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Stepwise classification of cancer samples using clinical and molecular data

BMC BIOINFORMATICS 2011年第1期12卷 1-12页

作者： Obulkasim, Askar Meijer, Gerrit A. van de Wiel, Mark A. Vrije Univ Amsterdam Med Ctr Dept Epidemiol & Biostat Amsterdam Netherlands Vrije Univ Amsterdam Med Ctr Dept Pathol Amsterdam Netherlands Vrije Univ Amsterdam Dept Math Amsterdam Netherlands

Background: Combining clinical and molecular data types may potentially improve prediction accuracy of a classifier. However, currently there is a shortage of effective and efficient statistical and bioinformatic tools for true integrative data analysis. Existing integrative classifiers have two main disadvantages: First, coarse combination may lead to subtle contributions of one data type to be overshadowed by more obvious contributions of the other. Second, the need to measure both data types for all patients may be both unpractical and (cost) inefficient. Results: We introduce a novel classification method, a stepwise classifier, which takes advantage of the distinct classification power of clinical data and high-dimensional molecular data. We apply classification algorithms to two data types independently, starting with the traditional clinical risk factors. We only turn to relatively expensive molecular data when the uncertainty of prediction result from clinical data exceeds a predefined limit. Experimental results show that our approach is adaptive: the proportion of samples that needs to be re-classified using molecular data depends on how much we expect the predictive accuracy to increase when re-classifying those samples. Conclusions: Our method renders a more cost-efficient classifier that is at least as good, and sometimes better, than one based on clinical or molecular data alone. Hence our approach is not just a classifier that minimizes a particular loss function. Instead, it aims to be cost-efficient by avoiding molecular tests for a potentially large subgroup of individuals;moreover, for these individuals a test result would be quickly available, which may lead to reduced waiting times (for diagnosis) and hence lower the patients distress. Stepwise classification is implemented in R-package stepwiseCM and available at the Bioconductor website.

关键词： Partial Little Square Data Type Random Forest Molecular Data classification algorithm

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Density-based Hardware-oriented classification for Spike Sorting Microsystems

Density-based Hardware-oriented Classification for Spike Sor...

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5th International IEEE Engineering-in-Medicine-and-Biology-Society (EMBS) Conference on Neural Engineering (NER)

作者： Cheng, Li-Fang Chen, Tung-Chien Chang, Nai-Fu Chen, Liang-Gee Natl Taiwan Univ Dept Elect Engn Taipei 10764 Taiwan Natl Taiwan Univ Grad Inst Elect Engn Taipei 10764 Taiwan

ISBN: (纸本)9781424441419

Successful proof-of-concept laboratory experiments on cortically-controlled brain computer interface motivate continued development for neural prosthetic microsystems (NPMs). One of the research directions is to realize realtime spike sorting processors (SSPs) on the NPM. The SSP detects the spikes, extracts the features, and then performs the classification algorithm in realtime in order to differentiate the spikes for the different firing neurons. Several architectures have been designed for the spike detection and feature extraction. However, the classification hardware is missing. To complete the SSP, a density-based hardware-oriented classification algorithm is proposed for hardware implementation. The traditional classification algorithms require a considerable memory space to store all the training features during the processing iteration, which results in a considerable power and area for the hardware. The proposed one is designed based on the density map of the spike features. The density map can be accumulated on-line with the coming of the spike features. Therefore the algorithm can save significant memory space, and is good for efficient hardware implementation.

关键词： algorithm design and analysis Clustering algorithms Feature extraction Hardware Memory management Sorting Training brain-computer interfaces classification algorithm cortically-controlled brain computer interface density-based hardware-oriented classification feature extraction feature extraction firing neurons hardware-oriented classification medical signal detection medical signal processing neural prosthetic microsystems neurophysiology prosthetics signal classification spike sorting microsystems spike sorting processors

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Optimal selection of potential customer range through the union sequential pattern by using a response model

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第6期38卷 7451-7461页

作者： Chen, Wen-Chin Hsu, Chiun-Chieh Hsu, Jing-Ning Natl Taiwan Univ Sci & Technol Dept Informat Management Taipei Taiwan Chunghwa Telecom Co Ltd Telecommun Labs Business & Mkt Strategy Res Dept Taipei Taiwan Chunghwa Telecom Co Ltd Data Commun Business Grp Dept Mkt Taipei Taiwan

Direct marketing is a common data mining application. Previous studies largely adopt the approach that, as the subjects for response model predictions, the entire customer population or filtered through certain attribute values is based on recommendations made from sales professionals. However, such methods may reduce response rates due to an oversized potential customer population, thus diminishing the accuracy of the prediction model. In resolve this problem, this work presents proposes a novel forecasting method that integrates the union sequential pattern with classification algorithms to facilitate the construction of customer response models. Based on use of a union sequential pattern, the potential customer size is established by identifying attributes with a high level of association. The prediction model is then constructed using classification algorithms such as support vector machines and logistic regression. Consequently, the problem involving the setting of range for potential customers can be solved, as well as the time spent on processing extended lists of customers during prediction. Finally, predicted potential Internet-phone customers and churning mobile-phone customers of a telecommunication company in Taiwan as are taken as an illustrative example, based on the proposed prediction model. The proposed method more accurately predicts potential customers than those of previous studies. Crown Copyright (c) 2010 Published by Elsevier Ltd. All rights reserved.

