关键词： cancer biology   machine learning   support vector machines   LASSO algorithm   elastic net algorithm   compressed sensing  

摘要： The objectives of this Perspective paper are to review some recent advances in sparse feature selection for regression and classification, as well as compressed sensing, and to discuss how these might be used to develop tools to advance personalized cancer therapy. As an illustration of the possibilities, a new algorithm for sparse regression is presented and is applied to predict the time to tumour recurrence in ovarian cancer. A new algorithm for sparse feature selection in classification problems is presented, and its validation in endometrial cancer is briefly discussed. Some open problems are also presented.

关键词： image analysis   fruit detection   machine learning   young fruit   tomato  

摘要： Fully automated yield estimation of intact fruits prior to harvesting provides various benefits to farmers. Until now, several studies have been conducted to estimate fruit yield using image-processing technologies. However, most of these techniques require thresholds for features such as color, shape and size. In addition, their performance strongly depends on the thresholds used, although optimal thresholds tend to vary with images. Furthermore, most of these techniques have attempted to detect only mature and immature fruits, although the number of young fruits is more important for the prediction of long-term fluctuations in yield. In this study, we aimed to develop a method to accurately detect individual intact tomato fruits including mature, immature and young fruits on a plant using a conventional RGB digital camera in conjunction with machine learning approaches. The developed method did not require an adjustment of threshold values for fruit detection from each image because image segmentation was conducted based on classification models generated in accordance with the color, shape, texture and size of the images. The results of fruit detection in the test images showed that the developed method achieved a recall of 0.80, while the precision was 0.88. The recall values of mature, immature and young fruits were 1.00, 0.80 and 0.78, respectively.

关键词： Roughness   Road pavement   Support vector machine   Random forest   Neural network   Weighted longitudinal profile   Machine learning   In-vehicle sensors   Vehicle dynamics   Simulation  

摘要： The roughness of a road pavement affects safety, ride comfort, and road durability. A useful indicator for evaluating roughness is the weighted longitudinal profile (wLP). In this paper, three machine learning models are compared for estimating the wLP when vehicle response information, i.e., accelerometer and wheel speed data is collected from common in-vehicle sensors. A multilayer perceptron, support vector machine (SVM) and random forest were applied for testing their effectiveness in estimating the key indices of wLP, namely, range and standard deviation. These models were trained from a set of features extracted from vehicle response simulations on accurate replications of roads with various roughness problems. In contrast to other research, the authors validated models with measurements collected with a probe vehicle. The results show that roughness phenomena can be accurately detected. The SVM produced the best results, although the models achieved rather similar performance. However, differences were found regarding the model robustness when reducing the size of the training feature set. The proposed method enables road network monitoring to be achieved by conventional passenger cars, which can be seen as a practical supplement to the prevalent road measurements with cost-intensive mobile devices. (C) 2014 American Society of Civil Engineers.

关键词： Bagged nearest neighbor   Nonparametric regression   Probability estimation   Random forest   Support vector machine  

摘要： Probability estimation for binary and multicategory outcome using logistic and multinomial logistic regression has a long-standing tradition in biostatistics. However, biases may occur if the model is misspecified. In contrast, outcome probabilities for individuals can be estimated consistently with machine learning approaches, including k-nearest neighbors (k-NN), bagged nearest neighbors (b-NN), random forests (RF), and support vector machines (SVM). Because machine learning methods are rarely used by applied biostatisticians, the primary goal of this paper is to explain the concept of probability estimation with these methods and to summarize recent theoretical findings. Probability estimation in k-NN, b-NN, and RF can be embedded into the class of nonparametric regression learning machines;therefore, we start with the construction of nonparametric regression estimates and review results on consistency and rates of convergence. In SVMs, outcome probabilities for individuals are estimated consistently by repeatedly solving classification problems. For SVMs we review classification problem and then dichotomous probability estimation. Next we extend the algorithms for estimating probabilities using k-NN, b-NN, and RF to multicategory outcomes and discuss approaches for the multicategory probability estimation problem using SVM. In simulation studies for dichotomous and multicategory dependent variables we demonstrate the general validity of the machine learning methods and compare it with logistic regression. However, each method fails in at least one simulation scenario. We conclude with a discussion of the failures and give recommendations for selecting and tuning the methods. Applications to real data and example code are provided in a companion article

