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Iterative Bayesian Model Averaging: a method for the application of survival analysis to high-dimensional microarray data

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BMC BIOINFORMATICS 2009年第1期10卷 1-17页

作者： Annest, Amalia Bumgarner, Roger E. Raftery, Adrian E. Yeung, Ka Yee Univ Washington Dept Microbiol Seattle WA 98195 USA Univ Washington Inst Technol Comp & Software Syst Tacoma WA 98402 USA Univ Washington Dept Stat Seattle WA 98195 USA

Background: Microarray technology is increasingly used to identify potential biomarkers for cancer prognostics and diagnostics. Previously, we have developed the iterative Bayesian Model Averaging (BMA) algorithm for use in classification. Here, we extend the iterative BMA algorithm for application to survival analysis on high-dimensional microarray data. The main goal in applying survival analysis to microarray data is to determine a highly predictive model of patients' time to event (such as death, relapse, or metastasis) using a small number of selected genes. Our multivariate procedure combines the effectiveness of multiple contending models by calculating the weighted average of their posterior probability distributions. Our results demonstrate that our iterative BMA algorithm for survival analysis achieves high prediction accuracy while consistently selecting a small and cost-effective number of predictor genes. Results: We applied the iterative BMA algorithm to two cancer datasets: breast cancer and diffuse large B-cell lymphoma (DLBCL) data. On the breast cancer data, the algorithm selected a total of 15 predictor genes across 84 contending models from the training data. The maximum likelihood estimates of the selected genes and the posterior probabilities of the selected models from the training data were used to divide patients in the test (or validation) dataset into high-and low-risk categories. Using the genes and models determined from the training data, we assigned patients from the test data into highly distinct risk groups (as indicated by a p-value of 7.26e-05 from the log-rank test). Moreover, we achieved comparable results using only the 5 top selected genes with 100% posterior probabilities. On the DLBCL data, our iterative BMA procedure selected a total of 25 genes across 3 contending models from the training data. Once again, we assigned the patients in the validation set to significantly distinct risk groups (p-value = 0.00139). Conclusion: T

关键词： Posterior Probability Partial Little Square Lasso Ridge Regression feature selection algorithm

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OPTIMUM feature SUBSET FOR OPTIMIZING CROP YIELD PREDICTION USING FILTER AND WRAPPER APPROACHES

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APPLIED ENGINEERING IN AGRICULTURE 2019年第1期35卷 9-14页

作者： Gopal, P. S. Maya Bhargavi, R. VIT Univ Sch Comp Sci & Engn Chennai 600127 Tamil Nadu India

In agriculture, crop yield prediction is critical. Crop yield depends on various features which can be categorized as geographical, climatic, and biological. Geographical features consist of cultivable land in hectares, canal length to cover the cultivable land, number of tanks and tube wells available for irrigation. Climatic features consist of rainfall, temperature, and radiation. Biological features consist of seeds, minerals, and nutrients. In total, 15 features were considered for this study to understand features impact on paddy crop yield for all seasons of each year. For selecting vital features, five filter and wrapper approaches were applied. For predicting accuracy of features selection algorithm, Multiple Linear Regression (MLR) model was used. The RMSE, MAE, R, and RRMSE metrics were used to evaluate the performance of feature selection algorithms. Data used for the analysis was drawn from secondary sources of state Agriculture Department, Government of Tamil Nadu, India, for over 30 years. Seventy-five percent of data was used for training and 25% was used for testing. Low computational time was also considered for the selection of best feature subset. Outcome of all feature selection algorithms have given similar results in the RMSE, RRMSE, R, and MAE values. The adjusted R(2 )value was used to find the optimum feature subset despite all the deviations. The evaluation of the dataset used in this work shows that total area of cultivation, number of tanks and open wells used for irrigation, length of canals used for irrigation, and average maximum temperature during the season of the crop are the best features for better crop yield prediction on the study area. The MLR gives 85% of model accuracy for the selected features with low computational time.

