ Sch Elect Engn & Comp Sci Dept Machine Intelligence Beijing 100871 Peoples R China Peking Univ Key Lab Machine Percept Minist Educ Beijing 100871 Peoples R China Univ Vienna Res Grp Theory & Applicat Algorithms Vienna Austria

This paper presents a cooperative framework for fireworks algorithm (CoFFWA). A detailed analysis of existing fireworks algorithm (FWA) and its recently developed variants has revealed that (i) the current selection strategy has the drawback that the contribution of the firework with the best fitness (denoted as core firework) overwhelms the contributions of all other fireworks (non-core fireworks) in the explosion operator, (ii) the Gaussian mutation operator is not as effective as it is designed to be. To overcome these limitations, the CoFFWA is proposed, which significantly improves the exploitation capability by using an independent selection method and also increases the exploration capability by incorporating a crowdness-avoiding cooperative strategy among the fireworks. Experimental results on the CEC2013 benchmark functions indicate that CoFFWA outperforms the state-of-the-art FWA variants, artificial bee colony, differential evolution, and the standard particle swarm optimization SPSO2007/SPSO2011 in terms of convergence performance.

关键词： Swarm intelligence fireworks algorithm explosion amplitude cooperative strategy

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A New Feature Vector Based on Gene Ontology Terms for Protein-Protein Interaction Prediction

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2017年第4期14卷 762-770页

作者： Bandyopadhyay, Sanghamitra Mallick, Koushik Indian Stat Inst Machine Intelligence Unit Kolkata 700108 India

Protein-protein interaction (PPI) plays a key role in understanding cellular mechanisms in different organisms. Many supervised classifiers like Random Forest (RF) and Support Vector Machine (SVM) have been used for intra or inter-species interaction prediction. For improving the prediction performance, in this paper we propose a novel set of features to represent a protein pair using their annotated Gene Ontology (GO) terms, including their ancestors. In our approach, a protein pair is treated as a document (bag of words), where the terms annotating the two proteins represent the words. Feature value of each word is calculated using information content of the corresponding term multiplied by a coefficient, which represents the weight of that term inside a document (i.e., a protein pair). We have tested the performance of the classifier using the proposed feature on different well known data sets of different species like S. cerevisiae, H. Sapiens, E. Coli, and D. melanogaster. We compare it with the other GO based feature representation technique, and demonstrate its competitive performance.

关键词： Protein interaction prediction GO based feature kernel methods for PPI prediction

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Convolutional Neural Network Approach to Lung Cancer Classification Integrating Protein Interaction Network and Gene Expression Profiles

Convolutional Neural Network Approach to Lung Cancer Classif...

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ieee symposium on bioinformatics and Bioengineering (BIBE)

作者： Teppei Matsubara Jose C. Nacher Tomoshiro Ochiai Morihiro Hayashida Tatsuya Akutsu Dept. Information Science Faculty of Science Toho University Funabashi Chiba Japan Toho Daigaku Ota-ku Tokyo JP Department of Social Information Studies Otsuma Women's University Tokyo Japan Dept. of Electrical Engineering and Computer Science Matsue College Shimane Japan

ISBN: (纸本)9781538650431

Deep learning technologies are permeating every field from image and speech recognition to computational and systems biology. However, the application of convolutional neural networks to 'omics' data poses some difficulties, such as the processing of complex networks structures as well as its integration with transcriptome data. Here, we propose a convolutional neural network (CNN) approach that combines spectral clustering information processing to classify lung cancer. The developed spectral-convolutional neural network based method achieves success in integrating protein interaction network data and gene expression profiles to classify lung cancer. Data and CNN code can be downloaded from the link: https://***/site/nacherlab/analysis.

关键词： Proteins Gene expression Two dimensional displays Convolutional neural networks Cancer bioinformatics Lung

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Nurturing Promotes the Evolution of Reinforcement Learning in Changing Environments

Nurturing Promotes the Evolution of Reinforcement Learning i...

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ieee symposium Series on computational intelligence (ieee SSCI)

作者： Shah, Syed Naveed Hussain Hougen, Dean F. Univ Oklahoma Gallogly Coll Engn Sch Comp Sci Robot Evolut Adaptat & Learning Lab Norman OK 73019 USA Microsoft Corp Redmond WA 98052 USA

ISBN: (纸本)9781538627266

An agent interacting with its environment may learn to perform complex tasks through reinforcement learning. Reinforcement learning requires exploration of unfamiliar situations, which necessarily involves unknown and potentially dangerous or costly outcomes. Various sorts of external support for the learning agent are possible through investments of time or other resources. Nurturing, one individual investing in the development of another individual with which it has an ongoing relationship, is widely seen in the biological world, often with parents nurturing their offspring. In artificial intelligence, nurturing can be seen as an opportunity to develop both better machine learning algorithms and robots that assist or supervise other robots. Although research into robot-to-robot nurturing is at a very early stage, the hope is that this approach can result in more sophisticated learning systems. The research presented here demonstrates the effectiveness of nurturing through the evolution of the parameters of a reinforcement learning algorithm that is capable of finding good policies in a changing environment.

