Rotation Forest Classifier Research Articles

A New Efficient and Privacy-Preserving Hybrid Classification model for Patient-Centric Clinical Decision Support System

A lot of focus has recently been placed on clinical decision support systems, that use advanced data mining methods to assist clinicians in making wise decisions. Along with increasing diagnosis accuracy, clinical decision support systems (CDSS) have the added benefit of speeding up diagnosis. Data security is crucial in this system. In this research, we offer EPPCD (Efficient and Privacy preserving Patient-centric Clinical Decision) support system to assist physicians in predicting illness risks of patients in a privacy-preserving manner. This system is proposed to solve the privacy difficulties present in the CDSS. The fine-grained access control enabled by the novel Double Encryption Ciphertext Policy Attribute-Based Encryption (DE-CPABE) technique is found to be a potential solution to this issue. In the proposed system, the past patients’ historical data are stored in cloud and can be used to train the hybrid Rotation Forest and AdaBoost classifier. Furthermore, extensive simulations used to evaluate performance show that our technology is capable of quickly and accurately determining a patient's disease risk while maintaining their privacy. The proposed system model is divided into five parties: Cloud Platform (CP), Trusted authority (TA), processing unit (PU), data provider (DP), and undiagnosed patient (PA).

Drug–target interaction prediction based on protein features, using wrapper feature selection

Drug–target interaction prediction is a vital stage in drug development, involving lots of methods. Experimental methods that identify these relationships on the basis of clinical remedies are time-taking, costly, laborious, and complex introducing a lot of challenges. One group of new methods is called computational methods. The development of new computational methods which are more accurate can be preferable to experimental methods, in terms of total cost and time. In this paper, a new computational model to predict drug–target interaction (DTI), consisting of three phases, including feature extraction, feature selection, and classification is proposed. In feature extraction phase, different features such as EAAC, PSSM and etc. would be extracted from sequence of proteins and fingerprint features from drugs. These extracted features would then be combined. In the next step, one of the wrapper feature selection methods named IWSSR, due to the large amount of extracted data, is applied. The selected features are then given to rotation forest classification, to have a more efficient prediction. Actually, the innovation of our work is that we extract different features; and then select features by the use of IWSSR. The accuracy of the rotation forest classifier based on tenfold on the golden standard datasets (enzyme, ion channels, G-protein-coupled receptors, nuclear receptors) is as follows: 98.12, 98.07, 96.82, and 95.64. The results of experiments indicate that the proposed model has an acceptable rate in DTI prediction and is compatible with the proposed methods in other papers.

Software Fault Prediction Using an RNN-Based Deep Learning Approach and Ensemble Machine Learning Techniques

Alongside the modern software development life cycle approaches, software testing has gained more importance and has become an area researched actively within the software engineering discipline. In this study, machine learning and deep learning-related software fault predictions were made through a data set named SFP XP-TDD, which was created using three different developed software projects. A data set of five different classifiers widely used in the literature and their Rotation Forest classifier ensemble versions were trained and tested using this data set. Numerous publications in the literature discussed software fault predictions through ML algorithms addressing solutions to different problems. Some of these articles indicated the usage of feature selection algorithms to improve classification performance, while others reported operating ensemble machine learning algorithms for software fault predictions. Besides, a detailed literature review revealed that there were few studies involving software fault prediction with DL algorithms due to the small sample sizes in the data sets and the low success rates in the tests performed on these datasets. As a result, the major contribution of this research was to statistically demonstrate that DL algorithms outperformed ML algorithms in data sets with large sample values via employing three separate software fault prediction datasets. The experimental outcomes of a model that includes a layer of recurrent neural networks (RNNs) were enclosed within this study. Alongside the aforementioned and generated data sets, the study also utilized the Eclipse and Apache Active MQ data sets in to test the effectiveness of the proposed deep learning method.

