iSucc-PseAAC:Prediction of Lysine Succinylation Modification Sites Based on Ensemble Machine Learning / 中国生物化学与分子生物学报

Lysine succinylation is a novel post-translational modification, which plays an important role in regulating distinct cellular functions control, therefore it is necessary to accurately identify succinylation sites in proteins. As traditional experiments consume material and financial resources, prediction by calculation is an efficient method being proposed recently. In this study, we developed a new prediction method iSucc-PseAAC, which uses a variety of classification algorithms combined with different feature extraction methods. Moreover, it is found that under the feature extraction based on coupled sequence (PseAAC), the classification effect of support vector machine is the best, and it could be combined with ensemble learning to solve the problem of data imbalance. Compared with the existing methods, iSucc-PseAAC has more significance and practicality in predicting lysine succinylation sites.

iSulf_wide-PseAAC: A Protein S-sulfonylation Prediction Method Based on Ensemble Support Vector Machine Approach / 中国生物化学与分子生物学报

Protein S-sulfonylation is a reversible protein post-translational modification (PTM) that plays a vital role in biological growth. It is also related to some diseases. Therefore from the perspective of basic research or drug development, we are faced with a challenging question: where are the S-sulfonylation sites on proteins? In order to solve this problem, we developed a method for site prediction. The working principle of the system is (1) Combine these proteins into isometric amino acids.(2) Use down sampling to balance the training data set;(3) Build a comprehensive prediction system through ensemble support vector machines. In the end, the accuracy rate on the independent tester reached 90. 77%. Compared with the existing methods, all other indicators have been improved, and the improvement effect is obvious. Thus we provided help for the development of bioinformatics. We have established a friendly web server prediction website: http://www.jci-bioinfo.cn/iSulf_Wide-PseAAC, through which the website can make the prediction online, without complicated calculation formulas. At the same time, the mathematical methods used in this article can solve many problems in similar related fields.

Application of multiple empirical kernel mapping ensemble classifier based on self-paced learning in ultrasound-based computer-aided diagnosis for breast cancer / 生物医学工程学杂志

Both feature representation and classifier performance are important factors that determine the performance of computer-aided diagnosis (CAD) systems. In order to improve the performance of ultrasound-based CAD for breast cancers, a novel multiple empirical kernel mapping (MEKM) exclusivity regularized machine (ERM) ensemble classifier algorithm based on self-paced learning (SPL) is proposed, which simultaneously promotes the performance of both feature representation and the classifier. The proposed algorithm first generates multiple groups of features by MEKM to enhance the ability of feature representation, which also work as the kernel transform in multiple support vector machines embedded in ERM. The SPL strategy is then adopted to adaptively select samples from easy to hard so as to gradually train the ERM classifier model with improved performance. This algorithm is verified on a B-mode ultrasound dataset and an elastography ultrasound dataset, respectively. The results show that the classification accuracy, sensitivity and specificity on B-mode ultrasound are (86.36±6.45)%, (88.15±7.12)%, and (84.52±9.38)%, respectively, and the classification accuracy, sensitivity and specificity on elastography ultrasound are (85.97±3.75)%, (85.93±6.09)%, and (86.03±5.88)%, respectively. It indicates that the proposed algorithm can effectively improve the performance of ultrasound-based CAD for breast cancers with the potential for application.

Psychosis speech recognition algorithm based on deep embedded sparse stacked autoencoder and manifold ensemble / 生物医学工程学杂志

Speech feature learning is the core and key of speech recognition method for mental illness. Deep feature learning can automatically extract speech features, but it is limited by the problem of small samples. Traditional feature extraction (original features) can avoid the impact of small samples, but it relies heavily on experience and is poorly adaptive. To solve this problem, this paper proposes a deep embedded hybrid feature sparse stack autoencoder manifold ensemble algorithm. Firstly, based on the prior knowledge, the psychotic speech features are extracted, and the original features are constructed. Secondly, the original features are embedded in the sparse stack autoencoder (deep network), and the output of the hidden layer is filtered to enhance the complementarity between the deep features and the original features. Third, the L1 regularization feature selection mechanism is designed to compress the dimensions of the mixed feature set composed of deep features and original features. Finally, a weighted local preserving projection algorithm and an ensemble learning mechanism are designed, and a manifold projection classifier ensemble model is constructed, which further improves the classification stability of feature fusion under small samples. In addition, this paper designs a medium-to-large-scale psychotic speech collection program for the first time, collects and constructs a large-scale Chinese psychotic speech database for the verification of psychotic speech recognition algorithms. The experimental results show that the main innovation of the algorithm is effective, and the classification accuracy is better than other representative algorithms, and the maximum improvement is 3.3%. In conclusion, this paper proposes a new method of psychotic speech recognition based on embedded mixed sparse stack autoencoder and manifold ensemble, which effectively improves the recognition rate of psychotic speech.

