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Finding optimal models for small gene networks

S Ott, S Imoto, S Miyano
2004 Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing  
Finding gene networks from microarray data has been one focus of research in recent years.  ...  We present a method that finds optimal Bayesian networks of considerable size and show first results of the application to yeast data.  ...  Acknowledgements The authors would like to thank Michiel de Hoon for discussions of the manuscript, and Hideo Bannai for advice on implementational issues.  ... 
pmid:14992533 fatcat:4oslt6wxubg25cjh2jywve2tta

FINDING OPTIMAL MODELS FOR SMALL GENE NETWORKS

S. OTT, S. IMOTO, S. MIYANO
2003 Biocomputing 2004  
Acknowledgements The authors would like to thank Michiel de Hoon for discussions of the manuscript, and Hideo Bannai for advice on implementational issues.  ...  In Bayesian networks, the behaviour of the gene network is modeled as a joint probability distribution for all genes. This allows a very general modeling of gene interactions.  ...  then what we need to do in order to find the optimal network is to find the optimal permutation π, which yields the global minimum.  ... 
doi:10.1142/9789812704856_0052 fatcat:yhumh6dzpfbnxbf5rebtqareda

Feedback Memetic Algorithms for Modeling Gene Regulatory Networks

C. Spieth, F. Streichert, J. Supper, N. Speer, A. Zell
2005 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology  
In this paper we address the problem of finding gene regulatory networks from experimental DNA microarray data.  ...  We propose a memetic method, which separates the overall inference problem into two subproblems to find the correct network: first, the search for a valid topology, and secondly, the optimization of the  ...  Small-sized Networks Due to the fact that GRNs in nature are sparse systems [26] , we randomly created 4 small-sized ((N ∈ [2, 5] )) regulatory networks for each model type with a cardinality of 0 ≤  ... 
doi:10.1109/cibcb.2005.1594899 fatcat:6ir6giqe65gcjjkef2bj7j2iee

Optimizing Topology and Parameters of Gene Regulatory Network Models from Time-Series Experiments [chapter]

Christian Spieth, Felix Streichert, Nora Speer, Andreas Zell
2004 Lecture Notes in Computer Science  
In this paper we address the problem of finding gene regulatory networks from experimental DNA microarray data.  ...  We propose a new method, which evolves the topology as well as the parameters of the mathematical model to find the correct network.  ...  The ES optimizes the parameters of the mathematical model used for representation of the regulatory network. Fitness.  ... 
doi:10.1007/978-3-540-24854-5_46 fatcat:swc5bk3zzbax7p53m7lsukskti

A Survey on Recurrent Neural Network Based Modelling of Gene Regulatory Network

Sudip Mandal
2016 MOJ Proteomics & Bioinformatics  
It is observed that finding out the most suitable and efficient optimization techniques for the accurate inference of small artificial, large artificial, Dream4 Network, and real world GRNs with less computational  ...  The correct inference of gene regulatory networks (GRN) remains as a fascinating task for researchers to understand the detailed process of complex biological regulations and functions.  ...  Their result had shown very good accuracy in finding all true regulation and dynamics for both small and large network.  ... 
doi:10.15406/mojpb.2016.04.00125 fatcat:htwayj2gvvchjboobxnbqi5tye

Recurrent Neural Network Based Modeling of Gene Regulatory Network Using Bat Algorithm [article]

Sudip Mandal, Goutam Saha, Rajat K. Pal
2017 arXiv   pre-print
In this paper, Bat Algorithm (BA) is applied to optimize the model parameters of RNN model of Gene Regulatory Network (GRN).  ...  The model is also validated in presence of different level of random noise for the small artificial network and that proved its ability to infer the correct inferences in presence of noise like real world  ...  The result had shown very good accuracy in finding all true regulation and dynamics for both small and large network.  ... 
arXiv:1509.03221v2 fatcat:mtb563z3onabffvqzs4mvhs3xi

Is the optimal intervention policy UC superior to the suboptimal policy MFPT over inferred probabilistic Boolean network models?

X.Z. Zan, W.B. Liu, M.X. Hu, L.Z. Shen
2016 Genetics and Molecular Research  
Additionally, using a relatively complex model (gene number N is more than 1) is beneficial for the intervention process, especially for the sensitive UC policy.  ...  Theoretically, in a complete network, the optimal policy performs better than the suboptimal policy.  ...  INTRODUCTION From a translational perspective, modeling gene regulatory networks (GRNs) provides a mathematical basis for system-based optimal therapeutic strategies.  ... 
doi:10.4238/gmr15049334 pmid:28002610 fatcat:zmv3lap6pfg35phu3yxf25tngq

Multi-objective Model Optimization for Inferring Gene Regulatory Networks [chapter]

Christian Spieth, Felix Streichert, Nora Speer, Andreas Zell
2005 Lecture Notes in Computer Science  
In this paper we address the problem of finding gene regulatory networks from experimental DNA microarray data.  ...  The investigation of gene regulatory networks has become one of the major topics in Systems Biology.  ...  Although the standard single-objective algorithms, which were used in some cases for parameter optimization for the parameterized models, tend to find completely connected networks and thus biologically  ... 
doi:10.1007/978-3-540-31880-4_42 fatcat:72q7ztzpkveujnki7uctidlyfa