关键词： feature selection algorithm Model validation Multiple linear regression Performance metrics

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Solving customer insurance coverage sales plan problem using a multi-stage data mining approach

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KYBERNETES 2018年第1期47卷 2-19页

作者： Abdi, Farshid Khalili-Damghani, Kaveh Abolmakarem, Shaghayegh Islamic Azad Univ Dept Ind Engn South Tehran Branch Tehran Iran

Purpose - Customer insurance coverage sales plan problem, in which the loyal customers are recognized and offered some special plans, is an essential problem facing insurance companies. On the other hand, the loyal customers who have enough potential to renew their insurance contracts at the end of the contract term should be persuaded to repurchase or renew their contracts. The aim of this paper is to propose a three-stage data-mining approach to recognize high-potential loyal insurance customers and to predict/plan special insurance coverage sales. Design/methodology/approach - The first stage addresses data cleansing. In the second stage, several filter and wrapper methods are implemented to select proper features. In the third stage, K-nearest neighbor algorithm is used to cluster the customers. The approach aims to select a compact feature subset with the maximal prediction capability. The proposed approach can detect the customers who are more likely to buy a specific insurance coverage at the end of a contract term. Findings - The proposed approach has been applied in a real case study of insurance company in Iran. On the basis of the findings, the proposed approach is capable of recognizing the customer clusters and planning a suitable insurance coverage sales plans for loyal customers with proper accuracy level. Therefore, the proposed approach can be useful for the insurance company which helps them to identify their potential clients. Consequently, insurance managers can consider appropriate marketing tactics and appropriate resource allocation of the insurance company to their high-potential loyal customers and prevent switching themto competitors. Originality/value - Despite the importance of recognizing high-potential loyal insurance customers, little study has been done in this area. In this paper, data-mining techniques were developed for the prediction of special insurance coverage sales on the basis of customers' characteristics. The method allows

关键词： Data mining Knowledge management feature selection algorithm K-nearest neighbor algorithm Insurance coverage sales

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DNN-BP: a novel framework for cuffless blood pressure measurement from optimal PPG features using deep learning model

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2024年第12期62卷 3687-3708页

作者： Raju, S. M. Taslim Uddin Dipto, Safin Ahmed Hossain, Md Imran Chowdhury, Md. Abu Shahid Haque, Fabliha Nashrah, Ayesha Tun Nishan, Araf Khan, Md Mahamudul Hasan Hashem, M. M. A. Khulna Univ Engn & Technol Dept Comp Sci & Engn Khulna 9203 Bangladesh Khulna Univ Engn & Technol Dept Biomed Engn Khulna 9203 Bangladesh Int Amer Univ Dept Business Adm Los Angeles CA 90010 USA

Continuous blood pressure (BP) provides essential information for monitoring one's health condition. However, BP is currently monitored using uncomfortable cuff-based devices, which does not support continuous BP monitoring. This paper aims to introduce a blood pressure monitoring algorithm based on only photoplethysmography (PPG) signals using the deep neural network (DNN). The PPG signals are obtained from 125 unique subjects with 218 records and filtered using signal processing algorithms to reduce the effects of noise, such as baseline wandering, and motion artifacts. The proposed algorithm is based on pulse wave analysis of PPG signals, extracted various domain features from PPG signals, and mapped them to BP values. Four feature selection methods are applied and yielded four feature subsets. Therefore, an ensemble feature selection technique is proposed to obtain the optimal feature set based on major voting scores from four feature subsets. DNN models, along with the ensemble feature selection technique, outperformed in estimating the systolic blood pressure (SBP) and diastolic blood pressure (DBP) compared to previously reported approaches that rely only on the PPG signal. The coefficient of determination ( R-2) and mean absolute error (MAE) of the proposed algorithm are 0.962 and 2.480 mmHg, respectively, for SBP and 0.955 and 1.499 mmHg, respectively, for DBP. The proposed approach meets the Advancement of Medical Instrumentation standard for SBP and DBP estimations. Additionally, according to the British Hypertension Society standard, the results attained Grade A for both SBP and DBP estimations. It concludes that BP can be estimated more accurately using the optimal feature set and DNN models. The proposed algorithm has the potential ability to facilitate mobile healthcare devices to monitor continuous BP.

