Model selection in the reconstruction of regulatory networks from time-series data

Abstract Motivation Gene regulatory networks (GRNs) capture the regulatory interactions between genes, resulting from the fundamental biological process of transcription and translation. In some cases, the topology of GRNs is not known, and has to be inferred from gene expression data. Most of the existing GRNs reconstruction algorithms are either applied to time-series data or steady-state data. Although time-series data include more information about the system dynamics, steady-state data imply stability of the underlying regulatory networks. Results In this article, we propose a method for inferring GRNs from time-series and steady-state data jointly. We make use of a non-linear ordinary differential equations framework to model dynamic gene regulation and an importance measurement strategy to infer all putative regulatory links efficiently. The proposed method is evaluated extensively on the artificial DREAM4 dataset and two real gene expression datasets of yeast and Escherichia coli. Based on public benchmark datasets, the proposed method outperforms other popular inference algorithms in terms of overall score. By comparing the performance on the datasets with different scales, the results show that our method still keeps good robustness and accuracy at a low computational complexity. Availability and implementation The proposed method is written in the Python language, and is available at: https://github.com/lab319/GRNs_nonlinear_ODEs Supplementary information Supplementary data are available at Bioinformatics online.

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Model Selection Criteria for Model-Based Clustering of Categorical Time Series Data: A Monte Carlo Study

Studies in Classification, Data Analysis, and Knowledge Organization - Advances in Data Analysis ◽

10.1007/978-3-540-70981-7_3 ◽

2007 ◽

pp. 23-30 ◽

Cited By ~ 4

Author(s):

José G. Dias

Keyword(s):

Time Series ◽

Monte Carlo ◽

Model Selection ◽

Selection Criteria ◽

Time Series Data ◽

Monte Carlo Study ◽

Series Data ◽

Model Selection Criteria ◽

Model Based Clustering ◽

Categorical Time Series

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Reconstruction of extended Petri nets from time series data and its application to signal transduction and to gene regulatory networks

BMC Systems Biology ◽

10.1186/1752-0509-5-113 ◽

2011 ◽

Vol 5 (1) ◽

pp. 113 ◽

Cited By ~ 19

Author(s):

Markus Durzinsky ◽

Annegret Wagler ◽

Wolfgang Marwan

Keyword(s):

Signal Transduction ◽

Time Series ◽

Petri Nets ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Time Series Data ◽

Series Data ◽

Extended Petri Nets ◽

Gene Regulatory

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PERAMALAN JUMLAH PENUMPANG PESAWAT TERBANG DI PINTU KEDATANGAN BANDAR UDARA INTERNASIONAL PATTIMURA AMBON DENGAN MENGGUNAKAN METODE ARIMA BOX-JENKINS

BAREKENG JURNAL ILMU MATEMATIKA DAN TERAPAN ◽

10.30598/barekengvol13iss3pp135-144ar883 ◽

2019 ◽

Vol 13 (3) ◽

pp. 135-144

Author(s):

Sasmita Hayoto ◽

Yopi Andry Lesnussa ◽

Henry W. M. Patty ◽

Ronald John Djami

Keyword(s):

Time Series ◽

Model Selection ◽

Time Series Data ◽

Moving Average ◽

Arima Model ◽

Series Data ◽

Autoregressive Integrated Moving Average ◽

Significant Parameter ◽

International Airport ◽

Forecast Time

The Autoregressive Integrated Moving Average (ARIMA) model is often used to forecast time series data. In the era of globalization, rapidly progressing times, one of them in the field of transportation. The aircraft is one of the transportation that the residents can use to support their activities, both in business and tourism. The objective of the research is to know the forecasting of the number of passengers of airplanes at the arrival gate of Pattimura Ambon International Airport using ARIMA Box-Jenkins method. The best model selection is ARIMA (0, 1, 3) because it has significant parameter value and MSE value is smaller.

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Bristlecone: An F# library for model-fitting and model-selection of ecological time-series data

10.31219/osf.io/zt2wn ◽

2019 ◽

Author(s):

Andrew C Martin

Keyword(s):

Time Series ◽

Model Selection ◽

Tree Rings ◽

Time Series Data ◽

Sediment Cores ◽

Model Fitting ◽

Series Data ◽

Ecological Problems ◽

Biodiversity And Ecosystem Function

Environmental archives such as sediment cores and tree rings provide important insights on the timing and rates of change in biodiversity and ecosystem function over the long-term. Such datasets are often analysed using empirical methods, which limits their ability to address ecological questions that seek to understand underlying ecological mechanisms and processes. Top down modelling approaches – where data is confronted with simple ecological models – can be used to infer the presence, form, and strength of mechanisms of interest. To aid adoption of time-series mechanistic modelling for long-term ecology, we created a F# library, Bristlecone, that can be used to apply this approach using a Model- Fitting and Model-Selection workflow. Our objective with Bristlecone was to create a library that could be used to efficiency and effectively conduct a full MFMS analysis for long-term ecological problems. We incorporated techniques to address specific challenges with environmental archives, including uneven time steps from age-depth models (for sediment cores), and allometry and seasonality (for tree rings). We include an example analysis to demonstrate functionality of Bristlecone. Our solution presents a straightforward, repeatable, and highly parallel method for conducting inference for long- term ecological problems.