关键词： Robots Evolution (biology) Task analysis Switches Light sources Learning (artificial intelligence) Learning systems

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A Normalisation Strategy to Optimally Design Experiments in computational biology 1

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11th International Conference on Practical Applications of computational biology and bioinformatics (PACBB)

作者： Garcia, Miriam R. Alonso, Antonio A. Balsa-Canto, Eva CSIC IIM Bioproc Engn Grp Vigo Spain

ISBN: (数字)9783319608167

ISBN: (纸本)9783319608167;9783319608150

In this work we describe a new methodology to improve predictive capabilities of dynamic models when parameters differ in orders of magnitude. The main idea is to normalise the model unknown parameters before solving the classical problem of optimal experimental design based on the Fisher information matrix. The normalisation improves the relative confidence intervals of the estimated parameters and the conditioning of the Fisher matrix, especially for those criteria aiming to decorrelate the model parameters. Using the so-called core predictions, we show how the new approach improves the final model predictive capabilities in two terms: predictions are closer to the real dynamics and with better confidence intervals. We illustrate the concepts using two toy examples linear and non-linear in their parameters. Finally we test the performance of the normalisation in a model simulating the bacterial SOS response. This pathway remains of main relevance to work towards a predictive model of antimicrobial resistance.

关键词： Normalisation Fisher Information Matrix (FIM) Relative parameter confidence intervals Core predictions Optimal Experimental Design (OED) Bacterial SOS response

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Significance and Functional Similarity for Identification of Disease Genes

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2017年第6期14卷 1419-1433页

作者： Maji, Pradipta Shah, Ekta Indian Stat Inst Biomed Imaging & Bioinformat Lab Machine Intelligence Unit Kolkata 700108 India

One of the most significant research issues in functional genomics is insilico identification of disease related genes. In this regard, the paper presents a new gene selection algorithm, termed as SiFS, for identification of disease genes. It integrates the information obtained from interaction network of proteins and gene expression profiles. The proposed SiFS algorithm culls out a subset of genes from microarray data as disease genes by maximizing both significance and functional similarity of the selected gene subset. Based on the gene expression profiles, the significance of a gene with respect to another gene is computed using mutual information. On the other hand, a new measure of similarity is introduced to compute the functional similarity between two genes. Information derived from the protein-protein interaction network forms the basis of the proposed SiFS algorithm. The performance of the proposed gene selection algorithm and new similarity measure, is compared with that of other related methods and similarity measures, using several cancer microarray data sets.

关键词： Disease gene identification microarray data analysis feature selection protein-protein interaction network

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An Automated Colourimetric Test by computational Chromaticity Analysis: A Case Study of Tuberculosis Test 11th

An Automated Colourimetric Test by Computational Chromaticit...

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11th International Conference on Practical Applications of computational biology and bioinformatics (PACBB)

作者： Tania, Marzia Hoque Lwin, K. T. AbuHassan, Kamal Bakhori, Noremylia Mohd Azmi, Umi Zulaikha Mohd Yusof, Nor Azah Hossain, M. A. Anglia Ruskin Univ Anglia Ruskin IT Res Inst ARITI Chelmsford England Univ Putra Malaysia Inst Adv Technol Serdang Malaysia Univ Putra Malaysia Dept Chem Fac Sci Serdang Malaysia

ISBN: (纸本)9783319608167;9783319608150

This paper presents an investigation into a novel approach for an automated universal colourimetric test by chromaticity analysis. This work particularly focuses on how a well-adjusted harmony between computational complexity and biochemical analysis can reduce the associated cost and unlock the limit on conventional chemical practice. The proposed research goal encompasses the potential to the criteria- anytime anywhere access, low cost, rapid detection, better sensitivity, specificity and accuracy. Our method includes obtaining the amount of colour change for each instance by delta E calculation. The system can provide the result in any ambient condition from the trajectory of colour change using Euclidean distance in LAB colour space. The strategy is verified on plasmonic ELISA based diagnosis of tuberculosis (TB). TB detection by plasmonic ELISA is a challenging, demanding and a time-consuming diagnosis. Completing the computation in real time, we circumvent the obstacle liberating the TB diagnosis in less than 15 min.

关键词： Colourimetric test Plasmonic ELISA TB test Chromatic analysis Delta E

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K-Means Clustering with Infinite Feature Selection for Classification Tasks in Gene Expression Data 1

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11th International Conference on Practical Applications of computational biology and bioinformatics (PACBB)

作者： Remli, Muhammad Akmal Daud, Kauthar Mohd Nies, Hui Wen Mohamad, Mohd Saberi Deris, Safaai Omatu, Sigeru Kasim, Shahreen Sulong, Ghazali Univ Teknol Malaysia Fac Comp Artificial Intelligence & Bioinformat Res Grp Skudai 81310 Johor Malaysia Univ Malaysia Kelantan Fac Creat Technol & Heritage Locked Bag 01 Kota Baharu 16300 Kelantan Malaysia Osaka Inst Technol Dept Elect Informat & Commun Engn Osaka 5358585 Japan Univ Tun Hussein Onn Malaysia Fac Comp Sci & Informat Technol Batu Paha 86400 Malaysia Univ Malaysia Terengganu Sch Informat & Appl Math Kuala Nerus 21030 Terengganu Malaysia

ISBN: (数字)9783319608167

ISBN: (纸本)9783319608167;9783319608150

In the bioinformatics and clinical research areas, microarray technology has been widely used to distinguish a cancer dataset between normal and tumour samples. However, the high dimensionality of gene expression data affects the classification accuracy of an experiment. Thus, feature selection is needed to select informative genes and remove non-informative genes. Some of the feature selection methods, yet, ignore the interaction between genes. Therefore, the similar genes are clustered together and dissimilar genes are clustered in other groups. Hence, to provide a higher classification accuracy, this research proposed k-means clustering and infinite feature selection for identifying informative genes in the selected subset. This research has been applied to colorectal cancer and small round blue cell tumors datasets. Eventually, this research successfully obtained higher classification accuracy than the previous work.

关键词： Gene expression data K-means clustering Infinite feature selection Cancer classification Small round blue cell tumors Informative genes Artificial intelligence

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