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Recognition of alternatively spliced cassette exons based on a hybrid model

BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS 2016年第3期471卷 368-372页

作者： Zhang, Xiaokang Peng, Qinke Li, Liang Li, Xintong Xi An Jiao Tong Univ Syst Engn Inst Xian 710049 Peoples R China

Alternative splicing (AS) is an important mechanism of gene regulation that contributes to protein diversity. It is of great significance to recognize different kinds of AS accurately so as to understand the mechanism of gene regulation. Many in silica methods have been applied to detecting AS with vast features, but the result is far from satisfactory. In this paper, we used the features proven to be useful in recognizing AS in previous literature and proposed a hybrid method combining gene expression programming (GEP) and Random Forests (RF) to classify the constitutive exons and cassette exons which is the most common AS phenomenon. GEP will firstly make prediction to the samples of strong signal, and the other samples of weak signal will be distinguished with a more complex classifier based on RF. The experiment result indicates that this method can highly improve the recognition level in this issue. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Cassette exons Constitutive exons Hybrid model gene expression programming Random forests

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Empirical models for longitudinal dispersion coefficient in natural streams

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JOURNAL OF HYDROLOGY 2019年 575卷 1359-1361页

作者： Gharabaghi, Bahram Sattar, Ahmed M. A. Univ Guelph Sch Engn Guelph ON NIG 2 W1 Canada Cairo Univ Fac Engn Dept Irrigat & Hydraul Giza Egypt

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RETRACTED: Experimental study and computer-aided prediction of percentage of water absorption of geopolymers produced by waste fly ash and rice husk bark ash (Retracted article. See vol. 191, 2023)

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INTERNATIONAL JOURNAL OF MINERAL PROCESSING 2012年 110卷 74-81页

作者： Nazari, Ali Islamic Azad Univ Dept Mat Sci & Engn Saveh Branch Saveh Iran

In the present work, percentage of water absorption of geopolymers made from seeded fly ash and rice husk bark ash has been predicted by gene expression programming. Different specimens, made from a mixture of fly ash and rice husk bark ash in fine and coarse forms together with alkali activator made of water glass and NaOH solution, were subjected to permeability tests at 7 and 28 days of curing. The curing regime was different: one set of the specimens was cured at room temperature until reaching 7 and 28 days and the other sets were oven cured for 36 h at the range of 40-90 degrees C and then cured at room temperature until 7 and 28 days. A model based on gene expression programming for predicting the percentage of water absorption of the specimens has been presented. To build the model, training and testing using experimental results from 120 specimens were conducted. The data used as inputs in gene expression programming models are arranged in a format of six parameters that cover the percentage of fine fly ash in the ash mixture, the percentage of coarse fly ash in the ash mixture, the percentage of fine rice husk bark ash in the ash mixture, the percentage of coarse rice husk bark ash in the ash mixture, the temperature of curing and the time of water curing. According to these input parameters, in the gene expression programming models, the percentage of water absorption of each specimen was predicted. The training and testing results in gene expression programming models have shown a strong potential for predicting the percentage of water absorption of the geopolymer specimens. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Geopolymer Percentage of water absorption Fly ash Rice husk bark ash gene expression programming

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A New Short-term Load Forecasting Model of Smart Distribution Grid

A New Short-term Load Forecasting Model of Smart Distributio...

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18th International Conference on Management Science and Engineering

作者： Li Yan-mei Wang Jing-min N China Elect Power Univ Sch Business & Adm Baoding 071003 Peoples R China

ISBN: (纸本)9781457718861

Applying the improved gene expression programming arithmetic to optimization plan, the convergence rate and precision of the model can be improved, which can be used to load forecasting. Preprocessing the load sample data, and applying the flexible skills of the improved gene expression programming arithmetic,the paper forecasts the whole point load of future short-term to see the same point load sequence of different work-day as sample. Through a case analysis, the improved gene expression programming arithmetic has been proved to have more efficiency and faster convergence rate than optimization methods.

关键词： gene expression programming smart grid short-term load forecasting

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Comparison of two data-driven approaches for daily river flow forecasting

Comparison of two data-driven approaches for daily river flo...