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On the Autoregressive Time Series Model Using Real and Complex Analysis

Forecasting ◽

10.3390/forecast3040044 ◽

2021 ◽

Vol 3 (4) ◽

pp. 716-728

Author(s):

Torsten Ullrich

Keyword(s):

Time Series ◽

Model Selection ◽

Closed Form ◽

Autoregressive Model ◽

Complex Analysis ◽

Time Series Data ◽

Selection Process ◽

Global Structure ◽

Series Data ◽

Time Step

The autoregressive model is a tool used in time series analysis to describe and model time series data. Its main structure is a linear equation using the previous values to compute the next time step; i.e., the short time relationship is the core component of the autoregressive model. Therefore, short-term effects can be modeled in an easy way, but the global structure of the model is not obvious. However, this global structure is a crucial aid in the model selection process in data analysis. If the global properties are not reflected in the data, a corresponding model is not compatible. This helpful knowledge avoids unsuccessful modeling attempts. This article analyzes the global structure of the autoregressive model through the derivation of a closed form. In detail, the closed form of an autoregressive model consists of the basis functions of a fundamental system of an ordinary differential equation with constant coefficients; i.e., it consists of a combination of polynomial factors with sinusoidal, cosinusoidal, and exponential functions. This new insight supports the model selection process.

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Improved Fuzzy Cognitive Maps for Gene Regulatory Networks Inference Based on Time Series Data

10.21203/rs.3.rs-770358/v1 ◽

2021 ◽

Author(s):

Marzieh Emadi ◽

Farsad Zamani Boroujeni ◽

jamshid Pirgazi

Keyword(s):

Time Series ◽

Compressed Sensing ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Time Series Data ◽

Cognitive Maps ◽

Boolean Networks ◽

Fuzzy Cognitive Maps ◽

Series Data ◽

Gene Regulatory

Abstract Recently with the advancement of high-throughput sequencing, gene regulatory network inference has turned into an interesting subject in bioinformatics and system biology. But there are many challenges in the field such as noisy data, uncertainty, time-series data with numerous gene numbers and low data, time complexity and so on. In recent years, many research works have been conducted to tackle these challenges, resulting in different methods in gene regulatory networks inference. A number of models have been used in modeling of the gene regulatory networks including Boolean networks, Bayesian networks, Markov model, relational networks, state space model, differential equations model, artificial neural networks and so on. In this paper, the fuzzy cognitive maps are used to model gene regulatory networks because of their dynamic nature and learning capabilities for handling non-linearity and inherent uncertainty. Fuzzy cognitive maps belong to the family of recurrent networks and are well-suited for gene regulatory networks. In this research study, the Kalman filtered compressed sensing is used to infer the fuzzy cognitive map for the gene regulatory networks. This approach, using the advantages of compressed sensing and Kalman filters, allows robustness to noise and learning of sparse gene regulatory networks from data with high gene number and low samples. In the proposed method, stream data and previous knowledge can be used in the inference process. Furthermore, compressed sensing finds likely edges and Kalman filter estimates their weights. The proposed approach uses a novel method to decrease the noise of data. The proposed method is compared to CSFCM, LASSOFCM, KFRegular, ABC, RCGA, ICLA, and CMI2NI. The results show that the proposed approach is superior to the other approaches in fuzzy cognitive maps learning. This behavior is related to the stability against noise and offers a proper balance between data error and network structure.

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Boolean dynamics of genetic regulatory networks inferred from microarray time series data

Bioinformatics ◽

10.1093/bioinformatics/btm021 ◽

2007 ◽

Vol 23 (7) ◽

pp. 866-874 ◽

Cited By ~ 100

Author(s):

Shawn Martin ◽

Zhaoduo Zhang ◽

Anthony Martino ◽

Jean-Loup Faulon

Keyword(s):

Time Series ◽

Regulatory Networks ◽

Time Series Data ◽

Genetic Regulatory Networks ◽

Series Data ◽

Boolean Dynamics

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Windowed Granger causal inference strategy improves discovery of gene regulatory networks

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1710936115 ◽

2018 ◽

Vol 115 (9) ◽

pp. 2252-2257 ◽

Cited By ~ 15

Author(s):

Justin D. Finkle ◽

Jia J. Wu ◽

Neda Bagheri

Keyword(s):

Time Series ◽

Network Structure ◽

Time Delays ◽

Regulatory Networks ◽

Network Inference ◽

Time Series Data ◽

Series Data ◽

Directed Network ◽

Rapid Characterization

Accurate inference of regulatory networks from experimental data facilitates the rapid characterization and understanding of biological systems. High-throughput technologies can provide a wealth of time-series data to better interrogate the complex regulatory dynamics inherent to organisms, but many network inference strategies do not effectively use temporal information. We address this limitation by introducing Sliding Window Inference for Network Generation (SWING), a generalized framework that incorporates multivariate Granger causality to infer network structure from time-series data. SWING moves beyond existing Granger methods by generating windowed models that simultaneously evaluate multiple upstream regulators at several potential time delays. We demonstrate that SWING elucidates network structure with greater accuracy in both in silico and experimentally validated in vitro systems. We estimate the apparent time delays present in each system and demonstrate that SWING infers time-delayed, gene–gene interactions that are distinct from baseline methods. By providing a temporal framework to infer the underlying directed network topology, SWING generates testable hypotheses for gene–gene influences.

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