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Discovering statistically significant biclusters in gene expression data
TLDR
A new method to detect significant biclusters in large expression datasets is proposed and is able to detect and relate finer tissue types than was previously possible in cancer data and outperforms the biclustering algorithm of Cheng and Church (2000). Expand
The age of responsibilization: on market-embedded morality
Abstract This article explores emerging discursive formations concerning the relationship of business and morality. It suggests that contemporary tendencies to economize public domains and methods ofExpand
Network-based prediction of protein function
TLDR
The current computational approaches for theFunctional annotation of proteins are described, including direct methods, which propagate functional information through the network, and module‐assisted methods, who infer functional modules within the network and use those for the annotation task. Expand
Quantification of protein half-lives in the budding yeast proteome
TLDR
Analysis of a simple dynamic protein production model reveals a remarkable correlation between transcriptional regulation and protein half-life within some groups of coregulated genes, suggesting that cells coordinate these two processes to achieve uniform effects on protein abundances. Expand
Modelling and analysis of gene regulatory networks
Gene regulatory networks have an important role in every process of life, including cell differentiation, metabolism, the cell cycle and signal transduction. By understanding the dynamics of theseExpand
EXPANDER – an integrative program suite for microarray data analysis
BackgroundGene expression microarrays are a prominent experimental tool in functional genomics which has opened the opportunity for gaining global, systems-level understanding of transcriptionalExpand
Clustering Gene Expression Patterns
TLDR
This paper defines an appropriate stochastic error model on the input, and proves that under the conditions of the model, the algorithm recovers the cluster structure with high probability, and presents a practical heuristic based on the same algorithmic ideas. Expand
CLICK and EXPANDER: a system for clustering and visualizing gene expression data
TLDR
A novel clustering algorithm, called CLICK, is presented, which utilizes graph-theoretic and statistical techniques to identify tight groups (kernels) of highly similar elements, which are likely to belong to the same true cluster. Expand
Genome-wide in silico identification of transcriptional regulators controlling the cell cycle in human cells.
TLDR
Using human genomic sequences, models for binding sites of known transcription factors, and gene expression data, it is demonstrated that the reverse engineering approach, which infers regulatory mechanisms from gene expression patterns, can reveal transcriptional networks in human cells. Expand
A clustering algorithm based on graph connectivity
TLDR
A novel algorithm for cluster analysis that is based on graph theoretic techniques and produces a solution with some provably good properties and performs well on simulated and real data. Expand
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