A novel hybrid features grey incidence model and its application in identifying key factors influencing air pollution in Jiangsu Province.

Accurately assessing the key factors influencing air pollution is crucial for effective air pollution control. To address this need, we propose a novel Hybrid Features Grey Incidence Model (HFGIM), which integrates geometric feature differences from both proximity and similarity perspectives. Firstly, we extract geometric feature difference vectors of proximity and similarity from time series data and measure the overall feature difference degree by calculating vector norms. Secondly, we calculate the relative feature differences and information contribution rates of proximity and similarity to derive the hybrid feature differences coefficient between sequences, thereby obtaining the hybrid features incidence degree. After detailing the model's properties and modeling steps, we introduce the Cross-sectional Data Hybrid Features Grey Incidence Model (C-HFGIM) and the Panel Data Hybrid Features Grey Incidence Model (P-HFGIM) for handling cross-sectional and panel data, respectively. Applying HFGIM, we identified the key pollutants and primary pollution source indicators of air pollution in Jiangsu Province. We also compared HFGIM with other classical grey incidence models to verify the proposed model's effectiveness. Based on the analysis results, we propose policy recommendations for air pollution control in Jiangsu Province.

MTFN: multi-temporal feature fusing network with co-attention for DCE-MRI synthesis.

BACKGROUND: Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) plays an important role in the diagnosis and treatment of breast cancer. However, obtaining complete eight temporal images of DCE-MRI requires a long scanning time, which causes patients' discomfort in the scanning process. Therefore, to reduce the time, the multi temporal feature fusing neural network with Co-attention (MTFN) is proposed to generate the eighth temporal images of DCE-MRI, which enables the acquisition of DCE-MRI images without scanning. In order to reduce the time, multi-temporal feature fusion cooperative attention mechanism neural network (MTFN) is proposed to generate the eighth temporal images of DCE-MRI, which enables DCE-MRI image acquisition without scanning. METHODS: In this paper, we propose multi temporal feature fusing neural network with Co-attention (MTFN) for DCE-MRI Synthesis, in which the Co-attention module can fully fuse the features of the first and third temporal image to obtain the hybrid features. The Co-attention explore long-range dependencies, not just relationships between pixels. Therefore, the hybrid features are more helpful to generate the eighth temporal images. RESULTS: We conduct experiments on the private breast DCE-MRI dataset from hospitals and the multi modal Brain Tumor Segmentation Challenge2018 dataset (BraTs2018). Compared with existing methods, the experimental results of our method show the improvement and our method can generate more realistic images. In the meanwhile, we also use synthetic images to classify the molecular typing of breast cancer that the accuracy on the original eighth time-series images and the generated images are 89.53% and 92.46%, which have been improved by about 3%, and the classification results verify the practicability of the synthetic images. CONCLUSIONS: The results of subjective evaluation and objective image quality evaluation indicators show the effectiveness of our method, which can obtain comprehensive and useful information. The improvement of classification accuracy proves that the images generated by our method are practical.

Health Status Recognition Method for Rotating Machinery Based on Multi-Scale Hybrid Features and Improved Convolutional Neural Networks.

Rotating machinery is susceptible to harsh environmental interference, and fault signal features are challenging to extract, leading to difficulties in health status recognition. This paper proposes multi-scale hybrid features and improved convolutional neural networks (MSCCNN) health status identification methods for rotating machinery. Firstly, the rotating machinery vibration signal is decomposed into intrinsic modal components (IMF) using empirical wavelet decomposition, and multi-scale hybrid feature sets are constructed by simultaneously extracting time-domain, frequency-domain and time-frequency-domain features based on the original vibration signal and the intrinsic modal components it decomposes. Secondly, using correlation coefficients to select features sensitive to degradation, construct rotating machinery health indicators based on kernel principal component analysis and complete health state classification. Finally, a convolutional neural network model (MSCCNN) incorporating multi-scale convolution and hybrid attention mechanism modules is developed for health state identification of rotating machinery, and an improved custom loss function is applied to improve the superiority and generalization ability of the model. The bearing degradation data set of Xi'an Jiaotong University is used to verify the effectiveness of the model. The recognition accuracy of the model is 98.22%, which is 5.83%, 3.30%, 2.29%, 1.52%, and 4.31% higher than that of SVM, CNN, CNN + CBAM, MSCNN, and MSCCNN + conventional features, respectively. The PHM2012 challenge dataset is used to increase the number of samples to validate the model effectiveness, and the model recognition accuracy is 97.67%, which is 5.63%, 1.88%, 1.36%, 1.49%, and 3.69% higher compared to SVM, CNN, CNN + CBAM, MSCNN, and MSCCNN + conventional features methods, respectively. The MSCCNN model recognition accuracy is 98.67% when validated on the degraded dataset of the reducer platform.

