Hyperplane Distance Research Articles

Therapeutic peptides identification via kernel risk sensitive loss-based k-nearest neighbor model and multi-Laplacian regularization.

Therapeutic peptides are therapeutic agents synthesized from natural amino acids, which can be used as carriers for precisely transporting drugs and can activate the immune system for preventing and treating various diseases. However, screening therapeutic peptides using biochemical assays is expensive, time-consuming, and limited by experimental conditions and biological samples, and there may be ethical considerations in the clinical stage. In contrast, screening therapeutic peptides using machine learning and computational methods is efficient, automated, and can accurately predict potential therapeutic peptides. In this study, a k-nearest neighbor model based on multi-Laplacian and kernel risk sensitive loss was proposed, which introduces a kernel risk loss function derived from the K-local hyperplane distance nearest neighbor model as well as combining the Laplacian regularization method to predict therapeutic peptides. The findings indicated that the suggested approach achieved satisfactory results and could effectively predict therapeutic peptide sequences.

Multi-view local hyperplane nearest neighbor model based on independence criterion for identifying vesicular transport proteins

Vesicular transport proteins participate in various biological processes and play a significant role in the movement of substances within cells. These proteins are associated with numerous human diseases, making their identification particularly important. In this study, we developed a novel strategy for accurately identifying vesicular transport proteins. We developed a novel multi-view classifier called graph-regularized k-local hyperplane distance nearest neighbor model (HSIC-GHKNN), which combines the Hilbert–Schmidt independence criterion (HSIC)-based multi-view learning method with a local hyperplane distance nearest-neighbor classifier. We first extracted protein evolution information using two feature extraction methods, pseudo-position-specific scoring matrix (PsePSSM) and AATP, and addressed dataset imbalance using the Edited Nearest Neighbors (ENN) algorithm. Subsequently, we employed a local hyperplane distance nearest-neighbor classifier for each view identification and added an HSIC term to maintain independence between views. We then assessed the performance of our identification strategy and analyzed the PsePSSM and AATP feature sets to determine the influencing factors of the classification results. The experimental results demonstrate that the accurate and Matthew correlation coefficients of our strategy on the independent test set are 85.8 % and 0.548, respectively. Our approach outperformed existing methods in most evaluation metrics. In addition, the proposed multi-view classification model can easily be applied to similar identification tasks.

A GHKNN model based on the physicochemical property extraction method to identify SNARE proteins.

There is a great deal of importance to SNARE proteins, and their absence from function can lead to a variety of diseases. The SNARE protein is known as a membrane fusion protein, and it is crucial for mediating vesicle fusion. The identification of SNARE proteins must therefore be conducted with an accurate method. Through extensive experiments, we have developed a model based on graph-regularized k-local hyperplane distance nearest neighbor model (GHKNN) binary classification. In this, the model uses the physicochemical property extraction method to extract protein sequence features and the SMOTE method to upsample protein sequence features. The combination achieves the most accurate performance for identifying all protein sequences. Finally, we compare the model based on GHKNN binary classification with other classifiers and measure them using four different metrics: SN, SP, ACC, and MCC. In experiments, the model performs significantly better than other classifiers.

Identify ncRNA Subcellular Localization via Graph Regularized k-Local Hyperplane Distance Nearest Neighbor Model on Multi-Kernel Learning.

Non-coding RNAs (ncRNAs) are a type of RNAs which are not used to encode protein sequences. Emerging evidence shows that lots of ncRNAs may participate in many biological processes and must be widely involved in many types of cancers. Therefore, understanding their functionality is of great importance. Similar to proteins, various functions of ncRNAs relies on their subcellular localizations. Traditional high-throughput methods in wet-lab to identify subcellular localization is time-consuming and costly. In this paper, we propose a novel computational method based on multi-kernel learning to identify multi-label ncRNA subcellular localizations, via graph regularized k-local hyperplane distance nearest neighbor algorithm. First, we construct six types of sequence-based feature descriptors and select important feature vectors. Then, we build a multi-kernel learning model with Hilbert-Schmidt independence criterion (HSIC) to obtain optimal weights for vairous features. Furthermore, we propose the graph regularized k-local hyperplane distance nearest neighbor algorithm (GHKNN) as a binary classification model for detecting one kind of non-coding RNA subcellular localization. Finally, we apply One-vs-Rest strategy to decompose multi-label problem of non-coding RNA subcellular localizations. Our method achieves excellent performance on three ncRNA datasets and three human ncRNA datasets, and out-performs other outstanding machine learning methods. Comparing to existing method, our model also performs well especially on small datasets. We expect that this model will be useful for the prediction of subcellular localization and the study of important functional mechanisms of ncRNAs. Furthermore, we establish user-friendly web server (http://ncrna.lbci.net/) with the implementation of our method, which can be easily used by most experimental scientists.