Single-modal neuroimaging computer aided diagnosis for schizophrenia based on ensemble learning using privileged information / 生物医学工程学杂志

Neuroimaging technologies have been applied to the diagnosis of schizophrenia. In order to improve the performance of the single-modal neuroimaging-based computer-aided diagnosis (CAD) for schizophrenia, an ensemble learning algorithm based on learning using privileged information (LUPI) was proposed in this work. Specifically, the extreme learning machine based auto-encoder (ELM-AE) was first adopted to learn new feature representation for the single-modal neuroimaging data. Random project algorithm was then performed on the learned high-dimensional features to generate several new feature subspaces. After that, multiple feature pairs were built among these subspaces to work as source domain and target domain, respectively, which were used to train multiple support vector machine plus (SVM+) classifier. Finally, a strong classifier is learned by combining these SVM+ classifiers for classification. The proposed algorithm was evaluated on a public schizophrenia neuroimaging dataset, including the data of structural magnetic resonance imaging (sMRI) and functional MRI (fMRI). The results showed that the proposed algorithm achieved the best diagnosis performance. In particular, the classification accuracy, sensitivity and specificity of the proposed algorithm were 72.12% ± 8.20%, 73.50% ± 15.44% and 70.93% ± 12.93%, respectively, on the sMRI data, and it also achieved the classification accuracy of 72.33% ± 8.95%, sensitivity of 68.50% ± 16.58% and specificity of 75.73% ± 16.10% on the fMRI data. The proposed algorithm overcomes the problem that the traditional LUPI methods need the additional privileged information modality as source domain. It can be directly applied to the single-modal data for classification, and also can improve the classification performance. Therefore, it suggests that the proposed algorithm will have wider applications.

A partition bagging ensemble learning algorithm for Parkinson's speech data mining / 生物医学工程学杂志

Methods for achieving diagnosis of Parkinson's disease (PD) based on speech data mining have been proven effective in recent years. However, due to factors such as the degree of disease of the data collection subjects and the collection equipment and environment, there are different categories of sample aliasing in the sample space of the acquired data set. Samples in the aliased area are difficult to be identified effectively, which seriously affects the classification accuracy of the algorithm. In order to solve this problem, a partition bagging ensemble learning is proposed in this article, which measures the aliasing degree of the sample by designing the the ratio of sample centroid distance metrics and divides the training set into multiple subsets. And then the method of transfer training of misclassified samples is used to adjust the results of subset partitioning. Finally, the optimized weights of each sub-classifier are used to integrate the test results. The experimental results show that the classification accuracy of the proposed method is significantly improved on two public datasets and the increasement of mean accuracy is up to 25.44%. This method not only effectively improves the classification accuracy of PD speech dataset, but also increases the sample utilization rate, providing a new idea for the diagnosis of PD.

Early prognosis of Alzheimer's disease based on convolutional neural networks and ensemble learning / 生物医学工程学杂志

Alzheimer's disease (AD) is a typical neurodegenerative disease, which is clinically manifested as amnesia, loss of language ability and self-care ability, and so on. So far, the cause of the disease has still been unclear and the course of the disease is irreversible, and there has been no cure for the disease yet. Hence, early prognosis of AD is important for the development of new drugs and measures to slow the progression of the disease. Mild cognitive impairment (MCI) is a state between AD and healthy controls (HC). Studies have shown that patients with MCI are more likely to develop AD than those without MCI. Therefore, accurate screening of MCI patients has become one of the research hotspots of early prognosis of AD. With the rapid development of neuroimaging techniques and deep learning, more and more researchers employ deep learning methods to analyze brain neuroimaging images, such as magnetic resonance imaging (MRI), for early prognosis of AD. Hence, in this paper, a three-dimensional multi-slice classifiers ensemble based on convolutional neural network (CNN) and ensemble learning for early prognosis of AD has been proposed. Compared with the CNN classification model based on a single slice, the proposed classifiers ensemble based on multiple two-dimensional slices from three dimensions could use more effective information contained in MRI to improve classification accuracy and stability in a parallel computing mode.