Cross-Validation Convolution Neural Network-Based Algorithm for Automated Detection of Diabetic Retinopathy

The substantial vision loss due to Diabetic Retinopathy (DR) mainly damages the blood vessels of the retina. These feature changes in the blood vessels fail to exist any manifestation in the eye at its initial stage, if this problem doesn’t exhibit initially, that leads to permanent blindness. So, this type of disorder can be only screened and identified through the processing of fundus images. The different stages in DR are Micro aneurysms (Ma), Hemorrhages (HE), and Exudates, and the stages in lesion show the chance of DR. For the advancement of early detection of DR in the eye we have developed the CNN-based identification approach on the fundus blood lesion image. The CNN-based automated detection of DR proposes the novel Graph cutter-built background and foreground superpixel segmentation technique and the foremost classification of fundus images feature was done through hybrid classifiers as K-Nearest Neighbor (KNN) classifier, Support Vector Machine (SVM) classifier, and Cascaded Rotation Forest (CRF) classifier. Over this classifier, the feature cross-validation made the classification more accurate and the comparison is made with the previous works of parameters such as specificity, sensitivity, and accuracy shows that the hybrid classifier attains excellent performance and achieves an overall accuracy of 98%. Among these Cascaded Rotation Forest (CRF) classifier has more accuracy than others.

NSECDA: Natural Semantic Enhancement for CircRNA-Disease Association Prediction.

Increasing evidence suggest that circRNA, as one of the most promising emerging biomarkers, has a very close relationship with diseases. Exploring the relationship between circRNA and diseases can provide novel perspective for diseases diagnosis and pathogenesis. The existing circRNA-disease association (CDA) prediction models, however, generally treat the data attributes equally, do not pay special attention to the attributes with more significant influence, and do not make full use of the correlation and symbiosis between attributes to dig into the latent semantic information of the data. Therefore, in response to the above problems, this paper proposes a natural semantic enhancement method NSECDA to predict CDA. In practical terms, we first recognize the circRNA sequence as a biological language, and analyze its natural semantic properties through the natural language understanding theory; then integrate it with disease attributes, circRNA and disease Gaussian Interaction Profile (GIP) kernel attributes, and use Graph Attention Network (GAT) to focus on the influential attributes, so as to mine the deeply hidden features; finally, the Rotation Forest (RoF) classifier was used to accurately determine CDA. In the gold standard data set CircR2Disease, NSECDA achieved 92.49% accuracy with 0.9225 AUC score. In comparison with the non-natural semantic enhancement model and other classifier models, NSECDA also shows competitive performance. Additionally, 25 of the CDA pairs with unknown associations in the top 30 prediction scores of NSECDA have been proven by newly reported studies. These achievements suggest that NSECDA is an effective model to predict CDA, which can provide credible candidate for subsequent wet experiments, thus significantly reducing the scope of investigations.

A Novel Ensemble Learning-Based Computational Method to Predict Protein-Protein Interactions from Protein Primary Sequences.

Simple SummaryProtein–protein interactions (PPIs) play a central role in the evolution and progression of various biological processes. In this article, we constructed a novel ensemble-learning-based model to predict potential PPIs, which only utilized the protein sequence information. The presented method used Discrete Hilbert transform to extract amino acid sequence information from position-specific scoring matrices. Then these extracted features were fed into rotation forest for training and predicting. When applying our method to the three datasets (Yeast, Human, and Oryza sativa) for detecting PPIs, we obtained excellent prediction performance. Furthermore, the comparison results indicated that our computational model is effective and robust in predicting potential PPI pairs.Protein–protein interactions (PPIs) are crucial for understanding the cellular processes, including signal cascade, DNA transcription, metabolic cycles, and repair. In the past decade, a multitude of high-throughput methods have been introduced to detect PPIs. However, these techniques are time-consuming, laborious, and always suffer from high false negative rates. Therefore, there is a great need of new computational methods as a supplemental tool for PPIs prediction. In this article, we present a novel sequence-based model to predict PPIs that combines Discrete Hilbert transform (DHT) and Rotation Forest (RoF). This method contains three stages: firstly, the Position-Specific Scoring Matrices (PSSM) was adopted to transform the amino acid sequence into a PSSM matrix, which can contain rich information about protein evolution. Then, the 400-dimensional DHT descriptor was constructed for each protein pair. Finally, these feature descriptors were fed to the RoF classifier for identifying the potential PPI class. When exploring the proposed model on the Yeast, Human, and Oryza sativa PPIs datasets, it yielded excellent prediction accuracies of 91.93, 96.35, and 94.24%, respectively. In addition, we also conducted numerous experiments on cross-species PPIs datasets, and the predictive capacity of our method is also very excellent. To further access the prediction ability of the proposed approach, we present the comparison of RoF with four powerful classifiers, including Support Vector Machine (SVM), Random Forest (RF), K-nearest Neighbor (KNN), and AdaBoost. We also compared it with some existing superiority works. These comprehensive experimental results further confirm the excellent and feasibility of the proposed approach. In future work, we hope it can be a supplemental tool for the proteomics analysis.