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MSSANZ 19th Biennial Congress on Modelling and Simulation (MODSIM)

作者： Fernando, Achela K. Shamseldin, Asaad Y. Abrahart, Robert J. Unitec Inst Technol Dept Civil Engn Auckland New Zealand

ISBN: (纸本)9780987214317

Ongoing research on the use of data-driven techniques for rainfall-runoff modelling and forecasting has stimulated our desire to compare the effectiveness of transparent and black-box type models. Previous studies have shown that models based on Artificial Neural Networks (ANN) provide accurate black-box type forecasters: whilst gene expression programming (GEP: Ferreira, 2001;2006) provides transparent models in which the relationship between the independent and the dependant variables is explicitly determined. The study presented in this paper aims to advance our understanding of both approaches and their relative merits as applied to river flow forecasting. The study has been carried out to test the effectiveness of two forecasting models: a GEP evolved equation and a model that uses a combination of ANN and genetic Algorithms (GA). The two approaches are applied to daily rainfall and river flow in the Blue Nile catchment over a five year period. geneXproTools 4.0, a powerful soft computing software package, is utilised to perform symbolic regression operations by means of GEP and in so doing develop a rainfall-runoff forecasting model based on antecedent rainfall and river flow inputs. A transparent model with independent variables of antecedent rainfall and flow to forecast river discharge could be achieved. The ANN model is developed with the assistance of a GA: the latter being used in the selection of the ANN inputs from a pre-determined set of external inputs. The rainfall and flow data for the first four years was used to develop the model and the final year of data was used for testing. The paper describes the methods used for the selection of inputs, model development and then compares and contrasts the two approaches and their suitability for river flow forecasting. The results of the study show that the GEP model is a useful transparent model that is superior to the ANN-GA model in its performance for riverflow forecasting.

关键词： gene expression programming Artificial Neural Network genetic Algorithms Rainfall-runoff Model River Flow Forecasting

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Real-Time Hybrid Flow Shop Scheduling Approach in Smart Manufacturing Environment

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Complex System Modeling and Simulation 2021年第4期1卷 335-350页

作者： Xiuli Wu Zheng Cao Shaomin Wu Department of Logistics the School of Mechanical EngineeringUniversity of Science and Technology BeijingBeijing 100083China Kent Business School University of KentKentCT27FSUK.

Smart manufacturing in the“Industry 4.0”strategy promotes the deep integration of manufacturing and information technologies,which makes the manufacturing system a ubiquitous ***,the real-time scheduling of such a manufacturing system is a challenge faced by many decision *** deal with this challenge,this study focuses on the real-time hybrid flow shop scheduling problem(HFSP).First,the characteristic of the hybrid flow shop in a smart manufacturing environment is analyzed,and its scheduling problem is ***,a real-time scheduling approach for the HFSP is *** core module is to employ gene expression programming to construct a new and efficient scheduling rule according to the real-time status in the hybrid flow *** the scheduling rule,the priorities of the waiting job are calculated,and the job with the highest priority will be scheduled at this decision time point.A group of experiments are performed to prove the performance of the proposed *** numerical experiments show that the real-time scheduling approach outperforms other single-scheduling rules and the back-propagation neural network method in optimizing most objectives for different size ***,the contribution of this study is the proposal of a real-time scheduling approach,which is an effective approach for real-time hybrid flow shop scheduling in a smart manufacturing environment.

关键词： smart manufacturing real-time scheduling hybrid flow shop scheduling problem gene expression programming

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Earthquake Magnitude Prediction Model based on the GEP Algorithm and Markov Chain

Earthquake Magnitude Prediction Model based on the GEP Algor...