关键词： 结节病   肺结核   DCT   NB   SVM  

摘要： 结节病和肺结核的临床鉴别诊断目前仍然是困难的。搜集了106例结节病和肺结核的对比资料,并筛选出对分类有意义的临床指标作为特征,将其进行必要的量化和缩放形成训练数据,然后分别用支持向量机(SVM:Support Vector Machine)、决策分类树(DCT:Decision Classification Tree)、朴素贝叶斯(NB:Nave Bayes)3种不同的方法进行训练,并用5倍交叉验证评估各种不同的模型的有效性。实验结果表明,这3种方法在识别结节病时对应的ROC曲线下的面积分别为0.978,0.96,0.690,得到的测试精度分别达到100%,96.15%,96.15%,训练精度分别为95.28%,90.57%,92.38%。用这3种方法得到的分类器对19例临床未能确诊的病患进行预测,DCT方法的预测结果与SVM方法的结果高度吻合(19例中仅1例预测结果不同),而NB方法预测结果稍差(19例中有3例与SVM预测结果不一致)。实验结果表明,3种方法中,SVM方法的分类能力和分类精度最高。临床实验结果表明,19例临床未能确诊的病患按照SVM算法预测的结果进行治疗均得到了康复。

关键词： 提升小波   支持向量机   核函数   交叉验证   肝癌和肺癌基因芯片  

摘要： 针对肿瘤的早期诊断,提出了一种基于提升小波变换的特征提取的方法,对肿瘤数据样本进行分析鉴别.该方法利用提升小波变换对190例肝癌(包括对照)和107例肺癌(包括对照)基因表达谱芯片数据进行处理后,提取信号的低频信息,经支持向量机训练学习,构造分类器模型,用于癌和非癌样本的区分甄别.实验结果表明,经提升小波变换提取的特征基因,送入分类器中能得到较高的分类率,且在支持向量机中选取线性核函数或径向基函数都能达到较好的分类效果.通过随机选取的20例基因表改谱芯片样本,对所建立的模型进行了测试,获得了很好的效果,因此,本文提出的方法对肿瘤的诊断有一定的应用意义.

关键词： Bayesian analysis   Statistical data interpretation   Systems biology  

摘要： In this article we introduce modern statistical machine learning and bioinformatics approaches that have been used in learning statistical relationships from big data in medicine and behavioral science that typically include clinical, genomic (and proteomic) and environmental variables. Every year, data collected from biomedical and behavioral science is getting larger and more complicated. Thus, in medicine, we also need to be aware of this trend and understand the statistical tools that are available to analyze these datasets. Many statistical analyses that are aimed to analyze such big datasets have been introduced recently. However, given many different types of clinical, genomic, and environmental data, it is rather uncommon to see statistical methods that combine knowledge resulting from those different data types. To this extent, we will introduce big data in terms of clinical data, single nucleotide polymorphism and gene expression studies and their interactions with environment. In this article, we will introduce the concept of well-known regression analyses such as linear and logistic regressions that has been widely used in clinical data analyses and modern statistical models such as Bayesian networks that has been introduced to analyze more complicated data. Also we will discuss how to represent the interaction among clinical, genomic, and environmental data in using modern statistical models. We conclude this article with a promising modern statistical method called Bayesian networks that is suitable in analyzing big data sets that consists with different type of large data from clinical, genomic, and environmental data. Such statistical model form big data will provide us with more comprehensive understanding of human physiology and disease.