CPIELA: Computational Prediction of Plant Protein-Protein Interactions by Ensemble Learning Approach From Protein Sequences and Evolutionary Information.

Identification and characterization of plant protein–protein interactions (PPIs) are critical in elucidating the functions of proteins and molecular mechanisms in a plant cell. Although experimentally validated plant PPIs data have become increasingly available in diverse plant species, the high-throughput techniques are usually expensive and labor-intensive. With the incredibly valuable plant PPIs data accumulating in public databases, it is progressively important to propose computational approaches to facilitate the identification of possible PPIs. In this article, we propose an effective framework for predicting plant PPIs by combining the position-specific scoring matrix (PSSM), local optimal-oriented pattern (LOOP), and ensemble rotation forest (ROF) model. Specifically, the plant protein sequence is firstly transformed into the PSSM, in which the protein evolutionary information is perfectly preserved. Then, the local textural descriptor LOOP is employed to extract texture variation features from PSSM. Finally, the ROF classifier is adopted to infer the potential plant PPIs. The performance of CPIELA is evaluated via cross-validation on three plant PPIs datasets: Arabidopsis thaliana, Zea mays, and Oryza sativa. The experimental results demonstrate that the CPIELA method achieved the high average prediction accuracies of 98.63%, 98.09%, and 94.02%, respectively. To further verify the high performance of CPIELA, we also compared it with the other state-of-the-art methods on three gold standard datasets. The experimental results illustrate that CPIELA is efficient and reliable for predicting plant PPIs. It is anticipated that the CPIELA approach could become a useful tool for facilitating the identification of possible plant PPIs.

Prediction of Drug-Target Interactions by Combining Dual-Tree Complex Wavelet Transform with Ensemble Learning Method.

Identification of drug–target interactions (DTIs) is vital for drug discovery. However, traditional biological approaches have some unavoidable shortcomings, such as being time consuming and expensive. Therefore, there is an urgent need to develop novel and effective computational methods to predict DTIs in order to shorten the development cycles of new drugs. In this study, we present a novel computational approach to identify DTIs, which uses protein sequence information and the dual-tree complex wavelet transform (DTCWT). More specifically, a position-specific scoring matrix (PSSM) was performed on the target protein sequence to obtain its evolutionary information. Then, DTCWT was used to extract representative features from the PSSM, which were then combined with the drug fingerprint features to form the feature descriptors. Finally, these descriptors were sent to the Rotation Forest (RoF) model for classification. A 5-fold cross validation (CV) was adopted on four datasets (Enzyme, Ion Channel, GPCRs (G-protein-coupled receptors), and NRs (Nuclear Receptors)) to validate the proposed model; our method yielded high average accuracies of 89.21%, 85.49%, 81.02%, and 74.44%, respectively. To further verify the performance of our model, we compared the RoF classifier with two state-of-the-art algorithms: the support vector machine (SVM) and the k-nearest neighbor (KNN) classifier. We also compared it with some other published methods. Moreover, the prediction results for the independent dataset further indicated that our method is effective for predicting potential DTIs. Thus, we believe that our method is suitable for facilitating drug discovery and development.

