Fuzzy Clustering Models for Gene Expression Data Analysis

نویسنده

  • Yu Wang
چکیده

copies of full i tems can be reproduced, displayed or performed, and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided the authors, ti t le and full bibliographic details are given, as well as a hyperlink and/or URL to the original metadata page. The content must not be changed in any way. Full i tems must not be sold commercially in any format or medium without formal permission of the copyright holder. The full policy is available online: Declaration I declare that the work contained in this thesis has not been submitted for any other award. To the best of my knowledge and belief, this work fully acknowledges opinions, ideas and contributions from the work of others. Acknowledgements First of all, I would like to express my deep gratitude to my principal supervisor Maia Angelova. Without her encouragement and guidance, I cannot complete my Ph.D study. I would like to thank Mathematical Modeling Lab for excellent academic environment. In addition, my sincere appreciation to my first supervisor Akhtar Ali, he gives me many valuable suggestions. I would appreciate my parents support. I also owe special thanks to my wife for her patience and encouragement. Finally, I would like to appreciate Northumbria University and China Scholarship Council (CSC) to offer me a valuable opportunity studying in the UK. (2013) Fuzzy clustering of time series gene expression data with cubic-spline. Weighted kernel fuzzy c-means method for gene expression analysis, 2012 Spring Congress on Computational Biology and Bio-An automatic parameter selection in density weighted kernel fuzzy clustering for gene expression data analysis Bioinformatics (preparation) Abstract With the advent of microarray technology, it is possible to monitor gene expression of tens of thousands of genes in parallel. In order to gain useful biological knowledge, it is necessary to study the data and identify the underlying patterns, which challenges the conventional mathematical models. Clustering has been extensively used for gene expression data analysis to detect groups of related genes. The assumption in clustering gene expression data is that co-expression indicates co-regulation, thus clustering should identify genes that share similar functions. Microarray data contains plenty of uncertain and imprecise information. Fuzzy c-means (FCM) is an efficient model to deal with this type of data. However, it treats samples equally and cannot differentiate noise and meaningful data. In this thesis, motivated …

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Modification of the Fast Global K-means Using a Fuzzy Relation with Application in Microarray Data Analysis

Recognizing genes with distinctive expression levels can help in prevention, diagnosis and treatment of the diseases at the genomic level. In this paper, fast Global k-means (fast GKM) is developed for clustering the gene expression datasets. Fast GKM is a significant improvement of the k-means clustering method. It is an incremental clustering method which starts with one cluster. Iteratively ...

متن کامل

A Fuzzy Approach for Clustering Gene Expression Time Series Data

Identifying groups of genes that manifest similar expression patterns is crucial in the analysis of gene expression time series data. Choosing a similarity measure to determine the similarity or distance between profiles is an important task. Time series expression experiments are used to study a wide range of biological systems. More than 80% of all time series expression datasets are short (8...

متن کامل

Gene Expression Analysis Using Fuzzy K-Means Clustering

The recent advances of array technologies have made it possible to monitor huge amount of genes expression data. Clustering, for example, hierarchical clustering, self-organizing maps (SOM), kmeans clustering, has become important analysis for such gene expression data. We have applied the Fuzzy adaptive resonance theory (Fuzzy ART) [5] to the gene clustering of DNA microarray data and the clus...

متن کامل

Gene Expression Data Mining for Functional Genomics

Methods for supervised and unsupervised clustering and machine learning were studied in order to automatically model relationships between gene expression data and gene functions of the microorganism Escherichia coli. From a pre-selected subset of 265 genes (belonging to 3 functional groups) the function has been predicted with an accuracy higher than 50 % by various data mining methods describ...

متن کامل

Constrained Subspace Clustering for Time Series Gene Expression Data

For time series gene expression data, it is an important problem to find subgroups of genes with similar expression pattern in a consecutive time window. In this paper, we extend a fuzzy c-means clustering algorithm to construct two models to detect biclusters respectively, i.e., constant value biclusters and similarity-based biclusters whose gene expression profiles are similar within consecut...

متن کامل

An Analysis of Gene Expression Data using Penalized Fuzzy C-Means Approach

With the rapid advances of microarray technologies, large amounts of high-dimensional gene expression data are being generated, which poses significant computational challenges. A first step towards addressing this challenge is the use of clustering techniques, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. A ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2014