Robust Epileptic Seizure Detection Using Long Short-Term Memory and Feature Fusion of Compressed Time-Frequency EEG Images.

Epilepsy is a prevalent neurological disorder with considerable risks, including physical impairment and irreversible brain damage from seizures. Given these challenges, the urgency for prompt and accurate seizure detection cannot be overstated. Traditionally, experts have relied on manual EEG signal analyses for seizure detection, which is labor-intensive and prone to human error. Recognizing this limitation, the rise in deep learning methods has been heralded as a promising avenue, offering more refined diagnostic precision. On the other hand, the prevailing challenge in many models is their constrained emphasis on specific domains, potentially diminishing their robustness and precision in complex real-world environments. This paper presents a novel model that seamlessly integrates the salient features from the time-frequency domain along with pivotal statistical attributes derived from EEG signals. This fusion process involves the integration of essential statistics, including the mean, median, and variance, combined with the rich data from compressed time-frequency (CWT) images processed using autoencoders. This multidimensional feature set provides a robust foundation for subsequent analytic steps. A long short-term memory (LSTM) network, meticulously optimized for the renowned Bonn Epilepsy dataset, was used to enhance the capability of the proposed model. Preliminary evaluations underscore the prowess of the proposed model: a remarkable 100% accuracy in most of the binary classifications, exceeding 95% accuracy in three-class and four-class challenges, and a commendable rate, exceeding 93.5% for the five-class classification.

iTTCA-RF: a random forest predictor for tumor T cell antigens.

BACKGROUND: Cancer is one of the most serious diseases threatening human health. Cancer immunotherapy represents the most promising treatment strategy due to its high efficacy and selectivity and lower side effects compared with traditional treatment. The identification of tumor T cell antigens is one of the most important tasks for antitumor vaccines development and molecular function investigation. Although several machine learning predictors have been developed to identify tumor T cell antigen, more accurate tumor T cell antigen identification by existing methodology is still challenging. METHODS: In this study, we used a non-redundant dataset of 592 tumor T cell antigens (positive samples) and 393 tumor T cell antigens (negative samples). Four types feature encoding methods have been studied to build an efficient predictor, including amino acid composition, global protein sequence descriptors and grouped amino acid and peptide composition. To improve the feature representation ability of the hybrid features, we further employed a two-step feature selection technique to search for the optimal feature subset. The final prediction model was constructed using random forest algorithm. RESULTS: Finally, the top 263 informative features were selected to train the random forest classifier for detecting tumor T cell antigen peptides. iTTCA-RF provides satisfactory performance, with balanced accuracy, specificity and sensitivity values of 83.71%, 78.73% and 88.69% over tenfold cross-validation as well as 73.14%, 62.67% and 83.61% over independent tests, respectively. The online prediction server was freely accessible at http://lab.malab.cn/~acy/iTTCA . CONCLUSIONS: We have proven that the proposed predictor iTTCA-RF is superior to the other latest models, and will hopefully become an effective and useful tool for identifying tumor T cell antigens presented in the context of major histocompatibility complex class I.

[Psychosis speech recognition algorithm based on deep embedded sparse stacked autoencoder and manifold ensemble].

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.

Machine Learning Based Automated Segmentation and Hybrid Feature Analysis for Diabetic Retinopathy Classification Using Fundus Image.

The object of this study was to demonstrate the ability of machine learning (ML) methods for the segmentation and classification of diabetic retinopathy (DR). Two-dimensional (2D) retinal fundus (RF) images were used. The datasets of DR-that is, the mild, moderate, non-proliferative, proliferative, and normal human eye ones-were acquired from 500 patients at Bahawal Victoria Hospital (BVH), Bahawalpur, Pakistan. Five hundred RF datasets (sized 256 × 256) for each DR stage and a total of 2500 (500 × 5) datasets of the five DR stages were acquired. This research introduces the novel clustering-based automated region growing framework. For texture analysis, four types of features-histogram (H), wavelet (W), co-occurrence matrix (COM) and run-length matrix (RLM)-were extracted, and various ML classifiers were employed, achieving 77.67%, 80%, 89.87%, and 96.33% classification accuracies, respectively. To improve classification accuracy, a fused hybrid-feature dataset was generated by applying the data fusion approach. From each image, 245 pieces of hybrid feature data (H, W, COM, and RLM) were observed, while 13 optimized features were selected after applying four different feature selection techniques, namely Fisher, correlation-based feature selection, mutual information, and probability of error plus average correlation. Five ML classifiers named sequential minimal optimization (SMO), logistic (Lg), multi-layer perceptron (MLP), logistic model tree (LMT), and simple logistic (SLg) were deployed on selected optimized features (using 10-fold cross-validation), and they showed considerably high classification accuracies of 98.53%, 99%, 99.66%, 99.73%, and 99.73%, respectively.