Robust and accurate prediction of protein\u2013protein interactions by exploiting evolutionary information

Various biochemical functions of organisms are performed by protein–protein interactions (PPIs). Therefore, recognition of protein–protein interactions is very important for understanding most life activities, such as DNA replication and transcription, protein synthesis and secretion, signal transduction and metabolism. Although high-throughput technology makes it possible to generate large-scale PPIs data, it requires expensive cost of both time and labor, and leave a risk of high false positive rate. In order to formulate a more ingenious solution, biology community is looking for computational methods to quickly and efficiently discover massive protein interaction data. In this paper, we propose a computational method for predicting PPIs based on a fresh idea of combining orthogonal locality preserving projections (OLPP) and rotation forest (RoF) models, using protein sequence information. Specifically, the protein sequence is first converted into position-specific scoring matrices (PSSMs) containing protein evolutionary information by using the Position-Specific Iterated Basic Local Alignment Search Tool (PSI-BLAST). Then we characterize a protein as a fixed length feature vector by applying OLPP to PSSMs. Finally, we train an RoF classifier for the purpose of identifying non-interacting and interacting protein pairs. The proposed method yielded a significantly better results than existing methods, with 90.07% and 96.09% prediction accuracy on Yeast and Human datasets. Our experiment show the proposed method can serve as a useful tool to accelerate the process of solving key problems in proteomics.

FWHT-RF: A Novel Computational Approach to Predict Plant Protein-Protein Interactions via an Ensemble Learning Method

Protein-protein interactions (PPIs) in plants are crucial for understanding biological processes. Although high-throughput techniques produced valuable information to identify PPIs in plants, they are usually expensive, inefficient, and extremely time-consuming. Hence, there is an urgent need to develop novel computational methods to predict PPIs in plants. In this article, we proposed a novel approach to predict PPIs in plants only using the information of protein sequences. Specifically, plants’ protein sequences are first converted as position-specific scoring matrix (PSSM); then, the fast Walsh–Hadamard transform (FWHT) algorithm is used to extract feature vectors from PSSM to obtain evolutionary information of plant proteins. Lastly, the rotation forest (RF) classifier is trained for prediction and produced a series of evaluation results. In this work, we named this approach FWHT-RF because FWHT and RF are used for feature extraction and classification, respectively. When applying FWHT-RF on three plants’ PPI datasets Maize, Rice, and Arabidopsis thaliana (Arabidopsis), the average accuracies of FWHT-RF using 5-fold cross validation were achieved as high as 95.20%, 94.42%, and 83.85%, respectively. To further evaluate the predictive power of FWHT-RF, we compared it with the state-of-art support vector machine (SVM) and K-nearest neighbor (KNN) classifier in different aspects. The experimental results demonstrated that FWHT-RF can be a useful supplementary method to predict potential PPIs in plants.

Hybrid ensemble machine learning approaches for landslide susceptibility mapping using different sampling ratios at East Sikkim Himalayan, India

• Landslide susceptibility maps were prepared using new ensemble of RF-Bagging, RF-Random SubSpace (RS) and RF-rotational forest (RF) considering different sample ratios. • The accuracy of the random forest (RF) was increased after made ensemble with the meta classifiers. • The overall proficiency of RS-RF was found substantially greater than other models. • 70:30 sample ratio had the highest good-of-fit and accuracy as per the validation statistics. • Nearly 7.36% of the study area has the very high susceptibility of landslide. Landslide is a big problem in the mountainous region all over the world. Sikkim Himalayan region is also suffering from landslide problem. This study's main objective was to generate landslide susceptibility map (LSM) considering the hybrid ensemble of machine learning approaches using different sample ratios. Random Forest (RF) as the base classifier an ensemble with bagging, Rotation Forest (RTF), and Random Subspace (RS) Meta classifiers were used for spatial landslide modeling. First, collected 86 landslides locations through field investigation and from Sikkim district disaster office were mapped as a landslide inventory. Collected landslide locations were categorized into training and testing datasets randomly using four sample ratios (50:50, 60:40, 70:30 and 80:20). Based on the four sampling ratios and fifteen conditioning factors, a total of sixteen LSMs were prepared using RF, Bagging-RF (B-RF), RTF-RF and RS-RF in GIS platform. For assessing the modeling accuracy and comparison among these, the area under the receiver operating characteristics (AUROC) and other statistical methods such as root-mean-square-error (RMSE), mean-absolute-error (MAE) and R-index methods were used. The overall proficiency of RS-RF (AUC = 0.871, 0.847 of 50%:50%, AUC = 0.925, 0.931 of 60%:40%, AUC = 0.933, 0.939 of 70%:30%; AUC = 0.927, 0.933 of 80%:20%) was found to be substantially greater than the results of RF, B-RF, and RTF-RF. The RS-RF model and 70:30 sample ratio had the highest goodness-of-fit and accuracy as per the RMSE, MAE, and R-index methods. Furthermore, the model based on RS-RF is a promising and acceptable way of mapping regional landslides.

