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IEEE 14th International Conference on Machine Learning and Applications ICMLA

作者： Khademi, Mahmoud Nedialkov, Nedialko S. McMaster Univ Dept Comp & Software Hamilton ON Canada

ISBN: (纸本)9781509002870

We propose a probabilistic graphical model (PGM) for prognosis and diagnosis of breast cancer. PGMs are suitable for building predictive models in medical applications, as they are powerful tools for making decisions under uncertainty from big data with missing attributes and noisy evidence. Previous work relied mostly on clinical data to create a predictive model. Moreover, practical knowledge of an expert was needed to build the structure of a model, which may not be accurate. In our opinion, since cancer is basically a genetic disease, the integration of microarray and clinical data can improve the accuracy of a predictive model. However, since microarray data is high-dimensional, including genomic variables may lead to poor results for structure and parameter learning due to the curse of dimensionality and small sample size problems. We address these problems by applying manifold learning and a deep belief network (DBN) to microarray data. First, we construct a PGM and a DBN using clinical and microarray data, and extract the structure of the clinical model automatically by applying a structure learning algorithm to the clinical data. Then, we integrate these two models using softmax nodes. Extensive experiments using real-world databases, such as METABRIC and NKI, show promising results in comparison to Support Vector Machines (SVMs) and k-Nearest Neighbors (k-NN) classifiers, for classifying tumors and predicting events like recurrence and metastasis.

关键词： probabilistic graphical models deep belief networks breast cancer microarray data

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Computational Identification of Proteins Sub-network in Parkinson's Disease Study

Computational Identification of Proteins Sub-network in Park...

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6th International Conference on Anti-Counterfeiting, Security and Identification (ASID)

作者： Huang, Yue Zhang, Jun Huang, Yunying Xiamen Univ Dept Commun Engn Xiamen Peoples R China Xiamen Univ Tan Kah Kee Coll Dept Elect Engn Xiamen Peoples R China

ISBN: (纸本)9781467321433

Parkinson's disease (PD) is a typical case of neurodegenerative disorder, which often impairs the sufferer's motor skills, speech, and other functions. Combination of protein-protein interaction (PPI) network analysis and gene expression studies provides a better insight of Parkinson's disease. In our work a computational approach was developed to identify protein signal network in PD study. First, a network-constrain regularization analysis is employed to the linear regression model for gene expression data from transgenic mouse models in normal and with Parkinson's disease. Proteins sub-network was then detected based on an integer linear programming model by integrating microarray data and PPI database.

关键词： Parkinson's disease microarray data linear regression model integer linear programming protein network detection

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CONTROL OF THE MEAN NUMBER OF FALSE DISCOVERIES, BONFERRONI AND STABILITY OF MULTIPLE TESTING

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ANNALS OF APPLIED STATISTICS 2007年第1期1卷 179-190页

作者： Gordon, Alexander Glazko, Galina Qiu, Xing Yakovlev, Andrei Univ Rochester Dept Biostat & Computat Biol Rochester NY 14642 USA Univ N Carolina Dept Math & Stat Charlotte NC 28223 USA

The Bonferroni multiple testing procedure is commonly perceived as being overly conservative in large-scale Simultaneous testing situations such as those that arise in microarray data analysis. The objective of the present study is to show that this Popular belief is due to overly stringent requirements that are typically imposed oil the procedure rather than to its conservative nature. To get over its notorious conservatism, we advocate using the Bonferroni selection rule as a procedure that controls the per family error rate (PFER). The present paper reports the first Study of stability properties of the Bonferroni and Benjamini-Hochberg procedures. The Bonferroni procedure shows I superior stability in terms of the variance of both the number of true discoveries and the total number of discoveries. a property that is especially important in the presence of correlations between individual p-values. Its stability and the ability to provide strong control of the PFER make the Bonferroni procedure an attractive choice in microarray studies.

关键词： Multiple testing stability Bonferroni method microarray data

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Phase-wise Clustering of Time Series Gene Expression data

Phase-wise Clustering of Time Series Gene Expression Data

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Int Joint Conference of 10th IEEE Int Conf on Trust, Security and Privacy in Computing and Communications (TrustCom) / 8th IEEE Int Conf on Embedded Software and Systems (ICESS) / 6th Int Conf on Frontier of Computer Science and Technology (FCST)

作者： Goyal, Poonam Karwa, Rohan Sunil Goyal, Navneet John, Matthew BITS Pilani Dept Comp Sci Pilani Rajasthan India

