Automated Local Linear Embedding with an application to microarray data

نویسندگان

  • Elisa Grilli
  • Daniela Cocchi
  • Angela Montanari
  • Ernst Wit
  • Silvano Bordignon
  • Carla Rampichini
  • Maurizio Vichi
چکیده

Permission is herewith granted to Università degli studi di Bologna to circulate and to have copied for non-commercial purposes, at its discretion, the above title upon the request of individuals or institutions. iii Acknowledgements The essential work reported in this thesis was carried out during my visiting periods at the University of Glasgow in the Autumn 2005 and at the Lancaster University in May 2006. I would like to express my gratitude to Ernst Wit for being my supervisor and for his precious support and his helpful advices during my work. I would like to give my sincere and warm thanks to Angela Montanari, Cinzia Viroli and Marilena Pillati for their supervision and guidance of my PhD thesis and for their helpful criticism on this work. It has been a pleasure to exchange ideas with the other PhD student at the University of Glasgow and Bologna. Finally, I would like to give many thanks to my husband, my parents and my friends for their patient and for supporting me during the three years of my PhD period.

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

Link Prediction using Network Embedding based on Global Similarity

Background: The link prediction issue is one of the most widely used problems in complex network analysis. Link prediction requires knowing the background of previous link connections and combining them with available information. The link prediction local approaches with node structure objectives are fast in case of speed but are not accurate enough. On the other hand, the global link predicti...

متن کامل

Feature dimension reduction for microarray data analysis using locally linear embedding

Cancer classification is one major application of microarray data analysis. Due to the ultra high dimensionality nature of microarray data, data dimension reduction has drawn special attention for such type of data analysis. The currently available data dimension reduction methods are either supervised, where data need to be labeled, or computational complex. In this paper, we proposed to use a...

متن کامل

Gene expression data classification using locally linear discriminant embedding

Gene expression data collected from DNA microarray are characterized by a large amount of variables (genes), but with only a small amount of observations (experiments). In this paper, manifold learning method is proposed to map the gene expression data to a low dimensional space, and then explore the intrinsic structure of the features so as to classify the microarray data more accurately. The ...

متن کامل

Steganalysis Method for LSB Replacement Based on Local Gradient of Image Histogram

In this paper we present a new accurate steganalysis method for the LSBreplacement steganography. The suggested method is based on the changes that occur in thehistogram of an image after the embedding of data. Every pair of neighboring bins of ahistogram are either inter-related or unrelated depending on whether embedding of a bit ofdata in the image could affect both bins or not. We show that...

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 2006