KELM-CPPpred: Kernel Extreme Learning Machine Based Prediction Model for Cell-Penetrating Peptides.

Cell-penetrating peptides (CPPs) facilitate the transport of pharmacologically active molecules, such as plasmid DNA, short interfering RNA, nanoparticles, and small peptides. The accurate identification of new and unique CPPs is the initial step to gain insight into CPP activity. Experiments can provide detailed insight into the cell-penetration property of CPPs. However, the synthesis and identification of CPPs through wet-lab experiments is both resource- and time-expensive. Therefore, the development of an efficient prediction tool is essential for the identification of unique CPP prior to experiments. To this end, we developed a kernel extreme learning machine (KELM) based CPP prediction model called KELM-CPPpred. The main data set used in this study consists of 408 CPPs and an equal number of non-CPPs. The input features, used to train the proposed prediction model, include amino acid composition, dipeptide amino acid composition, pseudo amino acid composition, and the motif-based hybrid features. We further used an independent data set to validate the proposed model. In addition, we have also tested the prediction accuracy of KELM-CPPpred models with the existing artificial neural network (ANN), random forest (RF), and support vector machine (SVM) approaches on respective benchmark data sets used in the previous studies. Empirical tests showed that KELM-CPPpred outperformed existing prediction approaches based on SVM, RF, and ANN. We developed a web interface named KELM-CPPpred, which is freely available at http://sairam.people.iitgn.ac.in/KELM-CPPpred.html.

A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis.

Bearing fault diagnosis is imperative for the maintenance, reliability, and durability of rotary machines. It can reduce economical losses by eliminating unexpected downtime in industry due to failure of rotary machines. Though widely investigated in the past couple of decades, continued advancement is still desirable to improve upon existing fault diagnosis techniques. Vibration acceleration signals collected from machine bearings exhibit nonstationary behavior due to variable working conditions and multiple fault severities. In the current work, a two-layered bearing fault diagnosis scheme is proposed for the identification of fault pattern and crack size for a given fault type. A hybrid feature pool is used in combination with sparse stacked autoencoder (SAE)-based deep neural networks (DNNs) to perform effective diagnosis of bearing faults of multiple severities. The hybrid feature pool can extract more discriminating information from the raw vibration signals, to overcome the nonstationary behavior of the signals caused by multiple crack sizes. More discriminating information helps the subsequent classifier to effectively classify data into the respective classes. The results indicate that the proposed scheme provides satisfactory performance in diagnosing bearing defects of multiple severities. Moreover, the results also demonstrate that the proposed model outperforms other state-of-the-art algorithms, i.e., support vector machines (SVMs) and backpropagation neural networks (BPNNs).

Prediction of aptamer-protein interacting pairs using an ensemble classifier in combination with various protein sequence attributes.

BACKGROUND: Aptamer-protein interacting pairs play a variety of physiological functions and therapeutic potentials in organisms. Rapidly and effectively predicting aptamer-protein interacting pairs is significant to design aptamers binding to certain interested proteins, which will give insight into understanding mechanisms of aptamer-protein interacting pairs and developing aptamer-based therapies. RESULTS: In this study, an ensemble method is presented to predict aptamer-protein interacting pairs with hybrid features. The features for aptamers are extracted from Pseudo K-tuple Nucleotide Composition (PseKNC) while the features for proteins incorporate Discrete Cosine Transformation (DCT), disorder information, and bi-gram Position Specific Scoring Matrix (PSSM). We investigate predictive capabilities of various feature spaces. The proposed ensemble method obtains the best performance with Youden's Index of 0.380, using the hybrid feature space of PseKNC, DCT, bi-gram PSSM, and disorder information by 10-fold cross validation. The Relief-Incremental Feature Selection (IFS) method is adopted to obtain the optimal feature set. Based on the optimal feature set, the proposed method achieves a balanced performance with a sensitivity of 0.753 and a specificity of 0.725 on the training dataset, which indicates that this method can solve the imbalanced data problem effectively. To evaluate the prediction performance objectively, an independent testing dataset is used to evaluate the proposed method. Encouragingly, our proposed method performs better than previous study with a sensitivity of 0.738 and a Youden's Index of 0.451. CONCLUSIONS: These results suggest that the proposed method can be a potential candidate for aptamer-protein interacting pair prediction, which may contribute to finding novel aptamer-protein interacting pairs and understanding the relationship between aptamers and proteins.