ISBN: (纸本)9780769546001

Extensive studies have shown that analyzing microarray time series data is important in bioinformatics research and biomedical applications. An observation in the analysis of gene expression data is that many genes have similarity in their expression patterns and therefore appear to be co-regulated. Previously, the time series gene expression data was analyzed mainly by checking the global similarities between the gene expression profiles and local similarities were overlooked. Local similarities can provide useful insight into gene behavior. In this paper, we propose a clustering algorithm for analyzing the time series gene expression data to identify the gene clusters based on the phase-wise local similarities in the cell cycle. Our approach exploits the fact that the genes which are involved in one phase of a cell cycle would have a characteristic profile for time points belonging to that phase and may not be involved in other phases. Moreover, a gene that is clustered with a set of genes in one phase might be involved with a different set of genes in other phases. In the proposed approach, we first clustered the genes at every time point of a phase and group genes with similar expression profiles, i.e., we group those genes together which remain in the same cluster at every time point within a phase. The functions of genes were obtained from Gene Ontology. In this paper, the results are presented for different phases of a cell cycle. Candidate genes are identified for these phases and their groups are analyzed. We found that the group of candidate genes had few genes which are known to be involved. Furthermore, some genes are found to be involved in more than one phase with different set of genes. Results presented show that local similarities can provide useful insight into gene behavior. Results are compared with an existing algorithm, STEM. We have used a saccharomyces cerevisiae cell cycle microarray database which is part of the Stanford microarray database

关键词： component Time-series gene expression data microarray data Clustering gene expression profiles

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Utilizing evolutionary information and gene expression data for estimating gene networks with Bayesian network models

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Journal of Bioinformatics and Computational Biology 2005年第6期3卷 1295-1313页

作者： Tamada, Yoshinori Bannai, Hideo Imoto, Seiya Katayama, Toshiaki Kanehisa, Minoru Miyano, Satoru Bioinformatics Center Institute for Chemical Research Kyoto University Uji Kyoto 611-0011 Gokasho Japan Human Genome Center Institute of Medical Science The University of Tokyo Minato-ku Tokyo 108-8639 4-6-1 Shirokanedai Japan

Since microarray gene expression data do not contain sufficient information for estimating accurate gene networks, other biological information has been considered to improve the estimated networks. Recent studies have revealed that highly conserved proteins that exhibit similar expression patterns in different organisms, have almost the same function in each organism. Such conserved proteins are also known to play similar roles in terms of the regulation of genes. Therefore, this evolutionary information can be used to refine regulatory relationships among genes, which are estimated from gene expression data. We propose a statistical method for estimating gene networks from gene expression data by utilizing evolutionarily conserved relationships between genes. Our method simultaneously estimates two gene networks of two distinct organisms, with a Bayesian network model utilizing the evolutionary information so that gene expression data of one organism helps to estimate the gene network of the other. We show the effectiveness of the method through the analysis on Saccharomyces cerevisiae and Homo sapiens cell cycle gene expression data. Our method was successful in estimating gene networks that capture many known relationships as well as several unknown relationships which are likely to be novel. © Imperial College Press.

关键词： Bayesian network Evolutionary information Gene network microarray data

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Markov Blanket: Efficient Strategy For Feature Subset Selection Method For High Dimensional microarray Cancer datasets

Markov Blanket: Efficient Strategy For Feature Subset Select...

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Biological Ontologies and Knowledge Bases Workshop at IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM)

作者： Passi, Kalpdrum Nour, Abdala Jain, Chakresh Kumar Laurentian Univ Dept Math & Comp Sci Sudbury ON Canada Jaypee Inst Informat Technol Dept Biotechnol Noida India

ISBN: (纸本)9781509030507

In this paper, we discuss the importance of feature subset selection methods in machine learning techniques. An analysis of microarray expression was used to check whether global biological differences underlie common pathological features for different types of cancer datasets and to identify genes that might anticipate the clinical behavior of this disease. One way of finding relevant gene selection is by using Bayesian network based on Markov blanket. We present and compare the performance of the different approaches of features (genes) subset selection methods based on Wrapper and Markov Blanket models for the five-microarray cancer datasets. The first alternative depends on Memetic algorithms (MAs) for feature selection method. In the second alternative, we use MRMR (Minimum Redundant Maximum Relevant) for feature subset selection method hybridized by genetic search optimization techniques. We compare the performance of Markov blanket model with most common classification algorithms for those set of features. The results show that the performance measures of classification algorithms based on Markov Blanket model mostly offer better accuracy rates than other types of classical classification algorithms for the cancer microarray datasets.

关键词： microarray data feature subset selection Markov blanket Memetic algorithms Minimum Redundant Maximum Relevant

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An Incremental Updating Based Fast Phenotype Structure Learning Algorithm

An Incremental Updating Based Fast Phenotype Structure Learn...

