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A Novel Genetic Programming Approach for Inferring Gene Regulatory Network

M. N.VamsiThalatam, Allam Appa Rao
2015 International Journal of Computer Applications  
The core objective of this work is to study and model the different regulation mechanisms involved in the living organisms and propose accurate evolutionary algorithms for inferring Gene Regulatory Networks  ...  The evolutionary computing techniques like genetic programming plays vital role to effectively resolve several crucial problems related to genomics.  ...  METHODOLOGY In this present work, an attempt is made to inferring the gene regulatory network by proposing a Novel Genetic Programming (NGP) on time series gene expression data.  ... 
doi:10.5120/21147-4213 fatcat:fezewnhfjzd7bmn2irrz2b366u

MapReduce Algorithms for Inferring Gene Regulatory Networks from Time-Series Microarray Data Using an Information-Theoretic Approach

Yasser Abduallah, Turki Turki, Kevin Byron, Zongxuan Du, Miguel Cervantes-Cervantes, Jason T. L. Wang
2017 BioMed Research International  
These algorithms employ an information-theoretic approach to infer GRNs using time-series microarray data.  ...  Gene regulation is a series of processes that control gene expression and its extent.  ...  Zhong for useful conversations concerning gene network inference. BioMed Research International  ... 
doi:10.1155/2017/6261802 pmid:28243601 pmcid:PMC5294223 fatcat:wo35wdfpibczvhu6gu5we6lmua

MapReduce Algorithms for Inferring Gene Regulatory Networks from Time-Series Microarray Data Using an Information-Theoretic Approach [article]

Yasser Abduallah, Turki Turki, Kevin Byron, Zongxuan Du, Miguel Cervantes-Cervantes, Jason T. L. Wang
2017 arXiv   pre-print
These algorithms employ an information-theoretic approach to infer GRNs using time-series microarray data.  ...  Gene regulation is a series of processes that control gene expression and its extent.  ...  Zhong for useful conversations concerning gene network inference.  ... 
arXiv:1704.06548v1 fatcat:mstlhmgbaveu3asbusw7htzmlq

Identification of Gene Regulatory Networks by Integrating Genetic Programming with Particle Filtering

Baoshan Ma, Xiangtian Jiao, Fanyu Meng, Fengping Xu, Yao Geng, Rubin Gao, Wei Wang, Yeqing Sun
2019 IEEE Access  
However, measured gene expression data usually contain uncertain noise, and inference of gene regulatory network model under non-Gaussian noise is a challenging issue which needs to be addressed.  ...  In this study, a joint algorithm integrating genetic programming and particle filter is presented to infer the ordinary differential equations model of gene regulatory network.  ...  CONCLUSION The paper proposes a joint algorithm to infer the gene regulatory network model from the time-series data.  ... 
doi:10.1109/access.2019.2935216 fatcat:yiib47xgv5hxra3mwlhlsrswwu

Evolutionary algorithms in genetic regulatory networks model [article]

Khalid Raza, Rafat Parveen
2012 arXiv   pre-print
Genetic Regulatory Networks (GRNs) plays a vital role in the understanding of complex biological processes.  ...  Understanding the interactions between genes gives rise to develop better method for drug discovery and diagnosis of the disease since many diseases are characterized by abnormal behaviour of the genes  ...  [40] proposed an intelligent two-stage evolutionary algorithm (iTEA) to infer the Ssystem models of N-gene genetic networks from time-series data of gene expression.  ... 
arXiv:1205.1986v1 fatcat:sndsoua6cfbftpru4iv5jkxm6q

Using Additive Expression Programming for Gene Regulatory Network Inference

Bin Yang
2015 International Journal of Hybrid Information Technology  
In this paper, a non-linear differential equation model is used for gene regulatory network reconstruction and time-series prediction.  ...  A synthetic data and two real time-series expression datasets are used to test the validity of our proposed model and hybrid approach.  ...  Gene expression data is usually with a few time points, so our method has ability to infer gene regulatory network with expression data.  ... 
doi:10.14257/ijhit.2015.8.7.21 fatcat:splbdzvmcbheveejfujhezret4

Neural Gene Network Constructor: A Neural Based Model for Reconstructing Gene Regulatory Network [article]

Zhang Zhang, Lifei Wang, Shuo Wang, Ruyi Tao, Jingshu Xiao, Muyun Mou, Jun Cai, Jiang Zhang
2019 bioRxiv   pre-print
Here, we present a deep learning model 'Neural Gene Network Constructor' (NGNC), for inferring gene regulatory network and reconstructing the gene dynamics simultaneously from time series gene expression  ...  Gene regulatory networks could be reconstructed by experimental methods or from gene expression data.  ...  METHODS Model The aim of the gene regulatory networks inference task is to reconstruct the regulatory network from time series gene expression data which could be measured as RNAseq counts.  ... 
doi:10.1101/842369 fatcat:vvfy7zq53jayhguzjihqizi5lm

A novel procedure for statistical inference and verification of gene regulatory subnetwork

Haijun Gong, Jakob Klinger, Kevin Damazyn, Xiangrui Li, Shiyang Huang
2015 BMC Bioinformatics  
Results: In this work, we present a novel procedure to automatically infer and verify gene regulatory networks from time series expression data.  ...  from time series expression data than previous studies.  ...  Acknowledgements HG would like to thank Dr. Hartemink for answering some questions related to Banjo.  ... 
doi:10.1186/1471-2105-16-s7-s7 pmid:25952938 pmcid:PMC4423581 fatcat:bnigq6zcsbgnnixoqjb3qk7bpy

High-performance cartesian genetic programming on GPU for the inference of gene regulatory networks using scRNA-seq time-series data