Automatic Detection of Atrial Fibrillation in ECG Using Co-Occurrence Patterns of Dynamic Symbol Assignment and Machine Learning.

Early detection of atrial fibrillation from electrocardiography (ECG) plays a vital role in the timely prevention and diagnosis of cardiovascular diseases. Various algorithms have been proposed; however, they are lacking in considering varied-length signals, morphological transitions, and abnormalities over long-term recordings. We propose dynamic symbolic assignment (DSA) to differentiate a normal sinus rhythm (SR) from paroxysmal atrial fibrillation (PAF). We use ECG signals and their interbeat (RR) intervals from two public databases namely, AF Prediction Challenge Database (AFPDB) and AF Termination Challenge Database (AFTDB). We transform RR intervals into a symbolic representation and compute co-occurrence matrices. The DSA feature is extracted using varied symbol-length , word-size , and applied to five machine learning algorithms for classification. We test five hypotheses: (i) DSA captures the dynamics of the series, (ii) DSA is a reliable technique for various databases, (iii) optimal parameters improve DSA’s performance, (iv) DSA is consistent for variable signal lengths, and (v) DSA supports cross-data analysis. Our method captures the transition patterns of the RR intervals. The DSA feature exhibit a statistically significant difference in SR and PAF conditions (p < 0.005). The DSA feature with and yield maximum performance. In terms of F-measure (F), rotation forest and ensemble learning classifier are the most accurate for AFPDB (F = 94.6%) and AFTDB (F = 99.8%). Our method is effective for short-length signals and supports cross-data analysis. The DSA is capable of capturing the dynamics of varied-lengths ECG signals. Particularly, the optimal parameters-based DSA feature and ensemble learning could help to detect PAF in long-term ECG signals. Our method maps time series into a symbolic representation and identifies abnormalities in noisy, varied-length, and pathological ECG signals.

An Ensemble Learning-Based Method for Inferring Drug-Target Interactions Combining Protein Sequences and Drug Fingerprints.

Identifying the interactions of the drug-target is central to the cognate areas including drug discovery and drug reposition. Although the high-throughput biotechnologies have made tremendous progress, the indispensable clinical trials remain to be expensive, laborious, and intricate. Therefore, a convenient and reliable computer-aided method has become the focus on inferring drug-target interactions (DTIs). In this research, we propose a novel computational model integrating a pyramid histogram of oriented gradients (PHOG), Position-Specific Scoring Matrix (PSSM), and rotation forest (RF) classifier for identifying DTIs. Specifically, protein primary sequences are first converted into PSSMs to describe the potential biological evolution information. After that, PHOG is employed to mine the highly representative features of PSSM from multiple pyramid levels, and the complete describers of drug-target pairs are generated by combining the molecular substructure fingerprints and PHOG features. Finally, we feed the complete describers into the RF classifier for effective prediction. The experiments of 5-fold Cross-Validations (CV) yield mean accuracies of 88.96%, 86.37%, 82.88%, and 76.92% on four golden standard data sets (enzyme, ion channel, G protein-coupled receptors (GPCRs), and nuclear receptor, respectively). Moreover, the paper also conducts the state-of-art light gradient boosting machine (LGBM) and support vector machine (SVM) to further verify the performance of the proposed model. The experimental outcomes substantiate that the established model is feasible and reliable to predict DTIs. There is an excellent prospect that our model is capable of predicting DTIs as an efficient tool on a large scale.