关键词： Blood pressure Photoplethysmogram feature extraction feature selection algorithm Deep neural networks

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An application of kernel methods to variety identification based on SSR markers genetic fingerprinting

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BMC BIOINFORMATICS 2011年第1期12卷 1-7页

作者： Martin, Florian Philip Morris Int R&D Biol Syst Res Philip Morris Prod SA Neuchatel Switzerland

Background: In crop production systems, genetic markers are increasingly used to distinguish individuals within a larger population based on their genetic make-up. Supervised approaches cannot be applied directly to genotyping data due to the specific nature of those data which are neither continuous, nor nominal, nor ordinal but only partially ordered. Therefore, a strategy is needed to encode the polymorphism between samples such that known supervised approaches can be applied. Moreover, finding a minimal set of molecular markers that have optimal ability to discriminate, for example, between given groups of varieties, is important as the genotyping process can be costly in terms of laboratory consumables, labor, and time. This feature selection problem also needs special care due to the specific nature of the data used. Results: An approach encoding SSR polymorphisms in a positive definite kernel is presented, which then allows the usage of any kernel supervised method. The polymorphism between the samples is encoded through the Nei Li genetic distance, which is shown to define a positive definite kernel between the genotyped samples. Additionally, a greedy feature selection algorithm for selecting SSR marker kits is presented to build economical and efficient prediction models for discrimination. The algorithm is a filter method and outperforms other filter methods adapted to this setting. When combined with kernel linear discriminant analysis or kernel principal component analysis followed by linear discriminant analysis, the approach leads to very satisfactory prediction models. Conclusions: The main advantage of the approach is to benefit from a flexible way to encode polymorphisms in a kernel and when combined with a feature selection algorithm resulting in a few specific markers, it leads to accurate and economical identification models based on SSR genotyping.

关键词： Discrimination Power feature selection algorithm Kernel Principal Component Analysis Classification Error Rate Positive Definite Kernel

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selection of Important features for Optimizing Crop Yield Prediction

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INTERNATIONAL JOURNAL OF AGRICULTURAL AND ENVIRONMENTAL INFORMATION SYSTEMS 2019年第3期10卷 54-71页

作者： Gopal, Maya P. S. Bhargavi, R. VIT Univ Sch Comp Sci & Engn Chennai Campus Chennai Tamil Nadu India

In agriculture, crop yield prediction is critical. Crop yield depends on various features including geographic, climate and biological. This research article discusses five feature selection (FS) algorithms namely Sequential Forward FS, Sequential Backward Elimination FS, Correlation based FS, Random Forest Variable Importance and the Variance Inflation Factor algorithm for feature selection. Data used for the analysis was drawn from secondary sources of the Tamil Nadu state Agriculture Department for a period of 30 years. 75% of data was used for training and 25% data was used for testing. The performance of the feature selection algorithms are evaluated by Multiple Linear Regression. RMSE, MAE, R and RRMSE metrics are calculated for the feature selection algorithms. The adjusted R2 was used to find the optimum feature subset. Also, the time complexity of the algorithms was considered for the computation. The selected features are applied to Multilinear regression, Artificial Neural Network and M5Prime. MLR gives 85% of accuracy by using the features which are selected by SFFS algorithm.