Using the SMOTE technique and hybrid features to predict the types of ion channel-targeted conotoxins.

Conotoxins targeting different ion channels play distinct physiological functions and therapeutic potentials in organisms. Accurate identification of types of ion channel-targeted conotoxins will provide significant clues to reveal the physiological mechanism and pharmacological therapeutic potential of conotoxins. In this study, a random forest based predictor called ICTCPred for the types of ion channel-targeted conotoxin prediction is proposed with hybrid features incorporating CTD (Composition, Transition, and Distribution), g-Gap DC (g-Gap Dipeptide Composition), PP (Physicochemical Properties), and SSI (Secondary Structure Information). To deal with the imbalanced benchmark dataset, the SMOTE Technique (Synthetic Minority Over-sampling Technique) is applied. Based on the above-mentioned individual feature spaces, the average accuracy of ICTCPred lies in the range of 0.729-0.886, indicating the discriminative power of these features. In addition, ICTCPred yields the highest average accuracy of 0.895 using the hybrid feature space of CTD, g-Gap DC, PP and SSI. The Relief-IFS (Incremental Feature Selection) method is adopted to further improve the prediction performance of ICTCPred. Based on the training dataset, ICTCPred achieves satisfactory performance with an average accuracy of 0.910. To evaluate the prediction performance objectively, ICTCPred is compared with previous studies on the same independent testing dataset. Encouragingly, our proposed method performs better than previous studies to identify types of ion channel-targeted conotoxins, with the highest sensitivity of 0.919 for Na(+)-targeted conotoxins, the highest sensitivity of 1 for K(+)-targeted conotoxins, and the highest sensitivity of 1 for Ca(2+)-targeted conotoxins. It is anticipated that ICTCPred can be a potential candidate for the ion channel-targeted conotoxin prediction.

An Ensemble Method to Distinguish Bacteriophage Virion from Non-Virion Proteins Based on Protein Sequence Characteristics.

Bacteriophage virion proteins and non-virion proteins have distinct functions in biological processes, such as specificity determination for host bacteria, bacteriophage replication and transcription. Accurate identification of bacteriophage virion proteins from bacteriophage protein sequences is significant to understand the complex virulence mechanism in host bacteria and the influence of bacteriophages on the development of antibacterial drugs. In this study, an ensemble method for bacteriophage virion protein prediction from bacteriophage protein sequences is put forward with hybrid feature spaces incorporating CTD (composition, transition and distribution), bi-profile Bayes, PseAAC (pseudo-amino acid composition) and PSSM (position-specific scoring matrix). When performing on the training dataset 10-fold cross-validation, the presented method achieves a satisfactory prediction result with a sensitivity of 0.870, a specificity of 0.830, an accuracy of 0.850 and Matthew's correlation coefficient (MCC) of 0.701, respectively. To evaluate the prediction performance objectively, an independent testing dataset is used to evaluate the proposed method. Encouragingly, our proposed method performs better than previous studies with a sensitivity of 0.853, a specificity of 0.815, an accuracy of 0.831 and MCC of 0.662 on the independent testing dataset. These results suggest that the proposed method can be a potential candidate for bacteriophage virion protein prediction, which may provide a useful tool to find novel antibacterial drugs and to understand the relationship between bacteriophage and host bacteria. For the convenience of the vast majority of experimental Int. J. Mol. Sci. 2015, 16,21735 scientists, a user-friendly and publicly-accessible web-server for the proposed ensemble method is established.

A progressive framework for tooth and substructure segmentation from cone-beam CT images.

BACKGROUND: Accurate segmentation of individual tooth and their substructures including enamel, pulp, and dentin from cone-beam computed tomography (CBCT) images is essential for dental diagnosis and treatment planning in digital dentistry. Existing methods for tooth segmentation based on CBCT images have achieved substantial progress; however, techniques for further segmentation into substructures are yet to be developed. PURPOSE: We aim to propose a novel three-stage progressive deep-learning-based framework for automatically segmenting 3D tooth from CBCT images, focusing on finer substructures, i.e., enamel, pulp, and dentin. METHODS: In this paper, we first detect each tooth using its centroid by a clustering scheme, which efficiently determines each tooth detection by applying learned displacement vectors from the foreground tooth region. Next, guided by the detected centroid, each tooth proposal, combined with the corresponding tooth map, is processed through our tooth segmentation network. We also present an attention-based hybrid feature fusion mechanism, which provides intricate details of the tooth boundary while maintaining the global tooth shape, thereby enhancing the segmentation process. Additionally, we utilize the skeleton of the tooth as a guide for subsequent substructure segmentation. RESULTS: Our algorithm is extensively evaluated on a collected dataset of 314 patients, and the extensive comparison and ablation studies demonstrate superior segmentation results of our approach. CONCLUSIONS: Our proposed method can automatically segment tooth and finer substructures from CBCT images, underlining its potential applicability for clinical diagnosis and surgical treatment.