Rotation Forest for Big Data

The Rotation Forest classifier is a successful ensemble method for a wide variety of data mining applications. However, the way in which Rotation Forest transforms the feature space through PCA, although powerful, penalizes training and prediction times, making it unfeasible for Big Data. In this paper, a MapReduce Rotation Forest and its implementation under the Spark framework are presented. The proposed MapReduce Rotation Forest behaves in the same way as the standard Rotation Forest, training the base classifiers on a rotated space, but using a functional implementation of the rotation that enables its execution in Big Data frameworks. Experimental results are obtained using different cloud-based cluster configurations. Bayesian tests are used to validate the method against two ensembles for Big Data: Random Forest and PCARDE classifiers. Our proposal incorporates the parallelization of both the PCA calculation and the tree training, providing a scalable solution that retains the performance of the original Rotation Forest and achieves a competitive execution time (in average, at training, more than 3 times faster than other PCA-based alternatives). In addition, extensive experimentation shows that by setting some parameters of the classifier (i.e., bootstrap sample size, number of trees, and number of rotations), the execution time is reduced with no significant loss of performance using a small ensemble.

Self-Interacting Proteins Prediction from PSSM Based on Evolutionary Information

Self-interacting proteins (SIPs) play an influential role in regulating cell structure and function. Thus, it is critically important to identify whether proteins themselves interact with each other. Although there are some existing experimental methods for self-interaction recognition, the limitations of these methods are both expensive and time-consuming. Therefore, it is very necessary to develop an efficient and stable computational method for predicting SIPs. In this study, we develop an effective computational method for predicting SIPs based on rotation forest (RF) classifier, combined with histogram of oriented gradients (HOG) and synthetic minority oversampling technique (SMOTE). When performing SIPs prediction on yeast and human datasets, the proposed method achieves superior accuracies of 97.28% and 89.41%, respectively. In addition, the proposed approach was compared with the state-of-the-art support vector machine (SVM) classifiers and other different methods on the same datasets. The experimental results demonstrate that our method has good robustness and effectiveness and can be regarded as a useful tool for SIPs prediction.

A Sparse Feature Extraction Method with Elastic Net for Drug-Target Interaction Identification

The identification of drug-target interactions (DTIs) plays a crucial role in drug discovery. However, the traditional high-throughput techniques based on clinical trials are costly, cumbersome, and time-consuming for identifying DTIs. Hence, new intelligent computational methods are urgently needed to surmount these defects in predicting DTIs. In this paper, we propose a novel computational method that combines position-specific scoring matrix (PSSM), elastic net based sparse features extraction, and rotation forest (RF) classifier. Specifically, we converted each protein primary sequence into PSSM, which contains biological evolutionary information. Then we extract the hidden sparse feature descriptors in PSSM by elastic net based sparse feature extraction method (ESFE). After that, we fuse them with the features of drug, which are represented by molecular fingerprints. Finally, rotation forest classifier works on detecting the potential drug-target interactions. When performing the proposed method by the experiments of fivefold cross validation (CV) on enzyme, ion channel, G protein-coupled receptors (GPCRs), and nuclear receptor datasets, this method achieves average accuracies of 90.32%, 88.91%, 80.65%, and 79.73%, respectively. We also compared the proposed model with the state-of-the-art support vector machine (SVM) classifier and other effective methods on the same datasets. The comparison results distinctly indicate that the proposed model possesses the efficient and robust ability to predict DTIs. We expect that the new model will be able to take effects on predicting massive DTIs.

The Drug Design for Diabetes Mellitus type II using Rotation Forest Ensemble Classifier

Abstract Dipeptidyl peptidase-IV (DPP-IV) inhibitor is one of the drug targets for the treatment of diabetes. Some classes of those drugs have dangerous side effects so is critical to develop safer drugs. By using rotation forest methods and in silico, it will be more efficient than conventional methods that require a lot more costs and are more time-consuming. One of in silico methods used in drug design is ligand-based virtual screening (LBVS). The interlocking structure capabilities are identified by the LBVS Process. The fingerprint is one of the structural interpretations. Molecular fingerprints are used as a criterion for LBVS in computational drug discovery. A circular fingerprint is found to improve LBVS performance. In this paper, we used the representation of ECFP and FCFP as a method to extract features, after which we used a Rotation Forest classifier to predict active and inactive compounds. The experiment result shows Rotation Forest has good prediction based on the different circular fingerprint and can successfully better classify with results of MCC being 85% and accuracy 92%.