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2nd International Conference on Information Technology and Management Innovation (ICITMI 2013)

作者： Chen, Qinghua Liu, Zhi Wuhan Univ Technol Dept Informat Engn Wuhan 430070 Peoples R China Shandong Management Univ Dept Informat Engn Jinan Peoples R China

ISBN: (纸本)9783037858646

This paper introduces a method which uses the gene expression programming algorithm to conduct multivariate nonlinear function modeling, which is applied in the earthquake magnitude prediction. The experiment shows that the prediction accuracy of the GEP is significantly higher than that of the neural network model. Finally, by using the non-delayed effects and stability of the earthquake magnitude prediction data, the state-transition matrix is obtained through the Markov chain, and the state interval and corresponding probability of the GEP model prediction are obtained. In this way, the credibility of the prediction results has been increased.

关键词： gene expression programming function mining Markov chain earthquake magnitude prediction

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gene expression programing in long term water demand forecasts using wavelet decomposition 18th

Gene expression programing in long term water demand forecas...

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18th International Conference on Water Distribution System Analysis (WDSA)

作者： Yousefi, Peyman Shabani, Sina Mohammadi, Hadi Naser, Gholamreza Univ British Columbia Okanagan Sch Engn Vancouver BC Canada

Increasing draught seasons and lack of access to potable water reserves have been the major risks threatening water authorities and governments over the recent years. Therefore, long term water forecasts are receiving much more attention nowadays. Unlike the conventional projection of historical water demand, researchers have tried to implement sophisticated mathematical models to predict demand of water. gene expression programming (GEP), as a relatively new forecasting technique, remains to be explored in this endeavor. The main purpose of this research was to assess the performance of GEP models using wavelet decomposition with 2 transfer functions (db2 and haar) and 3 levels. Results of this study showed GEP models can be highly sensitive to wavelet decomposition if all combinations of proper lag times are used as inputs feeding these models. (C) 2016 The Authors. Published by Elsevier Ltd.

关键词： Water Demand Forecasting gene expression programming Wavelet Decomposition Lag Time

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Distributed GEP Function Mining on Consistency Merger in Grid Environment 3

Distributed GEP Function Mining on Consistency Merger in Gri...

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3rd International Conference on Computer Science and Network Technology (ICCSNT)

作者： Deng Song Zhang Tao Lin Wei-min Ma Yuan-yuan China Elect Power Res Inst Nanjing Branch Nanjing 211106 Jiangsu Peoples R China

ISBN: (纸本)9781479905614

Distributed function mining is an important field of distributed data mining. In order to solve local model merger of function mining in grid environments, this paper presents consistency merger of local function model (CMLFM). On the basis of CMLFM, distributed GEP function mining on consistency merger (DGEPFM-CM) is proposed which combines with grid service. Simulated experiments show that the time-consuming of DGEPFM-CM is less than traditional GEP. With the increasing of grid nodes, the global fitting error of DGEPFM-CM apparently decreases.

关键词： distributed function mining gene expression programming grid service consistency merger

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Mining Contrast Inequalities in Numeric Dataset

Mining Contrast Inequalities in Numeric Dataset

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11th International Conference on Web-Age Information Management (WAIM 2010)

作者： Duan, Lei Zuo, Jie Zhang, Tianqing Peng, Jing Gong, Jie Sichuan Univ Sch Comp Sci Chengdu 610065 Peoples R China Chengdu Municipal Publ Secur Bur Sci & Tech Dept Chengdu 610017 Peoples R China

ISBN: (纸本)9783642142451

Finding relational expressions which exist frequently in one class of data while not in the other class of data is an interesting work. In this paper, a relational expression of this kind is defined as a contrast inequality. gene expression programming (GEP) is powerful to discover relations from data and express them in mathematical level. Hence, it is desirable to apply GEP to such mining task. The main contributions of this paper include: (I) introducing the concept of contrast inequality mining, (2) designing a two-genome chromosome structure to guarantee that each individual in GEP is a valid inequality, (3) proposing a new genetic mutation to improve the efficiency of evolving contrast inequalities, (4) presenting a GEP-based method to discover contrast inequalities, (5) giving an extensive performance study on real-world datasets. The experimental results show that the proposed methods are effective. Contrast inequalities with high discriminative power are discovered from the real-world datasets. Some potential works on contrast inequality mining are discussed.

关键词： Data Mining Contrast Mining gene expression programming

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