Identification of Vesicle Transport Proteins via Hypergraph Regularized K-Local Hyperplane Distance Nearest Neighbour Model.

The prediction of protein function is a common topic in the field of bioinformatics. In recent years, advances in machine learning have inspired a growing number of algorithms for predicting protein function. A large number of parameters and fairly complex neural networks are often used to improve the prediction performance, an approach that is time-consuming and costly. In this study, we leveraged traditional features and machine learning classifiers to boost the performance of vesicle transport protein identification and make the prediction process faster. We adopt the pseudo position-specific scoring matrix (PsePSSM) feature and our proposed new classifier hypergraph regularized k-local hyperplane distance nearest neighbour (HG-HKNN) to classify vesicular transport proteins. We address dataset imbalances with random undersampling. The results show that our strategy has an area under the receiver operating characteristic curve (AUC) of 0.870 and a Matthews correlation coefficient (MCC) of 0.53 on the benchmark dataset, outperforming all state-of-the-art methods on the same dataset, and other metrics of our model are also comparable to existing methods.

Inequality distance hyperplane multiclass support vector machines

In this study, inequality distance hyperplane multiclass support vector machines (IDH-MSVM) algorithm is proposed on the basis of multiclassification support vector machine (MSVM) which was proposed by J. Weston and C. Watkins in 1999. It only needs to solve a single objective optimization problem to deal with multiclassification problems. For original MSVM, the hyperplane distance refers to the classification interval between classical margin and hyperplane, which is equality. However, the IDH-MSVM introduces parameters to adjust distance between every classification hyperplane and classical margin, which makes the hyperplane distance inequality. The effectiveness of the proposed method is experimented on UCI standard data sets and compared with several multiclassification algorithms. Experimental results show that this method has a better classification effect on multiclassification data.

Identification of protein-nucleotide binding residues via graph regularized k-local hyperplane distance nearest neighbor model

Identification of protein-nucleotide binding residues via graph regularized k-local hyperplane distance nearest neighbor model

Model uncertainty quantification for diagnosis of each main coronary artery stenosis

One of the main causes of death in the world is coronary artery disease (CAD). CAD occurs when there is stenosis in one or more of the three major coronary arteries: right coronary artery (RCA), left circumflex (LCX) artery, and left anterior descending (LAD) artery. The gold standard or CAD diagnosis is angiography, but it is invasive, costly, and time consuming. Therefore, researchers continually seek new machine learning methods that can screen for CAD non-invasively. For reliable and cost-effective CAD diagnosis, several algorithms have been developed. Most prior studies analyzed the presence or absence of CAD in a dichotomous manner. Herein, we studied the more complex problem of classification of stenosis in individual LAD, LCX, and RCA by applying machine learning algorithms on the Z-Alizadeh Sani dataset that comprised 303 subjects, each with 54 features. In addition, our new methodology is developed to handle model uncertainty in the prediction of individual artery stenosis. It uses the hyperplane distance from a sample and accuracy rate of the classifier during the training phase to enhance its performance. Our results demonstrate high diagnostic performance of the proposed method for diagnosis of stenosis in individual RCA, LCX, and LAD, achieving accuracy rates of 82.67%, 83.67% and 86.43%, respectively. This is the best performance of ML techniques applied to the Z-Alizadeh Sani dataset.

Colorectal Cancer and Colitis Diagnosis Using Fourier Transform Infrared Spectroscopy and an Improved K-Nearest-Neighbour Classifier.

