文档介绍：Linear Modeling of works from Experimental Data
. van Someren, . Wessels and . Reinders, In Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology, 2000.
Summarized by Kyu-Baek Hwang
Abstract
Topic
Modeling regulatory interactions between genes
Linear works
Gene expression data
The dimensionality problem (contribution of this paper)
The number of genes >> the number of measured time points many solutions that fit the training data
Prototypical genes (by clustering) biological works are sparse and redundant.
Experiments
An artificial dataset
S. cerevisiae yeast cell-cycle dataset
Exploitation of DNA Microarray Datasets
DNA microarray simultaneous measurements on the expression levels of thousands of genes
Infer functionality of genes based on this new massive datasets.
Clustering and pattern recognition techniques (NNs and SVMs)
The regulatory interactions between genes
works, works, works, works, and differential equations
Data sparseness problem inherent in the analysis of microarray data as few parameters as possible
works
The basic linear model
수식 1
where xj(t) represents the activity level of gene j at time point t, ri,j represents how strongly gene i controls gene j and N is the total number of genes under consideration.
Prototypical genes hierarchical clustering
Tackling the dimensionality problem
Input and output sharing among genes involved within a gene family or pathway
Genes are estimated to interact with four to eight other genes.
The Modeling Approach
Preprocessing Step I: Thresholding
Eliminate insignificant signals (genes).
Due to experimental noises
Gene expression levels in different cultures under similar conditions
Vary up to ratio of two
Gene expression levels in different cultures under different conditions
Vary up to ratio of two to five
Genes with profiles that remain below an absolute value of two do not participate in regulation
Reduce the dimensionality problem.