Efficient and Practical Algorithms for Deducing the History of Recombination in Populations [chapter]

Dan Gusfield
2006 Lecture Notes in Computer Science  
For small-size data, we can also (provably) sample uniformly from the set of optimal solutions, and can also determine the phase of genotypic data so as to minimize the number of recombinations needed  ...  In small-size data we can guarantee that an optimal solution will been found by running an exponentialtime algorithm to completion.  ...  For small-size data, we can also (provably) sample uniformly from the set of optimal solutions, and can also determine the phase of genotypic data so as to minimize the number of recombinations needed  ... 
doi:10.1007/11758525_83 fatcat:ro3iwmif2vefvmr2okgx4xhvpq

Study on the Use of Evolutionary Techniques for Inference in Gene Regulatory Networks [chapter]

Leon Palafox, Nasimul Noman, Hitoshi Iba
2013 Proceedings in Information and Communications Technology  
We will use the SOS network for E.coli to do the comparison to finally show how they fare against other techniques in the area of Gene Regulatory Network (GRN) inference.  ...  Inference in Gene Regulatory Networks remains an important problem in Molecular Biology. Many models have been proposed to model the relationships within genes in a DNA chain.  ...  Vohradský [9] , proposed using RNNs to model gene networks. Furthermore, Xu et al [3] successfully applied Particle Swarm Optimization (PSO) to find the parameters of the RNN.  ... 
doi:10.1007/978-4-431-54394-7_7 fatcat:w4wd7zqzk5bw3msekmv5lq3bla

Review on Deep Learning Technique for Detecting Non-Small Cell Lung Cancer

Bhargav Manjunath Hegde, Mahesh Ganapati Hegde, Chetan Channappagol, Dayananda P
2019 Zenodo  
In this work, we tried to enhance the analysis gene for non-small cell lung cancer and detect the non-small cell lung using human genome sequence.  ...  We can find two different lung cancers; there are small cell lung cancer and non-small cell lung cancer. Here, we are only concentrated about the non-small cell lung cancer or (NSCLC).  ...  This optimal model is compared with testing data using convolutional neural network. Using these optimal models, we can compare or predict many new datasets which are given by the user.  ... 
doi:10.5281/zenodo.3377126 fatcat:yhtt6xcajngxnjhgptbn3wffhy

Enumeration of Likely Gene Networks and Network Motif Extraction for Large Gene Networks

Sascha Ott, Satoru Miyano
2003 Genome Informatics Series  
Recently, an algorithm for the optimal estimation of small gene networks within the Bayesian network framework was found [3] .  ...  If we can find a partial network that is common to most of the likely network models, we can expect this part to be the most reliable part.  ... 
doi:10.11234/gi1990.14.354 fatcat:tckojx452vcr7hwkc57qvx7agi

Modelling gene functional linkages using yeast microarray data

Tie Wang, Guoliang Xue, Jeffrey W. Touchman
2007 International Journal of Bioinformatics Research and Applications  
Understanding how genes are functionally related requires efficient algorithms to model networks from expression data.  ...  We report a heuristic search algorithm called Two-Level Simulated Annealing (TLSA) that is more likely to find the global optimal network structure compared to conventional simulated annealing and other  ...  Edward Suh for assistance with high-performance computing on the ASU/TGen computer cluster.  ... 
doi:10.1504/ijbra.2007.013601 pmid:18048187 fatcat:zoh2bhtjrnex3ffpt44akyiijm

Iteratively Inferring Gene Regulatory Networks with Virtual Knockout Experiments [chapter]

Christian Spieth, Felix Streichert, Nora Speer, Andreas Zell
2004 Lecture Notes in Computer Science  
In this paper we address the problem of finding gene regulatory networks from experimental DNA microarray data.  ...  Further on, the problem often is multi-modal and therefore appropriate optimization strategies become necessary.  ...  Due to the small number of data the system is highly under-determined and therefore finding the biologically correct model is very difficult.  ... 
doi:10.1007/978-3-540-24653-4_11 fatcat:tjlhmovcarbsda74be2ixrxbki

Intervention in Gene Regulatory Networks via a Stationary Mean-First-Passage-Time Control Policy

G. Vahedi, B. Faryabi, J.-F. Chamberland, A. Datta, E.R. Dougherty
2008 IEEE Transactions on Biomedical Engineering  
and dynamic programming used to find optimal control policies.  ...  A prime objective of modeling genetic regulatory networks is the identification of potential targets for therapeutic intervention.  ...  This procedure is prone to modeling errors and suffers from problems of computational complexity for both network inference and finding the optimal control solutions.  ... 
doi:10.1109/tbme.2008.925677 pmid:18838357 fatcat:zp3sywsjefbwfci7ir45s3uba4
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