Combining Fourier transform infrared spectroscopy (FTIR) with endoscopy, it is expected that noninvasive, rapid detection of colorectal cancer can be performed in vivo in the future. In this study, Fourier transform infrared spectra were collected from 88 endoscopic biopsy colorectal tissue samples (41 colitis and 47 cancers). A new method, viz., entropy weight local-hyperplane k-nearest-neighbor (EWHK), which is an improved version of K-local hyperplane distance nearest-neighbor (HKNN), is proposed for tissue classification. In order to avoid limiting high dimensions and small values of the nearest neighbor, the new EWHK method calculates feature weights based on information entropy. The average results of the random classification showed that the EWHK classifier for differentiating cancer from colitis samples produced a sensitivity of 81.38% and a specificity of 92.69%.

Diagnosis of Breast Cancer Tissues Using 785 nm Miniature Raman Spectrometer and Pattern Regression.

For achieving the development of a portable, low-cost and in vivo cancer diagnosis instrument, a laser 785 nm miniature Raman spectrometer was used to acquire the Raman spectra for breast cancer detection in this paper. However, because of the low spectral signal-to-noise ratio, it is difficult to achieve high discrimination accuracy by using the miniature Raman spectrometer. Therefore, a pattern recognition method of the adaptive net analyte signal (NAS) weight k-local hyperplane (ANWKH) is proposed to increase the classification accuracy. ANWKH is an extension and improvement of K-local hyperplane distance nearest-neighbor (HKNN), and combines the advantages of the adaptive weight k-local hyperplane (AWKH) and the net analyte signal (NAS). In this algorithm, NAS was first used to eliminate the influence caused by other non-target factors. Then, the distance between the test set samples and hyperplane was calculated with consideration of the feature weights. The HKNN only works well for small values of the nearest-neighbor. However, the accuracy decreases with increasing values of the nearest-neighbor. The method presented in this paper can resolve the basic shortcoming by using the feature weights. The original spectra are projected into the vertical subspace without the objective factors. NAS was employed to obtain the spectra without irrelevant information. NAS can improve the classification accuracy, sensitivity, and specificity of breast cancer early diagnosis. Experimental results of Raman spectra detection in vitro of breast tissues showed that the proposed algorithm can obtain high classification accuracy, sensitivity, and specificity. This paper demonstrates that the ANWKH algorithm is feasible for early clinical diagnosis of breast cancer in the future.

Optimal combination of feature selection and classification via local hyperplane based learning strategy.

BackgroundClassifying cancers by gene selection is among the most important and challenging procedures in biomedicine. A major challenge is to design an effective method that eliminates irrelevant, redundant, or noisy genes from the classification, while retaining all of the highly discriminative genes.ResultsWe propose a gene selection method, called local hyperplane-based discriminant analysis (LHDA). LHDA adopts two central ideas. First, it uses a local approximation rather than global measurement; second, it embeds a recently reported classification model, K-Local Hyperplane Distance Nearest Neighbor(HKNN) classifier, into its discriminator. Through classification accuracy-based iterations, LHDA obtains the feature weight vector and finally extracts the optimal feature subset. The performance of the proposed method is evaluated in extensive experiments on synthetic and real microarray benchmark datasets. Eight classical feature selection methods, four classification models and two popular embedded learning schemes, including k-nearest neighbor (KNN), hyperplane k-nearest neighbor (HKNN), Support Vector Machine (SVM) and Random Forest are employed for comparisons.ConclusionThe proposed method yielded comparable to or superior performances to seven state-of-the-art models. The nice performance demonstrate the superiority of combining feature weighting with model learning into an unified framework to achieve the two tasks simultaneously.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0629-6) contains supplementary material, which is available to authorized users.

