BioCode: A data-driven procedure to learn the growth of biological networks

Name: BioCode: A data-driven procedure to learn the growth of biological networks
Author: Sefer, Emre

İsim	BioCode: A data-driven procedure to learn the growth of biological networks
Yazar	Sefer, Emre
Basım Tarihi:	2022-11
Basım Yeri	- IEEE
Konu	Algorithms, Biological networks, Graph mining, Network growth models
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1545-5963
Kayıt Numarası	47a1a30f-afe4-4273-88e7-bf9ba8d1accd
Lokasyon	Computer Science
Tarih	2022-11
Örnek Metin	Probabilistic biological network growth models have been utilized for many tasks including but not limited to capturing mechanism and dynamics of biological growth activities, null model representation, capturing anomalies, etc. Well-known examples of these probabilistic models are Kronecker model, preferential attachment model, and duplication-based model. However, we should frequently keep developing new models to better fit and explain the observed network features while new networks are being observed. Additionally, it is difficult to develop a growth model each time we study a new network. In this paper, we propose BioCode, a framework to automatically discover novel biological growth models matching user-specified graph attributes in directed and undirected biological graphs. BioCode designs a basic set of instructions which are common enough to model a number of well-known biological graph growth models. We combine such instruction-wise representation with a genetic algorithm based optimization procedure to encode models for various biological networks. We mainly evaluate the performance of BioCode in discovering models for biological collaboration networks, gene regulatory networks, and protein interaction networks which features such as assortativity, clustering coefficient, degree distribution closely match with the true ones in the corresponding real biological networks. As shown by the tests on the simulated graphs, the variance of the distributions of biological networks generated by BioCode is similar to the known models' variance for these biological network types.
DOI	10.1109/TCBB.2022.3165092
Cilt	19

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BioCode: A data-driven procedure to learn the growth of biological networks

Yazar Sefer, Emre

Basım Tarihi 2022-11

Basım Yeri - IEEE

Konu Algorithms, Biological networks, Graph mining, Network growth models

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1545-5963

Kayıt Numarası 47a1a30f-afe4-4273-88e7-bf9ba8d1accd

Lokasyon Computer Science

Tarih 2022-11

Örnek Metin Probabilistic biological network growth models have been utilized for many tasks including but not limited to capturing mechanism and dynamics of biological growth activities, null model representation, capturing anomalies, etc. Well-known examples of these probabilistic models are Kronecker model, preferential attachment model, and duplication-based model. However, we should frequently keep developing new models to better fit and explain the observed network features while new networks are being observed. Additionally, it is difficult to develop a growth model each time we study a new network. In this paper, we propose BioCode, a framework to automatically discover novel biological growth models matching user-specified graph attributes in directed and undirected biological graphs. BioCode designs a basic set of instructions which are common enough to model a number of well-known biological graph growth models. We combine such instruction-wise representation with a genetic algorithm based optimization procedure to encode models for various biological networks. We mainly evaluate the performance of BioCode in discovering models for biological collaboration networks, gene regulatory networks, and protein interaction networks which features such as assortativity, clustering coefficient, degree distribution closely match with the true ones in the corresponding real biological networks. As shown by the tests on the simulated graphs, the variance of the distributions of biological networks generated by BioCode is similar to the known models' variance for these biological network types.

DOI 10.1109/TCBB.2022.3165092

Cilt 19