关键词： DNA methylation   Schizophrenia   Machine learning   Classification   Clustering  

摘要： Schizophrenia is a common mental disorder with high heritability. It is genetically complex and to date more than a hundred risk loci have been identified. Association of environmental factors and schizophrenia has also been reported, while epigenetic analyses have yielded ambiguous and sometimes conflicting results. Here, we analyzed fresh frozen post-mortem brain tissue from a cohort of 73 subjects diagnosed with schizophrenia and 52 control samples, using the Illumina Infinium HumanMethylation450 Bead Chip, to investigate genome-wide DNA methylation patterns in the two groups. Analysis of differential methylation was performed with the Bioconductor Minfi package and modern machine-learning and visualization techniques, which were shown previously to be successful in detecting and highlighting differentially methylated patterns in case-control studies. In this dataset, however, these methods did not uncover any significant signals discerning the patient group and healthy controls, suggesting that if there are methylation changes associated with schizophrenia, they are heterogeneous and complex with small effect.

关键词： Alzheimer's disease   ADNI   Cortical thickness   Gyrification index   Fractals   ADAS cognitive test   Machine learning   Classification   Support vector machine  

摘要： Early detection of Alzheimer's disease (AD) using structural magnetic resonance images is essential for early treatment that can slow the progression of the disease. Therefore, there is a need for accurate computer-aided-diagnosis (CAD) systems for detecting AD. The purpose of this work is to evaluate the degree to which specific features - including fractals obtained from MRI-based surfaces of the cerebral cortex, cortical thickness, gyrification index and the Alzheimer's disease assessment scale (ADAS) cognitive test scores - are informative for classifying AD patients and healthy control subjects using several machine learning classifiers. Our results show that a Support Vector Machine (SVM) trained with cortical thickness, gyrification index and ADAS cognitive test scores distinguishes between AD and healthy control subjects better than other machine learning methods and other feature combinations. This specific CAD system achieved ideal accuracy and outperformed recently proposed systems. (C) 2018 Published by Elsevier Ltd.

关键词： Feature extraction   Machine learning   Classification   Mammography   Breast cancer   Gabor wavelet  

摘要： Among the major causes of female mortality, breast cancer used to pose big challenges to the medical world. Currently, the most popular method of monitoring and diagnosesin addition to mammographyis carrying out repeated biopsies to locate the tumor further, that may result in loss of breast tissues. This paper presents an effective method of classifying and detecting the masses in mammograms. In the proposed method, we first attain the feature vector pertaining to each mammography image based on Gabor wavelet transform. Then, we performed tenfold cross validation through several experiments, analyzing the data complexity on each fold. We also used some machine learning methods as decision-making stage and achieved mean accuracies above 0.939, mean sensitivities as high as 0.951, and the mean specificities greater than 0.92. Evaluations and comparisons witness the effectiveness of the proposed method for better diagnosis of breast cancer against the known classification techniques developed in mammography. Simplicity, robustness and high accuracy are advantages of the proposed method.

关键词： Actinobacterial strains   Colony features   Image processing   Deep neural network   Transfer learning   ResNet  

摘要： Recognition of actinobacterial species on solid culture plates is an error-prone and time-consuming process which retard all the down-stream process after the isolation step. In this paper, we propose an optimized solution for the mentioned problem by using machine learning and image processing algorithms to diminish the cost and time and increase the accuracy of the detection. Three methods are compared in this paper for actinobacterial strains recognition. In the first method, two-level wavelet transform is applied on images of actinobacterial strains and statistical texture features are computed from wavelet subbands. Furthermore, some statistical color features are calculated from color information. In consequence, Principle Component Analysis (PCA) is employed for dimension reduction and finally, a Multi-Layer Perceptron (MLP) neural network was used for classification. In the second method, a Convolution Neural Network (CNN) is used to extract the features automatically. In the third method, transfer learning is employed for feature extraction and classfication. The first method is evaluated on two databases, *** and ***, and the accuracy obtained between 80.8% to 80.1%, respectively. Employing CNN as the feature extractor improved the accuracy about 4%. Transfer learnig results 85.96% accuracy on the *** and 91.90% on the subclasses of ***. The experiments have shown that using transfer learning of DCNN of type ResNet has better performance compared to the pervious methods. The proposed methods are universal and can be used for recognition of other circle-like colony-shape microorganisms. In particular, giving limited and unbalanced training data, which is a common failure in biological data sets, the proposed methods harbor remarkable accuracy. The data augmentation methods showed to be efficient and practical for the current purpose along with being easy to be implemented and integrated.

关键词： Pixels  

摘要： The problem of detecting neurons in optical microscopy is considered by the example of Nissl-stained mouse brain slices. The proposed algorithm consists of the following steps: preprocessing, textural feature extraction, pixel classification, and pixel clustering. When solving this problem, we investigate various preprocessing methods, machine learning algorithms, and textural features. At the classification step, the k nearest neighbor (kNN) algorithm or the naive Bayes classifier (NBC) is used to determine whether each pixel of the image belongs to the neuron's cell body. In this paper, we investigate textural features of two types: features based on the normalized histogram and features based on the gray level co-occurrence matrix (GLCM). To find the centers of the neurons, all pixels that belong to the neurons are clustered using the mean shift algorithm. It is shown that the best detection quality (precision = 0.82, recall = 0.92, and F1 = 0.86) is achieved with GLCM-based features and neighborhood radius R = 6. It is also shown that the selection of different preprocessing algorithms significantly affects the detection result. In terms of detection quality, the kNN algorithm outperforms the NBC. On the dataset used, the selection of the parameter k > 15 does not significantly improve the quality of detection. The proposed method yields the result similar to that achieved in [1]: Recall(A) = 865%. In sampling tests on some microscopy images from the Broad Bioimage Benchmark Collection (BBBC), the proposed approach shows the best or equivalent quality in detecting the number of cells on the image. For detection, the algorithm uses only local textural features, which removes restrictions on the parallelization of computations.

