- Title
- Large scale modeling of genetic networks using gene knockout data
- Creator
- Youseph, Ahammed; Chetty, Madhu; Karmakar, Gour
- Date
- 2018
- Type
- Text; Conference proceedings
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/164658
- Identifier
- vital:13120
- Identifier
-
https://doi.org/10.1145/3167918.3167950
- Identifier
- ISBN:9781450354363 (ISBN)
- Abstract
- Gene regulatory network (GRN) represents a set of genes and their regulatory interactions. The inference of the regulatory interactions between genes is usually carried out as an optimization problem using an appropriate mathematical model and the time-series gene expression data. Among the various models proposed for GRN inference, our recently proposed Michaelis-Menten kinetics based ODE model provides a good trade-off between the computational complexity and biological relevance. This model, like other known GRN models, also uses an evolutionary algorithm for parameter estimation. Since the search space for large networks is huge, leading to a low accuracy of inference, it is important to reduce the search region for improved performance of the optimization algorithm. In this paper, we propose a classification method using gene knockout data to eliminate a large infeasible region from the optimization search area. We also propose a method for partial inference of regulations when all the regulators of a given regulated gene are unregulated genes. The proposed method is evaluated by reconstructing in silico networks of large sizes. © 2018 ACM.
- Publisher
- Association for Computing Machinery
- Relation
- 2018 Australasian Computer Science Week Multiconference, ACSW 2018; Brisbane, Australia; 29th January-2nd February 2018; published in ACM International Conference Proceedings Series
- Rights
- Copyright © 2018 by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept., ACM, Inc., fax +1 (212) 869-0481, or permissions@acm.org. For other copying of articles that carry a code at the bottom of the first or last page, copying is permitted provided that the per-copy fee indicated in the code is paid through the Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923, +1-978-750-8400, +1-978-750-4470 (fax).
- Rights
- This metadata is freely available under a CCO license
- Subject
- Classification; Gene Knockout; Gene Regulatory Network (GRN); In-degree; Michaelis-Menten Kinetics; Out-degree
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