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Function approximation approach to the inference of reduced NGnet models of genetic networks

Shuhei Kimura, Katsuki Sonoda, Soichiro Yamane, Hideki Maeda, Koki Matsumura, Mariko Hatakeyama
2008 BMC Bioinformatics  
On the basis of this problem definition, we propose in this study a new method to infer reduced NGnet models of genetic networks.  ...  There does not seem to be a perfect model for the inference of genetic networks yet.  ...  This work is partially supported by the Ministry of Education, Culture, Sports, Science and Technology of Japan under Grant-in-Aid for Young Scientists (B) No. 18710166.  ... 
doi:10.1186/1471-2105-9-23 pmid:18194576 pmcid:PMC2258286 fatcat:qhcunrcugvawxj5k2hwk3yctiy

Inferring Gene Regulatory Network with Recurrent Neural Network and Extended Artificial Bee Colony Algorithm

Tausif Al Hossain, Mohammad Shoyaib, Saifuddin Md Tareeq
2018 Plant Tissue Culture and Biotechnology  
There are a number of algorithms available in the literature which use recurrent neural network for model building together with differential evolution, particle swarm optimization or genetic algorithm  ...  Gene regulatory network is the network of genes interacting with each other performing as functional circuitry inside a cell.  ...  Kimura S, Katsuki S, Soichiro Y, Hideki M, Koki M and Mariko H (2008) Function approximation approach to the inference of reduced ngnet models of genetic networks. BMC Bioinformatics 9: 111-124.  ... 
doi:10.3329/ptcb.v28i2.39682 fatcat:hebit3cr5rcpfhaekjd6y7vyvi

Incorporating time-delays in S-System model for reverse engineering genetic networks

Ahsan Chowdhury, Madhu Chetty, Nguyen Vinh
2013 BMC Bioinformatics  
Results: In this paper, we propose a novel Time-Delayed S-System (TDSS) model which uses a set of delay differential equations to represent the system dynamics.  ...  Moreover, we also propose a new criterion for model evaluation exploiting the sparse and scale-free nature of GRNs to effectively narrow down the search space, which not only reduces the computation time  ...  To infer a GRN of N genes using the S-System model, 2N(N+1) parameters must be estimated.  ... 
doi:10.1186/1471-2105-14-196 pmid:23777625 pmcid:PMC3839642 fatcat:olbvu5aj3jbstpx633djnlerpy

Reverse Engineering Genetic Networks Using Nonlinear Saturation Kinetics

Ahammed Sherief Kizhakkethil Youseph, Madhu Chetty, Gour Karmakar
2019 Biosystems (Amsterdam. Print)  
A gene regulatory network (GRN) represents a set of genes along with their regulatory interactions. Cellular behavior is driven by genetic level interactions.  ...  In this paper, we develop a complete framework for a novel model for GRN inference using MM kinetics. A set of coupled equations is first proposed for modeling GRNs.  ...  of real genetic networks.  ... 
doi:10.1016/j.biosystems.2019.103977 pmid:31185246 fatcat:z7mmg7k37jcxbilnk2gd3ei33e

Geometry and Topology Optimization of Switched Reluctance Machines: A Review

Mohamed Abdalmagid, Ehab Sayed, Mohamed Bakr, Ali Emadi
2022 IEEE Access  
On the other hand, the material distribution in a particular design space within the machine domain may be optimized using topology optimization.  ...  As optimizing the machine geometry and material distribution at the design phase is of substantial significance, this work offers a comprehensive literature review on the current state of the art and the  ...  A magnetic equivalent circuit model of the machine was used to reduce the optimization time.  ... 
doi:10.1109/access.2022.3140440 fatcat:76eqna2mrnezboalh57f4it3qq

Recent developments in parameter estimation and structure identification of biochemical and genomic systems

I-Chun Chou, Eberhard O. Voit
2009 Mathematical Biosciences  
equations, smoothing overly noisy data, estimating slopes of time series, reducing the complexity of the inference task, and constraining the parameter search space.  ...  structure and regulation of the underlying biological networks.  ...  This work was supported in part by a National Heart, Lung and Blood Institute Proteomics Initiative  ... 
doi:10.1016/j.mbs.2009.03.002 pmid:19327372 pmcid:PMC2693292 fatcat:rqnk3f34w5g2xl4qcwicbnwtnm