关键词： Response model Union sequential pattern classification algorithm Support vector machine Logistic regression

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Software to quantify and map vegetative cover in fallow fields for weed management decisions

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2011年第1期78卷 106-115页

作者： Wiles, L. J. USDA ARS Water Management Res Unit Ft Collins CO 80526 USA

Mapping weed cover during the fallow period of dryland crop rotations would be valuable for weed management in subsequent crops and could be done with low cost color digital cameras, however most managers lack the specialized software and expertise needed to create a map from the images. A system of software was developed to quantify weed cover in fallow fields in digital images and to simplify and automate the most challenging tasks that non-GIS professionals confront in creating and using maps derived from a large number of images. A GIS file of image locations is created with inexpensive consumer software. Images are classified, a GIS file is generated and the map is displayed in a simple GIS viewer with free software we developed. A map can be generated from 1000 images and 5000 GPS coordinates in 30 min, including image classification. The classified and original images for all locations can be viewed together easily from the map application. The accuracy of estimating weed cover was evaluated using images collected in 15 fields under natural light with a consumer grade camera mounted on an ATV driving 8-11 km h(-1). Weed cover was estimated with 96% accuracy for images, regardless of the amount of crop residue, unless part of the image was shaded by the camera. In those images, accuracy was 90% or better. This system will work with many professional and consumer digital cameras and GPS units and the classification algorithm can be easily modified for other applications. Published by Elsevier B.V.

关键词： Digital images Image analysis Weed map classification algorithm Site-specific weed management

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Superiority Demonstration of Variance-Considered Machines by Comparing Error Rate with Support Vector Machines

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2011年第3期9卷 595-600页

作者： Yeom, Hong-Gi Park, Seung-Min Park, Junheong Sim, Kwee-Bo Chung Ang Univ Sch Elect & Elect Engn Seoul South Korea

To improve the performance of classification algorithms, we proposed a new variance-considered machine (VCM) classification algorithm in a previous study. The study showed theoretically that VCMs have lower error probabilities than SVMs. The purpose of this paper is to experimentally demonstrate the superiority of VCMs. Therefore, we verified our proposal with several case experiments using data following a Gaussian distribution with different variances and prior probabilities. To estimate performance, the experiment for each case was executed 1000 times and the error rates were averaged for accuracy. The data of each experiment have different distances between means of data, and different ratios between training data and testing data. Thus, we proved that the error rate of VCMs is lower than the error rate of SVMs, although their performances were not similar in each case. Consequently, we expect that VCMs will be applied to a variety fields.

关键词： classification algorithm optimal hyperplane support vector machine variance-considered machine

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Feature extraction and selection for objective gait analysis and fall risk assessment by accelerometry

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BIOMEDICAL ENGINEERING ONLINE 2011年第1期10卷 1-19页

作者： Caby, Benoit Kieffer, Suzanne de Saint Hubert, Marie Cremer, Gerald Macq, Benoit Catholic Univ Louvain Telecommun & Teledetect Lab Louvain Belgium Mt Godinne Univ Clin Dept Geriatr Mt Godinne Belgium

Background: Falls in the elderly is nowadays a major concern because of their consequences on elderly general health and moral states. Moreover, the aging of the population and the increasing life expectancy make the prediction of falls more and more important. The analysis presented in this article makes a first step in this direction providing a way to analyze gait and classify hospitalized elderly fallers and non-faller. This tool, based on an accelerometer network and signal processing, gives objective informations about the gait and does not need any special gait laboratory as optical analysis do. The tool is also simple to use by a non expert and can therefore be widely used on a large set of patients. Method: A population of 20 hospitalized elderlies was asked to execute several classical clinical tests evaluating their risk of falling. They were also asked if they experienced any fall in the last 12 months. The accelerations of the limbs were recorded during the clinical tests with an accelerometer network distributed on the body. A total of 67 features were extracted from the accelerometric signal recorded during a simple 25 m walking test at comfort speed. A feature selection algorithm was used to select those able to classify subjects at risk and not at risk for several classification algorithms types. Results: The results showed that several classification algorithms were able to discriminate people from the two groups of interest: fallers and non-fallers hospitalized elderlies. The classification performances of the used algorithms were compared. Moreover a subset of the 67 features was considered to be significantly different between the two groups using a t-test. Conclusions: This study gives a method to classify a population of hospitalized elderlies in two groups: at risk of falling or not at risk based on accelerometric data. This is a first step to design a risk of falling assessment system that could be used to provide the right treatment as soon