Prediction of lncRNA-disease associations via an embedding learning HOPE in heterogeneous information networks

Uncovering additional long non-coding RNA (lncRNA)-disease associations has become increasingly important for developing treatments for complex human diseases. Identification of lncRNA biomarkers and lncRNA-disease associations is central to diagnoses and treatment. However, traditional experimental methods are expensive and time-consuming. Enormous amounts of data present in public biological databases are available for computational methods used to predict lncRNA-disease associations. In this study, we propose a novel computational method to predict lncRNA-disease associations. More specifically, a heterogeneous network is first constructed by integrating the associations among microRNA (miRNA), lncRNA, protein, drug, and disease, Second, high-order proximity preserved embedding (HOPE) was used to embed nodes into a network. Finally, the rotation forest classifier was adopted to train the prediction model. In the 5-fold cross-validation experiment, the area under the curve (AUC) of our method achieved 0.8328 ± 0.0236. We compare it with the other four classifiers, in which the proposed method remarkably outperformed other comparison methods. Otherwise, we constructed three case studies for three excess death rate cancers, respectively. The results show that 9 (lung cancer, gastric cancer, and hepatocellular carcinomas) out of the top 15 predicted disease-related lncRNAs were confirmed by our method. In conclusion, our method could predict the unknown lncRNA-disease associations effectively.

IMS-CDA: Prediction of CircRNA-Disease Associations From the Integration of Multisource Similarity Information With Deep Stacked Autoencoder Model.

Emerging evidence indicates that circular RNA (circRNA) has been an indispensable role in the pathogenesis of human complex diseases and many critical biological processes. Using circRNA as a molecular marker or therapeutic target opens up a new avenue for our treatment and detection of human complex diseases. The traditional biological experiments, however, are usually limited to small scale and are time consuming, so the development of an effective and feasible computational-based approach for predicting circRNA-disease associations is increasingly favored. In this study, we propose a new computational-based method, called IMS-CDA, to predict potential circRNA-disease associations based on multisource biological information. More specifically, IMS-CDA combines the information from the disease semantic similarity, the Jaccard and Gaussian interaction profile kernel similarity of disease and circRNA, and extracts the hidden features using the stacked autoencoder (SAE) algorithm of deep learning. After training in the rotation forest (RF) classifier, IMS-CDA achieves 88.08% area under the ROC curve with 88.36% accuracy at the sensitivity of 91.38% on the CIRCR2Disease dataset. Compared with the state-of-the-art support vector machine and K -nearest neighbor models and different descriptor models, IMS-CDA achieves the best overall performance. In the case studies, eight of the top 15 circRNA-disease associations with the highest prediction score were confirmed by recent literature. These results indicated that IMS-CDA has an outstanding ability to predict new circRNA-disease associations and can provide reliable candidates for biological experiments.

Comparison of Ensemble Machine Learning Methods for Automated Classification of Focal and Non-Focal Epileptic EEG Signals

This research presents the epileptic focus region localization during epileptic seizures by applying different signal processing and ensemble machine learning techniques in intracranial recordings of electroencephalogram (EEG). Multi-scale Principal Component Analysis (MSPCA) is used for denoising EEG signals and the autoregressive (AR) algorithm will extract useful features from the EEG signal. The performances of the ensemble machine learning methods are measured with accuracy, F-measure, and the area under the receiver operating characteristic (ROC) curve (AUC). EEG-based focus area localization with the proposed methods reaches 98.9% accuracy using the Rotation Forest classifier. Therefore, our results suggest that ensemble machine learning methods can be applied to differentiate the EEG signals from epileptogenic brain areas and signals recorded from non-epileptogenic brain regions with high accuracy.