Luciana Nascimento Santana Prachedes, José Eduardo Henriques da Silva, Heder Soares Bernardino, Itamar Leite de Oliveira
2022 Proceedings of the Genetic and Evolutionary Computation Conference Companion  
In this paper, we propose a strategy to infer GRN models from gene expression data using Cartesian Genetic Programming (CGP) and high-performance computing for reducing its processing time.  ...  The inference of Gene Regulatory Networks (GRNs) is an important topic with biotechnological and health applications, as comprehending patterns of gene interactions can lead to findings regarding living  ...  Thus, we proposed here a high-performance CGP approach to infer gene regulatory networks from scRNA-seq time series data using GPU to reduce the computational time.  ... 
doi:10.1145/3520304.3534032 fatcat:wyfi73ayjnfe3c655xihr2qtiy

DDGni: Dynamic delay gene-network inference from high-temporal data using gapped local alignment

Hari Krishna Yalamanchili, Bin Yan, Mulin Jun Li, Jing Qin, Zhongying Zhao, Francis Y.L. Chin, Junwen Wang
2013 Computer applications in the biosciences : CABIOS  
Results: Here, we propose DDGni (dynamic delay gene-network inference), a novel gene-network-inference algorithm based on the gapped local alignment of gene-expression profiles.  ...  Synthesis of a fully functional transcriptional factor/protein from DNA involves series of reactions, leading to a delay in gene regulation.  ...  Inferring regulatory networks from such data will help us understand various regulatory mechanisms involved in tissue differentiation and embryonic development.  ... 
doi:10.1093/bioinformatics/btt692 pmid:24285602 fatcat:f4ubjazcrvh5fpitoxynbbsr6e

Sparse and Stable Reconstruction of Genetic Regulatory Networks Using Time Series Gene Expression Data

Roozbeh Manshaei, Matthew Kyan
2007 Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics - BCB'13  
In this application, we are presented with a set of time series gene expression data, from which an unknown topology describing the regulatory interactions between genes must be inferred.  ...  To this end, we formulate an algorithm for reconstructing a genetic regulatory network to explain time series data obtained from genetic experiments.  ...  To test the capability of our algorithm, we used the algorithm 1 for 18+24 times to extract gene regulatory network (GRN) structures from inferred 's, and evaluate against GRNs extracted from KEGG database  ... 
doi:10.1145/2506583.2512380 dblp:conf/bcb/ManshaeiK13 fatcat:h27zefvgezc5rjwfin5f47bhxa

Comparing Genetic Programming and Evolution Strategies on Inferring Gene Regulatory Networks [chapter]

Felix Streichert, Hannes Planatscher, Christian Spieth, Holger Ulmer, Andreas Zell
2004 Lecture Notes in Computer Science  
In recent years several strategies for inferring gene regulatory networks from observed time series data of gene expression have been suggested based on Evolutionary Algorithms.  ...  We show that single problem instances are not sufficient to prove the effectiveness of a given strategy and that the Genetic Programming approach is less prone to varying instances than the Evolution Strategy  ...  This problem of inferring the real gene regulatory networks from time series data has recently become one of the major topics in bioinformatics.  ... 
doi:10.1007/978-3-540-24854-5_47 fatcat:mj7rqkelhjh4bclkfndbs5pqbi

Inferring Regulatory Programs Governing Region Specificity of Neuroepithelial Stem Cells during Early Hindbrain and Spinal Cord Development

Deborah Chasman, Nisha Iyer, Alireza Fotuhi Siahpirani, Maria Estevez Silva, Ethan Lippmann, Brian McIntosh, Mitchell D. Probasco, Peng Jiang, Ron Stewart, James A. Thomson, Randolph S. Ashton, Sushmita Roy
2019 Cell Systems  
We introduce Escarole, a probabilistic clustering algorithm for non-stationary time series, and combine it with prior-based regulatory network inference to identify genes that are regulated dynamically  ...  regulatory networks governing these processes are incompletely understood.  ...  Analysis of time-series data to identify dynamic regulatory networks requires us to address two main questions: (1) what genes exhibit dynamic expression and (2) how these genes are regulated.  ... 
doi:10.1016/j.cels.2019.05.012 pmid:31302154 pmcid:PMC6715525 fatcat:qfwmdoremfdkvewfxjtniztyx4

A comparative study of the time-series data for inference of gene regulatory networks using B-Spline

Haixin Wang, James E. Glover, Lijun Qian
2010 2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology  
The effect of the different sizes of observed time-series data on gene regulatory networks inference is analyzed.  ...  In this paper, the quantitative analysis of timeseries gene expression data on inference of gene regulatory networks is performed.  ...  The effects of the time-series data to infer gene regulatory networks are analyzed in synthetic model and microarray experimental expression data. II.  ... 
doi:10.1109/cibcb.2010.5510596 dblp:conf/cibcb/WangGQ10 fatcat:63chkboxifaddbacz3rfruruqm

Qualitative reasoning of dynamic gene regulatory interactions from gene expression data

Yu Chen, Byungkyu Park, Kyungsook Han
2010 BMC Genomics  
Results: We developed a new qualitative method for representing dynamic gene regulatory relations and algorithms for identifying dynamic gene regulations from the time-series gene expression data using  ...  The approach and the program developed in our study would be useful for identifying dynamic gene regulatory interactions from the large amount of gene expression data available and for analyzing the interactions  ...  There are a few programs that can infer gene regulatory interactions from time-series gene expression data [18, 19] .  ... 
doi:10.1186/1471-2164-11-s4-s14 pmid:21143797 pmcid:PMC3005929 fatcat:7vl23re52jh3jkgniwqpr76ybm
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