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10th International Conference on Intelligent Computing (ICIC)

作者： Cheng, Hao Zhao, Yu-Hai Yin, Ying Zhang, Li-Jun Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China Northeastern Univ Coll Sci Shenyang Peoples R China

ISBN: (纸本)9783319093307;9783319093291

Unsupervised phenotype structure learning is important in microarray data analysis. The goal is to (1) find groups of samples corresponding to different phenotypes (e.g. disease or normal), and (2) find a subset of genes that can distinguish different groups. Due to the large number of genes and a mass of noise in microarray data, the existing methods are often of some limitations in terms of efficicency and effectiveness. In this paper, we develop an incremental updating based phenotype structure learning algorithm, namely FPLA. With a randomly selected initial state, the algorithm iteratively tries three possible adjustments, i.e. gene addition, gene deletion and sample move, to improve the quality of the current result. Accordingly, four incremental updating based optimization strategies are devised to eliminate the redundancy computations in each iteration. Further, by utilizing a harmonic quality function, it improves the result accuracy by penalizing the "outlier" effect. The experiments conducted on several real microarray datasets show that FPLA outperforms the two representative competing algorithms on both effectiveness and efficiency.

关键词： data mining microarray data phenotype structure bioinformatics

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Inference of gene regulatory networks using S-system and differential evolution 05

Inference of gene regulatory networks using S-system and dif...

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Genetic and Evolutionary Computation Conference

作者： Noman, Nasimul Iba, Hitoshi Univ Tokyo Dept Frontier Informat Chiba 2778561 Japan

ISBN: (纸本)1595930108

In this work we present an improved evolutionary method for inferring S-system model of genetic networks from the time series data of gene expression. We employed Differential Evolution (DE) for optimizing the network parameters to capture the dynamics in gene expression data. In a preliminary investigation we ascertain the suitability of DE for a multimodal and strongly non-linear problem like gene network estimation. An extension of the fitness function for attaining the sparse structure of biological networks has been proposed. For estimating the parameter Values more accurately an enhancement of the optimization procedure has been also suggested. The effectiveness of the proposed method was justified performing experiments on a genetic network using different numbers of artificially created time series data.

关键词： S-system gene regulatory network Differential Evolution microarray data Reverse Engineering

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Inferring gene regulatory networks from multiple data sources via a dynamic Bayesian network with structural EM

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4th International Workshop on data Integration in the Life Sciences

作者： Zhang, Yu Deng, Zhidong Jiang, Hongshan Jia, Peifa Tsinghua Univ Comp Sci & Technol Dept State Key Lab Intelligent Technol & Syst Beijing 100084 Peoples R China Tsinghua Univ Dept Comp Sci Beijing 100084 Peoples R China

ISBN: (纸本)9783540732549

Using our dynamic Bayesian network with structural Expectation Maximization (SEM-DBN), we develop a new framework to model gene regulatory network from both gene expression data and transcriptional factor binding site data. Only based on mRNA expression data, it is not enough to accurately estimate a gene network. It is difficult for us to estimate a gene network accurately only with the mRNA expression data. In this paper, we use the transcription factor binding location data in order to introduce the prior knowledge to SEM-DBN model. Gene expression data are also exploited specifically for likelihood. Meanwhile, we incorporate the prior knowledge into every learning step by SEM rather than only learning from the very beginning, which can compensate the attenuation of the effect with location data. The effectiveness of our proposed method is demonstrated through the analysis of Saccharomyces cerevisiae cell cycle data. The combination of heterogeneous data from multiple sources ensures that our results are more accurate than those recovered from only gene expression data alone.

关键词： gene regulatory networks dynamic Bayesian network structural expectation maximization microarray data transcription factor binding location data

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S-System Based Gene Regulatory Network Reconstruction Using Firefly Algorithm 3

S-System Based Gene Regulatory Network Reconstruction Using ...

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3rd International Conference on Computer, Communication, Control and Information Technology (C3IT)

作者： Mandal, Sudip Saha, Goutam Pal, Rajat K. GIMT ECE Dept Krishna Nagar India NEHU IT Dept Shillong Meghalaya India Univ Calcutta CSE Dept Kolkata India

ISBN: (纸本)9781479944453

The correct inference of gene regulatory network plays a critical role in understanding biological regulation in cells and genome based therapeutics. DNA microarray is the most widely used technology for extracting the relationships between thousands of genes simultaneously. Since S-system is based on the rate law, it is considered as a suitable mathematical model for representing complex biological reactions between genes. As, this problem has multiple solutions, optimized solution need to be identified via different nature inspired metaheuristic algorithms. So, in this paper, a new method is elaborated that helps to infer gene regulatory network for Lung Adenocarcinoma using S-system and Firefly Optimization which is an efficient but simple metaheuristic inspired by natural motion of fireflies. By optimizing the values of parameters of the S-system model, gene network can be easily reconstructed and inferred. Though direct biological validation of the network is not possible, but accuracy of the proposed method can be described as how well the network fit with the initial training data for different genes which is quite satisfactory for our research work.

关键词： Gene Regulatory Network microarray data S-stem Firefly Algorithm

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