The Experts below are selected from a list of 28407 Experts worldwide ranked by ideXlab platform
Jinfeng Wang - One of the best experts on this subject based on the ideXlab platform.
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spatial Data Discretization methods for geocomputation
International Journal of Applied Earth Observation and Geoinformation, 2014Co-Authors: Yong Ge, Jinfeng WangAbstract:Abstract Geocomputation provides solutions to complex geographic problems. Continuous and discrete spatial Data are involved in the geocomputational process; however, geocomputational methods for discrete spatial Data cannot be directly applied to continuous or mixed spatial Data. Therefore, Discretization methods for continuous or mixed spatial Data are involved in the process. Since spatial Data has spatial features, such as association, heterogeneity and spatial structure, these features cannot be handled by traditional Discretization methods. Therefore, this work develops feature-based spatial Data Discretization methods that achieve optimal Discretization results for spatial Data using spatial information implicit in those features. Two Discretization methods considering the features of spatial Data are presented. One is an unsupervised method considering autocorrelation of spatial Data and the other is a supervised method considering spatial heterogeneity. Discretization processes of the two methods are exemplified using neural tube defects (NTD) for Heshun County in Shanxi Province, China. Effectiveness is also assessed.
Pramod P Wangikar - One of the best experts on this subject based on the ideXlab platform.
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Data Discretization for dynamic bayesian network based modeling of genetic networks
International Conference on Neural Information Processing, 2012Co-Authors: Nguyen Xuan Vinh, Madhu Chetty, Ross L Coppel, Pramod P WangikarAbstract:Dynamic Bayesian networks (DBN) are widely applied in Systems biology for modeling various biological networks, including gene regulatory networks and metabolic networks. The application of DBN models often requires Data Discretization. Although various Discretization techniques exist, currently there is no consensus on which approach is most suitable. Popular Discretization strategies within the bioinformatics community, such as interval and quantile Discretization, are likely not optimal. In this paper, we propose a novel approach for Data Discretization for mutual information based learning of DBN. In this approach, the Data are discretized so that the mutual information between parent and child nodes is maximized, subject to a suitable penalty put on the complexity of the Discretization. A dynamic programming approach is used to find the optimal Discretization threshold for each individual variable. Our approach iteratively learns both the network and the Discretization scheme until a locally optimal solution is reached. Tests on real genetic networks confirm the effectiveness of the proposed method.
Yanming Zhu - One of the best experts on this subject based on the ideXlab platform.
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Comparative study of Discretization methods of microarray Data for inferring transcriptional regulatory networks
BMC bioinformatics, 2010Co-Authors: Lili Liu, Xi Bai, Hua Cai, Dianjing Guo, Yanming ZhuAbstract:Microarray Data Discretization is a basic preprocess for many algorithms of gene regulatory network inference. Some common Discretization methods in informatics are used to discretize microarray Data. Selection of the Discretization method is often arbitrary and no systematic comparison of different Discretization has been conducted, in the context of gene regulatory network inference from time series gene expression Data. In this study, we propose a new Discretization method "bikmeans", and compare its performance with four other widely-used Discretization methods using different Datasets, modeling algorithms and number of intervals. Sensitivities, specificities and total accuracies were calculated and statistical analysis was carried out. Bikmeans method always gave high total accuracies. Our results indicate that proper Discretization methods can consistently improve gene regulatory network inference independent of network modeling algorithms and Datasets. Our new method, bikmeans, resulted in significant better total accuracies than other methods.
Ramana V Davuluri - One of the best experts on this subject based on the ideXlab platform.
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evaluation of Data Discretization methods to derive platform independent isoform expression signatures for multi class tumor subtyping
BMC Genomics, 2015Co-Authors: Segun Jung, Ramana V DavuluriAbstract:Many supervised learning algorithms have been applied in deriving gene signatures for patient stratification from gene expression Data. However, transferring the multi-gene signatures from one analytical platform to another without loss of classification accuracy is a major challenge. Here, we compared three unsupervised Data Discretization methods--Equal-width binning, Equal-frequency binning, and k-means clustering--in accurately classifying the four known subtypes of glioblastoma multiforme (GBM) when the classification algorithms were trained on the isoform-level gene expression profiles from exon-array platform and tested on the corresponding profiles from RNA-seq Data. We applied an integrated machine learning framework that involves three sequential steps; feature selection, Data Discretization, and classification. For models trained and tested on exon-array Data, the addition of Data Discretization step led to robust and accurate predictive models with fewer number of variables in the final models. For models trained on exon-array Data and tested on RNA-seq Data, the addition of Data Discretization step dramatically improved the classification accuracies with Equal-frequency binning showing the highest improvement with more than 90% accuracies for all the models with features chosen by Random Forest based feature selection. Overall, SVM classifier coupled with Equal-frequency binning achieved the best accuracy (> 95%). Without Data Discretization, however, only 73.6% accuracy was achieved at most. The classification algorithms, trained and tested on Data from the same platform, yielded similar accuracies in predicting the four GBM subgroups. However, when dealing with cross-platform Data, from exon-array to RNA-seq, the classifiers yielded stable models with highest classification accuracies on Data transformed by Equal frequency binning. The approach presented here is generally applicable to other cancer types for classification and identification of molecular subgroups by integrating Data across different gene expression platforms.
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Comparison of Data Discretization methods for cross platform transfer of gene-expression based tumor subtyping classifier
2014 IEEE 4th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS), 2014Co-Authors: Segun Jung, Yingtao Bi, Ramana V DavuluriAbstract:Molecular stratification of tumors is essential for developing personalized therapies. While patient stratification strategies have been successful, computational methods to accurately translate and integrate gene signatures across different high-throughput platforms (e.g., microarray, RNA-seq) are currently lacking. We performed comparative evaluation of different Data Discretization and feature selection methods combined with state-of-the-art machine learning algorithms to derive platform-independent and accurate multi-gene signatures for classification of the four known subtypes of glioblastoma. Our results show that integrative application of feature selection and Data Discretization is crucial for successful platform transition and higher prediction accuracy of the derived molecular classifiers.
Tailiang Chen - One of the best experts on this subject based on the ideXlab platform.
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a hybrid model based on adaptive network based fuzzy inference system to forecast taiwan stock market
Expert Systems With Applications, 2011Co-Authors: Liangying Wei, Tailiang ChenAbstract:Abstract In recent years, many academy researchers have proposed several forecasting models based on technical analysis to predict models such as Engle (1982) and Cheng, Chen, and Wei (2010) . After reviewing the literature, two major drawbacks are found in past models: (1) the forecasting models based on artificial intelligence algorithms (AI), such as neural networks (NN) and genetic algorithms (GAs), produce complex and unintelligible rules; and (2) statistic forecasting models, such as time series, require some basic assumptions for variables and build forecasting models based on mathematic equations, which are not easily understandable by stock investors. In order to refine these drawbacks of past models, this paper has proposed a model, based on adaptive-network-based fuzzy inference system which uses multi-technical indicators, to predict stock price trends. Three refined processes have proposed in the hybrid model for forecasting: (1) select essential technical indicators from popular indicators by a correlation matrix; (2) use the subtractive clustering method to partition technical indicator value into linguistic values based on an Data Discretization method; (3) employ a fuzzy inference system (FIS) to extract rules of linguistic terms from the Dataset of the technical indicators, and optimize the FIS parameters based on an adaptive network to produce forecasts. A six-year period of the TAIEX is employed as experimental Database to evaluate the proposed model with a performance indicator, root mean squared error (RMSE). The experimental results have shown that the proposed model is superior to two listing models (Chen’s and Yu’s models).
