Identifying Statistical Dependence in Genomic Sequences via Mutual Information Estimates
نویسندگان
چکیده
منابع مشابه
Identifying Statistical Dependence in Genomic Sequences via Mutual Information Estimates
Questions of understanding and quantifying the representation and amount of information in organisms have become a central part of biological research, as they potentially hold the key to fundamental advances. In this paper, we demonstrate the use of information-theoretic tools for the task of identifying segments of biomolecules (DNA or RNA) that are statistically correlated. We develop a prec...
متن کاملMeasuring Statistical Dependence via the Mutual Information Dimension
We propose to measure statistical dependence between two random variables by the mutual information dimension (MID), and present a scalable parameter-free estimation method for this task. Supported by sound dimension theory, our method gives an effective solution to the problem of detecting interesting relationships of variables in massive data, which is nowadays a heavily studied topic in many...
متن کاملMeasuring Dependence via Mutual Information
Considerable research has been done on measuring dependence between random variables. The correlation coefficient [10] is the most widely studied linear measure of dependence. However, the limitation of linearity limits its application. The informational coefficient of correlation [17] is defined in terms of mutual information. It also has some deficiencies, such as it is only normalized to con...
متن کاملStatistical Dependence: Copula Functions and Mutual Information Based Measures
Accurately and adequately modelling and analyzing relationships in real random phenomena involving several variables are prominent areas in statistical data analysis. Applications of such models are crucial and lead to severe economic and financial implications in human society. Since the beginning of developments in Statistical methodology as the formal scientific discipline, correlation based...
متن کاملDependence Maximizing Temporal Alignment via Squared-Loss Mutual Information
The goal of temporal alignment is to establish time correspondence between two sequences, which has many applications in a variety of areas such as speech processing, bioinformatics, computer vision, and computer graphics. In this paper, we propose a novel temporal alignment method called least-squares dynamic time warping (LSDTW). LSDTW finds an alignment that maximizes statistical dependency ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: EURASIP Journal on Bioinformatics and Systems Biology
سال: 2007
ISSN: 1687-4145
DOI: 10.1155/2007/14741