Hyperplane distance neighbor clustering based on local discriminant analysis for complex chemical processes monitoring

The collected training data often include both normal and faulty samples for complex chemical processes. However, some monitoring methods, such as partial least squares (PLS), principal component analysis (PCA), indepen- dent component analysis (ICA) and Fisher discriminant analysis (FDA), require fault-free data to build the normal op- eration model. These techniques are applicable after the preliminary step of data clustering is applied. We here propose a novel hyperplane distance neighbor clustering (HDNC) based on the local discriminant analysis (LDA) for chemical process monitoring. First, faulty samples are separated from normal ones using the HDNC method. Then, the optimal subspace for fault detection and classification can be obtained using the LDA approach. The proposed method takes the multimodality within the faulty data into account, and thus improves the capability of process monitoring signifi- cantly. The HDNC-LDA monitoring approach is applied to two simulation processes and then compared with the con- ventional FDA based on the K-nearest neighbor (KNN-FDA) method. The results obtained in two different scenarios demonstrate the superiority of the HDNC-LDA approach in terms of fault detection and classification accuracy.

Classification for breast cancer diagnosis with Raman spectroscopy.

In order to promote the development of the portable, low-cost and in vivo cancer diagnosis instrument, a miniature laser Raman spectrometer was employed to acquire the conventional Raman spectra for breast cancer detection in this paper. But it is difficult to achieve high discrimination accuracy. Then a novel method of adaptive weight k-local hyperplane (AWKH) is proposed to increase the classification accuracy. AWKH is an extension and improvement of K-local hyperplane distance nearest-neighbor (HKNN). It considers the features weights of the training data in the nearest neighbor selection and local hyperplane construction stage, which resolve the basic shortcoming of HKNN works well only for small values of the nearest-neighbor. Experimental results on Raman spectra of breast tissues in vitro show the proposed method can realize high classification accuracy.

Default mode network connectivity distinguishes chemotherapy-treated breast cancer survivors from controls

Breast cancer (BC) chemotherapy is associated with cognitive changes including persistent deficits in some individuals. We tested the accuracy of default mode network (DMN) resting state functional connectivity patterns in discriminating chemotherapy treated (C+) from non-chemotherapy (C-) treated BC survivors and healthy controls (HC). We also examined the relationship between DMN connectivity patterns and cognitive function. Multivariate pattern analysis was used to classify 30 C+, 27 C-, and 24 HC, which showed significant accuracy for discriminating C+ from C- (91.23%, P < 0.0001) and C+ from HC (90.74%, P < 0.0001). The C- group did not differ significantly from HC (47.06%, P = 0.60). Lower subjective memory function was correlated (P < 0.002) with greater hyperplane distance (distance from the linear decision function that optimally separates the groups). Disrupted DMN connectivity may help explain long-term cognitive difficulties following BC chemotherapy.

K-local hyperplane distance nearest neighbor classifier oriented local discriminant analysis

K-local hyperplane distance nearest neighbor (HKNN) classifier is an improved K-nearest neighbor (KNN) algorithm that has been successfully applied to pattern classification. This paper embeds the decision rule of HKNN classifier into the discriminant analysis model to develop a new feature extractor. The obtained feature extractor is called K-local hyperplane distance nearest neighbor classifier oriented local discriminant analysis (HOLDA), in which a regularization item is imposed on the original HKNN algorithm to obtain a more reliable distance metric. Based on this distance metric, the homo-class and hetero-class local scatters are characterized in HOLDA. By maximizing the ratio of the hetero-class local scatter to the homo-class local scatter, we obtain a subspace which is suitable for feature extraction and classification. In general, this paper provides a framework for building a feature extractor from the decision rule of a classifier. By this means, the feature extractor and classifier can be seamlessly integrated. Experimental results on four databases demonstrate that the integrated pattern recognition system is effective.

Random subspace evidence classifier

Although there exist a lot of k-nearest neighbor approaches and their variants, few of them consider how to make use of the information in both the whole feature space and subspaces. In order to address this limitation, we propose a new classifier named as the random subspace evidence classifier (RSEC). Specifically, RSEC first calculates the local hyperplane distance for each class as the evidences not only in the whole feature space, but also in randomly generated feature subspaces. Then, the basic belief assignment is computed according to these distances for the evidences of each class. In the following, all the evidences represented by basic belief assignments are pooled together by the Dempster's rule. Finally, RSEC assigns the class label to each test sample based on the combined belief assignment. The experiments in the datasets from UCI machine learning repository, artificial data and face image database illustrate that the proposed approach yields lower classification error in average comparing to 7 existing k-nearest neighbor approaches and variants when performing the classification task. In addition, RSEC has good performance in average on the high dimensional data and the minority class of the imbalanced data.