Computer-aided diagnosis of Parkinson's disease based on the stacked deep polynomial networks ensemble learning framework / 生物医学工程学杂志

Feature representation is the crucial factor for the magnetic resonance imaging (MRI) based computer-aided diagnosis (CAD) of Parkinson's disease (PD). Deep polynomial network (DPN) is a novel supervised deep learning algorithm, which has excellent feature representation for small dataset. In this work, a stacked DPN (SDPN) based ensemble learning framework is proposed for diagnosis of PD, which can improve diagnostic accuracy for small dataset. In the proposed framework, SDPN was performed on each subset of extracted features from MRI images to generate new feature representation. The support vector machine (SVM) was then adopted to perform classification task on each subset. The ensemble learning algorithm was then performed on all the SVM classifiers to generate the final diagnosis for PD. The experimental results on the Parkinson's Progression Markers Initiative dataset (PPMI) showed that the proposed algorithm achieved the classification accuracy, sensitivity and specificity of 90.15%, 85.48% and 93.27%, respectively, with the brain network features, and it also got the classification accuracy of 87.18%, sensitivity of 86.90% and specificity of 87.27% on the multi-view features extracted from different brain regions. Moreover, the proposed algorithm outperformed other algorithms on the MRI dataset from PPMI. It suggests that the proposed SDPN-based ensemble learning framework has the feasibility and effectiveness for the CAD of PD.

Analysis of the Influencing Factors of Post-stroke Depression: Based on Machine Learning / 中医杂志

Objective To determine the influencing factors of post-stroke depression by machine learning.Methods Stroke patients' medical records (688 cases eligible) were extracted from record system,including age,gender,pulse manifestation,complexion,tongue quality,fur,Chinese medicine intervention,body mass index (BMI),blood pressure,blood glucose,blood triglyceride,blood total cholesterol,smoking history,drinking history,depression family history,stroke lesion site in imaging,as well as the final depression judgment.Single rule algorithm (1R) was adopted to learn.The risk factors influencing post-stroke patients' depression in extracted information were determined.Then the cases collected were divided into the training dataset (500 cases) and the test dataset (188cases).Optimal discriminant results were obtained by random forest model.Results Single rule algorithm showed that the most important influencing factor of post-stroke depression was stroke lesion site.By computer speculation,stroke lesions in the frontal and temporal lobes were most prone to post-stroke depression.Basal ganglia,brain stem,cerebellum,medulla oblongata and occipital lobe lesions were less likely to cause depression.The accurate classification rate could amount to 88.95％ (612/688 cases).Random forest model determination was made in the former 500cases in the training dataset.The total correct rate of determining depression was 98.2％.The total correct rate of determination in 188 cases of the test dataset was 99.47％.Six hundred and eighty-eight patients' data were learnt by random forest model.The total correct rate was 98.84％.The importance measure results showed that top 3 important indexes of post-stroke depression were lesion site,Chinese medicine intervention and depression family history.Conclusion Patients with lesions in the frontal & temporal lobes and depression family history were most prone to post-stroke depression.

Approach of clustering ensemble in gene expression data based on Wavelet De-noising / 重庆医科大学学报

Objective:To provide the methodology reference for the gene expression data clustering.Methods:This paper proposed a fuzzy clustering ensemble algorithm based on wavelet de-noising.The new method is applied to the yeast cell's gene expression data,and the clustering results were compared with fuzzy ensemble clustering methods.Results:Apply micro-precision to evaluate the clustering results.The research indicated that the proposed method was superior to fuzzy ensemble clustering algotuthm.Conclusion:The proposed method has better clustering for the gene expression data clustering.