关键词： machine learning   rapid damage assessment   seismic instrumentation and monitoring   seismic response demands   tall building clusters  

摘要： An approach to reconstructing full-profile seismic response demands across multiple tall buildings, using kernel-based machine learning methods, is introduced. Nonlinear response history analyses are used to generate a dataset of peak floor accelerations and peak story drift ratios for a portfolio of tall buildings, using spatially explicit ground motions from the Northridge earthquake. Structural dissimilarities are incorporated by including a range of building heights and differences in the type and combination of lateral force resisting systems. Using measurements from limited locations within a subset of buildings, the full-profile response demands for all buildings in a portfolio are reconstructed. A rigorous evaluation procedure is used to demonstrate the ability of the kernel-based methods to accurately capture the highly nonlinear response demand patterns within and across buildings. For a scenario where the first floor, mid-height, and roof level responses are known for 40% of the buildings, the kernel-based machine learning methods are able to estimate the full-profile demands of the entire portfolio with a median error that is approximately 30% of the measured demands.

关键词： Comprehensive two-dimensional gas chromatography   GCxGC   Machine learning   Classification   Data mining  

摘要： Machine learning (ML) has been used previously to recognize particular patterns of constituent compounds. Here, ML is used with comprehensive chemical fingerprints that capture the distribution of all constituent compounds to flexibly perform various pattern recognition tasks. Such pattern recognition requires a sequence of chemical analysis, data analysis, and pattern analysis. Chemical analysis with comprehensive multidimensional chromatography is a maturing approach for highly effective separations of complex samples and so provides a solid foundation for undertaking comprehensive chemical fingerprinting. Data analysis with smart templates employs marker peaks and chemical logic for chromatographic alignment and peak-regions to delineate chromatographic windows in which analytes are quantified and matched consistently across chromatograms to create chemical profiles that serve as complete fingerprints. Pattern analysis uses ML techniques with the resulting fingerprints to recognize sample characteristics, e.g., for classification. Our experiments evaluated the effectiveness of seventeen different ML techniques for various classification problems with chemical fingerprints from a rich data set from 126 wine samples of different varieties, geographic regions, vintages, and wineries. Results of these experiments showed an accuracy range from 58% to 88% for different ML methods on the most difficult classification problems and 96% to 100% for different ML methods on the least difficult classification problems. Averaged over 14 classification problems, accuracy for the different methods ranged from 80% to 90%, with some relatively simple ML techniques among the top-performing methods. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Aluminium electrolysis   Electrolyte   Liquidus temperature   Prediction   Support vector machine   Machine learning method  

摘要： Liquidus temperature is an important physicochemical property parameter of electrolyte system. It is significant to construct a practical model to predict the liquidus temperature of electrolyte with universal adaptability, since the current formulas is unsatisfactory with many limitations. In this work, different kinds of machine learning algorithms were used to explore the correlations of the liquidus temperature with the components of electrolyte. It was found that the performance of SVM(Support vector machine) model and GBR(Gradient boosting regression) model were better than those of PIS (Partial least squares regression), MLR(Multiple linear regression), ANN(Artificial neural network) and RFR(Random forest regression) for predicting liquidus temperatures with the component of electrolyte. Furthermore, the optimal models were applied to 20 independent electrolyte samples to predict the liquidus temperatures. The predicted results based on SVM model is also much better than that of the linear formula reported in the literature and GBR model. The SVM model outlined here can provide the accurate prediction method of the liquidus temperature for industrial complex electrolyte, it is crucial for balance and stability of the electrolytic cell in the process of electrolytic aluminum production.

关键词： focuses on   robotics   Machine Learning   Learning methods  

关键词： GNSS-IR   NMRI   vegetation water content   artificial neural network   random forest   drought  

摘要： Vegetation water content (VWC) is recognized as an important parameter in vegetation growth studies, natural disasters such as forest fires, and drought prediction. Recently, the Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) has emerged as an important technique for monitoring vegetation information. The normalized microwave reflection index (NMRI) was developed to reflect the change of VWC based on this fact. However, NMRI uses local site-based data, and the sparse distribution hinders the application of NMRI. In this study, we obtained a 500 m spatially continuous NMRI product by integrating GNSS-IR site data with other VWC-related products using the point-surface fusion technique. The auxiliary data in the fusion process include the normalized difference vegetation index (NDVI), gross primary productivity (GPP), and precipitation. Meanwhile, the fusion performance of three machine learning methods, i.e., the back-propagation neural network (BPNN), generalized regression neural network (GRNN), and random forest (RF) are compared and analyzed. The machine learning methods achieve satisfactory results, with cross-validation R values of 0.71-0.83 and RMSEs of 0.025-0.037. The results show a clear improvement over the traditional multiple linear regression method, which achieves R (RMSE) values of only about 0.4 (0.045). It indicates that the machine learning methods can better learn the complex nonlinear relationship between NMRI and the input VWC-related index. Among the machine learning methods, the RF model obtained the best results. Long time-series NMRI images with a 500 m spatial resolution in the western part of the continental U.S. were then obtained. The results show that the spatial distribution of the NMRI product is consistent with a drought situation from 2012 to 2014 in the U.S., which verifies the feasibility of analyzing and predicting drought times and distribution ranges by using the 500 m fusion product.