Perceptual relativity-based local hyperplane classification

The k-local hyperplane distance nearest neighbors classification (HKNN) builds a non-linear decision surface with maximal local margin in the input space, with invariance inferred from the local neighborhood rather than the prior knowledge, so that it performs very well in many applications. However, it still cannot be comparable with human being in classification on the noisy, the sparse, and the imbalance data. This paper proposes a new approach,called relative local hyperplane classifier(RLHC),to overcome this problem by utilizing the perceptual relativity to HKNN. It finds k nearest neighbors for the query sample from each class and then performs the relative transformation over all these nearest neighbors to build the relative space. Subsequently, each local hyperplane is constructed in the relative space, which is then applied to perform the classification. Experimental results on both real and simulated data suggest that the proposed approach often gives the better results in classification and robustness.

NONLINEAR SUBPROFILE SPACE FOR RADAR HRRP RECOGNITION

In this paper, a novel approach, namely nonlinear subprofile space (NSS), is proposed for radar target recognition using high-resolution range profile (HRRP). First, the HRRP samples are mapped into a high-dimensional feature space using nonlinear mapping. Second, the nonlinear features, namely nonlinear subprofiles, are extracted by nonlinear discriminant analysis. Then, for each class, the nonlinear subprofile space is formed using all the training nonlinear subprofiles of class. Finally, the minimum hyperplane distance classifier (MHDC) is used for classification. The aim of NSS method is to represent the feature area of target using nonlinear subprofile space, and effectively measure the distance between the test HRRP and feature area via minimum hyperplane distance (MHD) metric. The experimental results of measured data show that the proposed method has better performance of recognition than KPCA and KFDA.

Conotoxin Superfamily Prediction Using Diffusion Maps Dimensionality Reduction and Subspace Classifier

Conotoxins are disulfide-rich small peptides that are invaluable channel-targeted peptides and target neuronal receptors, which have been demonstrated to be potent pharmaceuticals in the treatment of Alzheimer's disease, Parkinson's disease, and epilepsy. Accurate prediction of conotoxin superfamily would have many important applications towards the understanding of its biological and pharmacological functions. In this study, a novel method, named dHKNN, is developed to predict conotoxin superfamily. Firstly, we extract the protein's sequential features composed of physicochemical properties, evolutionary information, predicted secondary structures and amino acid composition. Secondly, we use the diffusion maps for dimensionality reduction, which interpret the eigenfunctions of Markov matrices as a system of coordinates on the original data set in order to obtain efficient representation of data geometric descriptions. Finally, an improved K-local hyperplane distance nearest neighbor subspace classifier method called dHKNN is proposed for predicting conotoxin superfamilies by considering the local density information in the diffusion space. The overall accuracy of 91.90% is obtained through the jackknife cross-validation test on a benchmark dataset, indicating the proposed dHKNN is promising.

Completely Lazy Learning

Local classifiers are sometimes called lazy learners because they do not train a classifier until presented with a test sample. However, such methods are generally not completely lazy because the neighborhood size k (or other locality parameter) is usually chosen by cross validation on the training set, which can require significant preprocessing and risks overfitting. We propose a simple alternative to cross validation of the neighborhood size that requires no preprocessing: instead of committing to one neighborhood size, average the discriminants for multiple neighborhoods. We show that this forms an expected estimated posterior that minimizes the expected Bregman loss with respect to the uncertainty about the neighborhood choice. We analyze this approach for six standard and state-of-the-art local classifiers, including discriminative adaptive metric kNN (DANN), a local support vector machine (SVM-KNN), hyperplane distance nearest neighbor (HKNN), and a new local Bayesian quadratic discriminant analysis (local BDA). The empirical effectiveness of this technique versus cross validation is confirmed with experiments on seven benchmark data sets, showing that similar classification performance can be attained without any training.