By Pey-Chang Kent Lin
ISBN-10: 1461494281
ISBN-13: 9781461494287
This e-book brings to undergo a physique of good judgment synthesis strategies, with the intention to give a contribution to the research and keep an eye on of Boolean Networks (BN) for modeling genetic ailments reminiscent of melanoma. The authors supply a number of VLSI common sense concepts to version the genetic illness habit as a BN, with robust implicit enumeration ideas. insurance additionally contains options from VLSI trying out to manage a defective BN, reworking its habit to a fit BN, in all likelihood supporting in efforts to discover the simplest applicants for remedy of genetic illnesses.
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Extra info for Logic Synthesis for Genetic Diseases: Modeling Disease Behavior Using Boolean Networks
Sample text
IEEE/ACM Trans. Comput. Biol. Bioinformatics, 5(2), 262–274 (2008) 9. se/. Accessed 6 April 2010 10. R: Gene prediction using multinomial probit regression with Bayesian gene selection. EURASIP J. App. Signal. Process. 115–124 (2004) 11. : Inferring gene regulatory networks from time series data using the minimum description length principle. Bioinformatics, 17, 2129–2135 (2006) 12. : Generating Boolean networks with a prescribed attractor structure. Bioinformatics, 21(21), 4021–4025 (2005) 13.
One issue with this approach is that logical conflicts can arise between different update functions obtained in this manner from the pathway information. The paper attempts to resolve these conflicts by perturbing the pathway information, possibly leading to a vastly different network. Another problem arises from the use of an explicit K-map based computation approach. One characteristic of assigning logic to the update function given a predictor set, is that in a predictor set, the gene connections or “wiring” is fixed.
For gene i, all of its associated predictor variables are written in a single clause ci1 = (v1i + · · · + vji ) In our example, for g1 , c11 = (v11 + v21 ). For g2 and g3 , we have c21 = (v12 + v22 + v32 + v42 ) and c31 = (v13 + v23 + v33 ) respectively. To satisfy any ci1 clause, at least one predictor in the clause must be chosen. To ensure that at least one predictor is chosen for all genes, we write the conjunction of all ci1 clauses as S1 (Eq. 1). 1) 2. The second constraint (S2 ) specifies that for each gene, exactly one predictor is chosen.
Logic Synthesis for Genetic Diseases: Modeling Disease Behavior Using Boolean Networks by Pey-Chang Kent Lin
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