关键词： Artificial Neural Network feature selection algorithm M5Prime Model Validation Multiple Linear Regression Performance Metrics

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Test on existence of histology subtype-specific prognostic signatures among early stage lung adenocarcinoma and squamous cell carcinoma patients using a Cox-model based filter

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BIOLOGY DIRECT 2015年第1期10卷 1-17页

作者： Tian, Suyan Wang, Chi An, Ming-Wen Jilin Univ Hosp 1 Div Clin Epidemiol Changchun 130021 Jilin Peoples R China Univ Kentucky Dept Biostat Lexington KY 40536 USA Univ Kentucky Markey Canc Ctr Lexington KY 40536 USA Vassar Coll Dept Math Poughkeepsie NY 12604 USA

Background: Non-small cell lung cancer (NSCLC) is the predominant histological type of lung cancer, accounting for up to 85% of cases. Disease stage is commonly used to determine adjuvant treatment eligibility of NSCLC patients, however, it is an imprecise predictor of the prognosis of an individual patient. Currently, many researchers resort to microarray technology for identifying relevant genetic prognostic markers, with particular attention on trimming or extending a Cox regression model. Adenocarcinoma (AC) and squamous cell carcinoma (SCC) are two major histology subtypes of NSCLC. It has been demonstrated that fundamental differences exist in their underlying mechanisms, which motivated us to postulate the existence of specific genes related to the prognosis of each histology subtype. Results: In this article, we propose a simple filter feature selection algorithm with a Cox regression model as the base. Applying this method to real-world microarray data identifies a histology-specific prognostic gene signature. Furthermore, the resulting 32-gene (32/12 for AC/SCC) prognostic signature for early-stage AC and SCC samples has superior predictive ability relative to two relevant prognostic signatures, and has comparable performance with signatures obtained by applying two state-of-the art algorithms separately to AC and SCC samples. Conclusions: Our proposal is conceptually simple, and straightforward to implement. Furthermore, it can be easily adapted and applied to a range of other research settings.

关键词： Non-small cell lung cancer (NSCLC) Adenocarcinoma (AC) Squamous cell carcinoma (SCC) Cox model Prognosis Histology-subtype specific Gene expression barcode feature selection algorithm

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Identification of Different Concentrations Pesticide Residues of Dimethoate on Spinach Leaves by Hyperspectral Image Technology

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IFAC-PapersOnLine 2018年第17期51卷 758-763页

作者： Zhan-qi, R.E.N. Zhen-hong, R.A.O. Hai-yan, J.I. Key Laboratory of Modern Precision Agriculture System Integration Research Ministry of Education China Agricultural University Beijing100083 China College of Science China Agricultural University Beijing100083 China

Taking spinach leaves with different concentrations of dimethoate as the research object, the feasibility of identifying spinach leaves containing different concentrations of dimethoate pesticide residues based on hyperspectral imaging and machine learning algorithms was discussed. The band range of hyperspectral images between 900 to 1700mm were scanned by hyperspectral imaging system. The region of interest (ROIs) of the leaves were selected by ENVI and corrected by multiple scatter correction (MSC). The principal component analysis (PCA) was used to analyze the spectral data, and the results showed that the PCA can effectively discriminate spinach samples with different concentrations at the visual level. In addition, the chi-square test feature selection algorithm was combined with support vectors classification (SVC), K nearest neighbor (KNN), random forest algorithm (RF), and linear discriminant analysis (LDA) respectively. The average and standard deviation of the prediction accuracy of the 10-fold cross-validation was chosen as evaluation methods. By comparison, chi-square test combined with LDA was the optimal model and the selected characteristic wavelengths were 1445.8, 1449, 1452.3, 1455.5, 1458.7, 1462, 1465.2 and 1468.4nm. The prediction accuracy and standard deviation of the model were 0.997, 0.008. The results showed that spinach leaves containing different concentrations of dimethoate pesticide residues could be accurately identified based on hyperspectral imaging. © 2018

关键词： Hyperspectral imaging Decision trees Discriminant analysis Image segmentation Independent component analysis Learning algorithms Learning systems Nearest neighbor search Pesticides Principal component analysis Spectroscopy Statistical tests Statistics 10 fold cross validation Dimethoate feature selection algorithm K nearest neighbor (KNN) Linear discriminant analyses (LDA) Pesticide residue Random forest algorithm Spinach leaves

