Reconstructing gene regulatory networks from knock-out data using Gaussian Noise Model and Pearson Correlation Coefficient

Salleh, Faridah Hani Mohamed; Arif, Shereena M.; Zainudin, Suhaila; Firdaus-Raih, Mohd

doi:10.1016/j.compbiolchem.2015.04.012

Cited by 50 publications

(9 citation statements)

References 48 publications

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“…The findings obtained by Salleh et al [ 32 ] pertaining to the topics discussed in this study proved that most of the false positives had been due to cascade errors. Meanwhile, researches conducted by [ 4 , 33 ] were strongly affected by cascade motifs, where these methods systematically predicted false positive interactions [ 34 ].…”

Section: Problem Statementssupporting

confidence: 68%

Multiple Linear Regression for Reconstruction of Gene Regulatory Networks in Solving Cascade Error Problems

Salleh

Zainudin

Arif

2017

Advances in Bioinformatics

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Gene regulatory network (GRN) reconstruction is the process of identifying regulatory gene interactions from experimental data through computational analysis. One of the main reasons for the reduced performance of previous GRN methods had been inaccurate prediction of cascade motifs. Cascade error is defined as the wrong prediction of cascade motifs, where an indirect interaction is misinterpreted as a direct interaction. Despite the active research on various GRN prediction methods, the discussion on specific methods to solve problems related to cascade errors is still lacking. In fact, the experiments conducted by the past studies were not specifically geared towards proving the ability of GRN prediction methods in avoiding the occurrences of cascade errors. Hence, this research aims to propose Multiple Linear Regression (MLR) to infer GRN from gene expression data and to avoid wrongly inferring of an indirect interaction (A → B → C) as a direct interaction (A → C). Since the number of observations of the real experiment datasets was far less than the number of predictors, some predictors were eliminated by extracting the random subnetworks from global interaction networks via an established extraction method. In addition, the experiment was extended to assess the effectiveness of MLR in dealing with cascade error by using a novel experimental procedure that had been proposed in this work. The experiment revealed that the number of cascade errors had been very minimal. Apart from that, the Belsley collinearity test proved that multicollinearity did affect the datasets used in this experiment greatly. All the tested subnetworks obtained satisfactory results, with AUROC values above 0.5.

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Section: Problem Statementssupporting

confidence: 68%

Multiple Linear Regression for Reconstruction of Gene Regulatory Networks in Solving Cascade Error Problems

Salleh

Zainudin

Arif

2017

Advances in Bioinformatics

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“…The simple linear regression is one of the most frequently used methods in establishing the correlation between traffic crashes and various possible causing variables, such as: the volume of traffic flow [2]; light conditions [21]; season of the year [20]; spatiotemporal parameters [26]. As a way to establish the intensity of the linear dependence between the two analyzed variables (in our case traffic crashes and importation of second-hand cars), Pearson correlation analysis was used [27,28] (2):…”

Section: Statistical Approachmentioning

confidence: 99%

The Safety of Transnational Imported Second-Hand Cars: A Case Study on Vehicle-to-Vehicle Crashes in Romania

2017

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Certain features of imported second-hand cars (e.g., age, degree of wear and tear, technical design) can increase their likelihood for traffic crashes. Three official datasets which cover an eight year period (2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015) are used to test the connection between importation of second-hand cars and different types of traffic crashes. The traffic crashes database was provided by the Traffic Department of the General Inspectorate of Romanian Police (GIRPTD). The car registration database was provided by Driving-License and Vehicles-Registration Direction (DLVRD). Right-hand driving (RHD) cars database was provided by the Romanian Automotive Registry (RAR). A spatio-temporal visualization of data was performed using Geographic Information System (GIS) while for the statistical analysis we use regression models and Pearson-Correlation-coefficient. The analysis suggests that a significant part of the variation in the volume of traffic accidents can be explained by the volume of imported second-hand cars at the county level. Moreover, an even stronger direct relation exists between the number of imported second-hand cars and Severe Traffic Accidents but also in the case of RHD imported second-hand cars. The overall impact of imported second-hand cars on the traffic safety in Romania is significant but small in comparison to other types of car registration. Study results belong to the category of empirical evidence production which can improve the quality of existing traffic regulations focused both on organizing and ensuring traffic safety, and on the policy of sustainable transport infrastructure development.

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“…is the squared of the standard deviation. r k varies from -1 to 1 and, in general, the higher the correlation coefficient, the stronger the relationship is [45]. Dancey and Reidy [46] state that if r k ranges from 0.7 to 0.9, the strength of correlation is high, and quite enough to determine the size of the correlation.…”

Section: Development Of Econometric Modelsmentioning

confidence: 99%

Optimal Design of a Carbon Dioxide Separation Process with Market Uncertainty and Waste Reduction

2019

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The aim of this work is to optimize the conceptual design of an amine-based carbon dioxide (CO2) separation process for Enhanced Oil Recovery (EOR). A systematic approach is applied to predict the economic profitability of the system while reducing the environmental impacts. Firstly, we model the process with UniSim and determine the governing degrees of freedom (DoF) through a sensitivity analysis. Then, we proceed with the formulation of the economic problem, where the employment of econometric models allows us to predict the highest dynamic economic potential (DEP). In the second part, we apply the Waste Reduction (WAR) algorithm to quantify the environmental risks of the studied process. This method is based on the minimization of the potential environmental indicator (PEI) by using the generalization of the Waste Reduction algorithm. Results show that the CO2 separation plant is promising in terms of economic revenues. However, the PEI value indicates that the higher the profitability, the larger the environmental risk. The optimal value of the DEP corresponds to 0.0274 kmol/h and 60 °C, with a plant capacity according to the mole flow rate of the produced acid gas. In addition, the highest environmental risk is observed at the upper bounds of the DoF.

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Reconstructing gene regulatory networks from knock-out data using Gaussian Noise Model and Pearson Correlation Coefficient

Cited by 50 publications

References 48 publications

Multiple Linear Regression for Reconstruction of Gene Regulatory Networks in Solving Cascade Error Problems

Multiple Linear Regression for Reconstruction of Gene Regulatory Networks in Solving Cascade Error Problems

The Safety of Transnational Imported Second-Hand Cars: A Case Study on Vehicle-to-Vehicle Crashes in Romania

Optimal Design of a Carbon Dioxide Separation Process with Market Uncertainty and Waste Reduction

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