关键词： Radial Basis Function classification algorithm Direct Feature Feature Selection algorithm Stop Line

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Detection of Bruise on Pear by Hyperspectral Imaging Sensor with Different classification algorithms

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SENSOR LETTERS 2010年第4期8卷 570-576页

作者： Zhao, Jiewen Ouyang, Qin Chen, Quansheng Wang, Jianhei Jiangsu Univ Sch Food & Biol Engn Zhenjiang 212013 Peoples R China

A hyperspectral imaging sensor system was developed for the detection of bruises on pears, for these bruises were difficult to be detected by traditional computer vision technique. Hyperspectral imaging sensor technique is susceptible to the effects of uneven illumination due to a spherical object of pear. The data of hyperspectral image is a 3-dimension cube, which contains a huge amount of information. So it requires a suitable algorithm to extract some useful information from the 3-dimension data cube. In this work, Principal Component Analysis (PCA) was firstly used to extract some useful information, then several other classification algorithms were used comparatively to process the 3-dimension data cube. These classification algorithms were Maximum Likelihood classification (MLC), Euclidean Distance classification (EDC), Mahalanobis Distance classification (MDC) and Spectral Angle Mapper (SAM), respectively. Results show that MDC and SAM have well performance, with detection accuracy of 93.8% and 95.0% respectively. Compared with the other classification algorithms, MDC and SAM can overcome the effects of uneven illumination in detecting bruise of pear by hyperspectral imaging sensor technique. This work demonstrates that it is feasible to detect the bruised region on the surface of pear by hyperspectral imaging sensor technique combined with MDC and SAM.

关键词： Hyperspectral Imaging Detection classification algorithm Bruise Pear

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Research on Decision Tree algorithm Based on Information Entropy

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International Conference on Materials Engineering for Advanced Technologies (ICMEAT2011)

作者： Du Ming Wang Shu-mei Gong Gu Xuzhou Normal Univ Coll Comp Sci & Technol Xuzhou 221116 Peoples R China

ISBN: (纸本)9783037851517

Decision tree is an important learning method in machine learning and data mining,this paper discusses the method of choosing the best attribute based on information entropy At analyzes the process and the characters of classification and the discovery knowledge based on decision tree about the application of decision tree on data mining. Through an instance, the paper shows the procedure of selecting the decision attribute in detail,finally it pointes out the developing trends of decision tree.

关键词： decision tree classification algorithm entropy information gain gain ratio

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Combining computational neuroscience and body sensor networks to investigate Alzheimer’s disease

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BMC Neuroscience 2012年第1期13卷 1-1页

作者： Jeroen Bergmann Newton Howard Medical Engineering Solutions in Osteoarthritis Centre of Excellence Imperial College London W68RF UK Massachusetts Institute of Technology Cambridge MA 02139 USA

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Inferring ethnicity from mitochondrial DNA sequence

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BMC proceedings 2011年第2期5 Suppl 2卷 S11页

作者： Chih Lee Ion I Măndoiu Craig E Nelson Computer Science and Engineering Department University of Connecticut Storrs CT USA. chihlee@engr.uconn.edu.

BACKGROUND:The assignment of DNA samples to coarse population groups can be a useful but difficult task. One such example is the inference of coarse ethnic groupings for forensic applications. Ethnicity plays an important role in forensic investigation and can be inferred with the help of genetic markers. Being maternally inherited, of high copy number, and robust persistence in degraded samples, mitochondrial DNA may be useful for inferring coarse ethnicity. In this study, we compare the performance of methods for inferring ethnicity from the sequence of the hypervariable region of the mitochondrial genome. RESULTS:We present the results of comprehensive experiments conducted on datasets extracted from the mtDNA population database, showing that ethnicity inference based on support vector machines (SVM) achieves an overall accuracy of 80-90%, consistently outperforming nearest neighbor and discriminant analysis methods previously proposed in the literature. We also evaluate methods of handling missing data and characterize the most informative segments of the hypervariable region of the mitochondrial genome. CONCLUSIONS:Support vector machines can be used to infer coarse ethnicity from a small region of mitochondrial DNA sequence with surprisingly high accuracy. In the presence of missing data, utilizing only the regions common to the training sequences and a test sequence proves to be the best strategy. Given these results, SVM algorithms are likely to also be useful in other DNA sequence classification applications.

关键词： Support Vector Machine Linear Discriminant Analysis classification algorithm Hypervariable Region Published Dataset

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