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Smartphone-based pH titration for liquid food applications

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CHEMICAL PAPERS 2024年第16期78卷 8849-8862页

作者： Xiao, Yuhui Huang, Yaqiu Qiu, Junhong Cai, Honghao Ni, Hui Jimei Univ Sch Sci Dept Phys Xiamen Fujian Peoples R China Jimei Univ Sch Marine Engn Dept Marine Engn Xiamen Fujian Peoples R China Jimei Univ Coll Food & Biol Engn Xiamen Fujian Peoples R China Fujian Prov Key Lab Food Microbiol & Enzyme Engn Xiamen Fujian Peoples R China

The pH detection helps control food quality, prevent spoilage, determine storage methods, and monitor additive levels. In the previous studies, colorimetric pH detection involved manual capture of target regions and classification of acid-base categories, leading to time-consuming processes. Additionally, some researchers relied solely on R*G*B* or H*S*V* to build regression models, potentially limiting their generalizability and robustness. To address the limitations, this study proposed a colorimetric method that combines pH paper, smartphone, computer vision, and machine learning for fast and precise pH detection. Advantages of the computer vision model YOLOv5 include its ability to quickly capture the target region of the pH paper and automatically categorize it as either acidic or basic. Subsequently, recursive feature elimination was applied to filter out irrelevant features from the R*G*B*, H*S*V*, L*a*b*, Gray, XR, XG, and XB. Finally, the support vector regression was used to develop the regression model for pH value prediction. YOLOv5 demonstrated exceptional performance with mean average precision of 0.995, classification accuracy of 100%, and detection time of 4.9 ms. The pH prediction model achieved a mean absolute error (MAE) of 0.023 for acidity and 0.061 for alkalinity, signifying a notable advancement compared to the MAE range of 0.03-0.46 observed in the previous studies. The proposed approach shows potential in improving the dependability and effectiveness of pH detection, specifically in resource-constrained scenarios.

关键词： pH detection Colorimetric detection Support vector regression Machine learning YOLO feature selection algorithm

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Automatic selection of Control features for Electroencephalography-Based Brain-Computer Interface Assisted Motor Rehabilitation: The GUIDER algorithm

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BRAIN TOPOGRAPHY 2022年第2期35卷 182-190页

作者： Colamarino, Emma Pichiorri, Floriana Toppi, Jlenia Mattia, Donatella Cincotti, Febo Sapienza Univ Rome Dept Comp Control & Management Engn Via Ariosto 25 I-00185 Rome Italy Fdn Santa Lucia IRCCS Neuroelect Imaging & Brain Comp Interface Lab Via Ardeatina 306 I-00179 Rome Italy

Sensorimotor rhythms-based Brain-Computer Interfaces (BCIs) have successfully been employed to address upper limb motor rehabilitation after stroke. In this context, becomes crucial the choice of features that would enable an appropriate electroencephalographic (EEG) sensorimotor activation/engagement underlying the favourable motor recovery. Here, we present a novel feature selection algorithm (GUIDER) designed and implemented to integrate specific requirements related to neurophysiological knowledge and rehabilitative principles. The GUIDER algorithm was tested on an EEG dataset collected from 13 subacute stroke participants. The comparison between the automatic feature selection procedure by means of GUIDER algorithm and the manual feature selection executed by an expert neurophysiologist returned similar performance in terms of both feature selection and classification. Our preliminary findings suggest that the choices of experienced neurophysiologists could be reproducible by an automatic approach. The proposed automatic algorithm could be apt to support the professional end-users not expert in BCI such as therapist/clinicians and, to ultimately foster a wider employment of the BCI-based rehabilitation after stroke.

关键词： Brain-Computer Interface feature selection algorithm Motor rehabilitation Stroke Electroencephalography Motor imagery

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