Abstract
This chapter contains the original research results on the monograph. We study the problem of reverse-engineering context-specific, genome-wide interaction networks from expression data. Two existing classes of methods, namely those based on mutual information and those based on Bayesian networks, are described first. Then a new algorithm, based on the so-called phi-mixing coefficient between random variables, is introduced. Unlike mutual information, the phi-mixing coefficient provides a directionally sensitive measure of the dependence between two random variables. The algorithm based on this new approach produces a gene interaction network in the form of a directed, strongly connected graph. The approach is validated on ChIP-seq data around the transcription factor ASCL1 in a lung cancer network.
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Notes
- 1.
Hereafter we shall avoid the unwieldy phrase ‘genes or gene products’ and shall instead say just ‘genes’. However, proteins are also encompassed in the phrase ‘gene products’, and protein interaction networks (PINs) are therefore subsumed by the phrase GINs introduced a little later.
- 2.
Note that the data for a single cell line could itself be a compendium of data obtained through multiple experiments carried out at different times.
- 3.
Note that language used here is not identical to that in [14] but is mathematically equivalent.
- 4.
Note that since the graph is undirected, it is not necessary to specify the direction.
- 5.
Medterms [17] defines a proto-oncogene as “A normal gene which, when altered by mutation, becomes an oncogene that can contribute to cancer,” and an oncogene as “A gene that played a normal role in the cell as a proto-oncogene and that has been altered by mutation and now may contribute to the growth of a tumor.”
- 6.
- 7.
It is interesting to note that in a preprint version of [22], their method is claimed to take only 1.6 h.
- 8.
Recall that the ‘central dogma’ of biology, as enunciated by Francis Crick [34] states that DNA is converted to RNA (transcription) which is then converted to protein(s) (translation).
- 9.
In the interests of brevity, these are referred to, somewhat inaccurately, as ‘ChIP-seq genes’.
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Vidyasagar, M. (2012). Inferring Gene Interaction Networks. In: Computational Cancer Biology. SpringerBriefs in Electrical and Computer Engineering(). Springer, London. https://doi.org/10.1007/978-1-4471-4751-0_3
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