关键词： agriculture   ASTER satellite images   object-oriented image analysis   hierarchical classification   neural networks  

摘要： The strategic management of agricultural lands involves crop field monitoring each year. Crop discrimination via remote sensing is a complex task, especially if different crops have a similar spectral response and cropping pattern. In such cases, crop identification could be improved by combining object-based image analysis and advanced machine learning methods. In this investigation, we evaluated the C4.5 decision tree, logistic regression (LR), support vector machine (SVM) and multilayer perceptron (MLP) neural network methods, both as single classifiers and combined in a hierarchical classification, for the mapping of nine major summer crops (both woody and herbaceous) from ASTER satellite images captured in two different dates. Each method was built with different combinations of spectral and textural features obtained after the segmentation of the remote images in an object-based framework. As single classifiers, MLP and SVM obtained maximum overall accuracy of 88%, slightly higher than LR (86%) and notably higher than C4.5 (79%). The SVM+SVM classifier (best method) improved these results to 89%. In most cases, the hierarchical classifiers considerably increased the accuracy of the most poorly classified class (minimum sensitivity). The SVM+SVM method offered a significant improvement in classification accuracy for all of the studied crops compared to the conventional decision tree classifier, ranging between 4% for safflower and 29% for corn, which suggests the application of object-based image analysis and advanced machine learning methods in complex crop classification tasks.

关键词： Numerical modeling   Biomedical modeling   Machine learning   Testing procedures   Intensity modulated radiation therapy   Cancer   Artificial neural networks   Medical treatment planning   Spatial analysis   Spatial dimensions  

摘要： Purpose: To evaluate the accuracy of the dose prediction models constructed with machine learning techniques, Support Vector Machine (SVM) and Artificial Neural Network (ANN) for the prediction of dose volume histogram (DVH) of organs-at-risk (OAR) in IMRT, compared to the model constructed by stepwise multiple regression (MR), and to investigate the number of prior plans required for the models to produce reliable predictions. Methods: IMRT plans from 102 prostate cases were randomly divided into two datasets for training and testing, respectively. The testing dataset contains a fixed number of 20 cases, while the number of cases in the training dataset varied from 5 to 80. Models were constructed with SVM, ANN, or MR to formulate the dependence of the OAR DVH on patient anatomical features including the Distance to Target Histogram (DTH), PTV and OAR volumes and their overlap, among other volumetric or spatial information. The D50 (Dose value at 50% volume) and the mean square of difference between D50 of clinical and predicted DVH were calculated for each modeling technique at each specific training dataset number. Results: The mean square of difference of D50 between clinical and predicted DVH decreases with the number of cases in the training dataset, and reaches stable beyond 30 for MR. With the 80 case training dataset, for the bladder model, the SVM predicted 70% D50 values within 10% error and the ANN predicted 85%, compared to 85% with multiple regression. For the rectum model, the numbers are SVM 80%, ANN 70%, and MR 85%. Conclusion: The machine learning techniques SVM and ANN are comparable to MR for producing OAR DVH prediction of the prostate cancer. The minimal number of training cases is around 30. Partially supported by NIH/NCI under grant #R21CA161389 and a master research grant by Varian Medical System.

关键词： Protein solubility   Protein solubility prediction   In silico prediction   Recombinant protein expression   Escherichia coli   Machine learning   Bioinformatics   Computational biology  

摘要： Background: Over the last 20 years in biotechnology, the production of recombinant proteins has been a crucial bioprocess in both biopharmaceutical and research arena in terms of human health, scientific impact and economic volume. Although logical strategies of genetic engineering have been established, protein overexpression is still an art. In particular, heterologous expression is often hindered by low level of production and frequent fail due to opaque reasons. The problem is accentuated because there is no generic solution available to enhance heterologous overexpression. For a given protein, the extent of its solubility can indicate the quality of its function. Over 30% of synthesized proteins are not soluble. In certain experimental circumstances, including temperature, expression host, etc., protein solubility is a feature eventually defined by its sequence. Until now, numerous methods based on machine learning are proposed to predict the solubility of protein merely from its amino acid sequence. In spite of the 20 years of research on the matter, no comprehensive review is available on the published methods. Results: This paper presents an extensive review of the existing models to predict protein solubility in Escherichia coli recombinant protein overexpression system. The models are investigated and compared regarding the datasets used, features, feature selection methods, machine learning techniques and accuracy of prediction. A discussion on the models is provided at the end. Conclusions: This study aims to investigate extensively the machine learning based methods to predict recombinant protein solubility, so as to offer a general as well as a detailed understanding for researches in the field. Some of the models present acceptable prediction performances and convenient user interfaces. These models can be considered as valuable tools to predict recombinant protein overexpression results before performing real laboratory experiments, thus saving labou