Auscultation-Based Pulmonary Disease Detection through Parallel Transformation and Deep Learning.

Respiratory diseases are among the leading causes of death, with many individuals in a population frequently affected by various types of pulmonary disorders. Early diagnosis and patient monitoring (traditionally involving lung auscultation) are essential for the effective management of respiratory diseases. However, the interpretation of lung sounds is a subjective and labor-intensive process that demands considerable medical expertise, and there is a good chance of misclassification. To address this problem, we propose a hybrid deep learning technique that incorporates signal processing techniques. Parallel transformation is applied to adventitious respiratory sounds, transforming lung sound signals into two distinct time-frequency scalograms: the continuous wavelet transform and the mel spectrogram. Furthermore, parallel convolutional autoencoders are employed to extract features from scalograms, and the resulting latent space features are fused into a hybrid feature pool. Finally, leveraging a long short-term memory model, a feature from the latent space is used as input for classifying various types of respiratory diseases. Our work is evaluated using the ICBHI-2017 lung sound dataset. The experimental findings indicate that our proposed method achieves promising predictive performance, with average values for accuracy, sensitivity, specificity, and F1-score of 94.16%, 89.56%, 99.10%, and 89.56%, respectively, for eight-class respiratory diseases; 79.61%, 78.55%, 92.49%, and 78.67%, respectively, for four-class diseases; and 85.61%, 83.44%, 83.44%, and 84.21%, respectively, for binary-class (normal vs. abnormal) lung sounds.

Hybrid Methods for Fundus Image Analysis for Diagnosis of Diabetic Retinopathy Development Stages Based on Fusion Features.

Diabetic retinopathy (DR) is a complication of diabetes that damages the delicate blood vessels of the retina and leads to blindness. Ophthalmologists rely on diagnosing the retina by imaging the fundus. The process takes a long time and needs skilled doctors to diagnose and determine the stage of DR. Therefore, automatic techniques using artificial intelligence play an important role in analyzing fundus images for the detection of the stages of DR development. However, diagnosis using artificial intelligence techniques is a difficult task and passes through many stages, and the extraction of representative features is important in reaching satisfactory results. Convolutional Neural Network (CNN) models play an important and distinct role in extracting features with high accuracy. In this study, fundus images were used for the detection of the developmental stages of DR by two proposed methods, each with two systems. The first proposed method uses GoogLeNet with SVM and ResNet-18 with SVM. The second method uses Feed-Forward Neural Networks (FFNN) based on the hybrid features extracted by first using GoogLeNet, Fuzzy color histogram (FCH), Gray Level Co-occurrence Matrix (GLCM), and Local Binary Pattern (LBP); followed by ResNet-18, FCH, GLCM and LBP. All the proposed methods obtained superior results. The FFNN network with hybrid features of ResNet-18, FCH, GLCM, and LBP obtained 99.7% accuracy, 99.6% precision, 99.6% sensitivity, 100% specificity, and 99.86% AUC.

HC-Net: A hybrid convolutional network for non-human primate brain extraction.

Brain extraction (skull stripping) is an essential step in the magnetic resonance imaging (MRI) analysis of brain sciences. However, most of the current brain extraction methods that achieve satisfactory results for human brains are often challenged by non-human primate brains. Due to the small sample characteristics and the nature of thick-slice scanning of macaque MRI data, traditional deep convolutional neural networks (DCNNs) are unable to obtain excellent results. To overcome this challenge, this study proposed a symmetrical end-to-end trainable hybrid convolutional neural network (HC-Net). It makes full use of the spatial information between adjacent slices of the MRI image sequence and combines three consecutive slices from three axes for 3D convolutions, which reduces the calculation consumption and promotes accuracy. The HC-Net consists of encoding and decoding structures of 3D convolutions and 2D convolutions in series. The effective use of 2D convolutions and 3D convolutions relieves the underfitting of 2D convolutions to spatial features and the overfitting of 3D convolutions to small samples. After evaluating macaque brain data from different sites, the results showed that HC-Net performed better in inference time (approximately 13 s per volume) and accuracy (mean Dice coefficient reached 95.46%). The HC-Net model also had good generalization ability and stability in different modes of brain extraction tasks.

A deep learning-based model using hybrid feature extraction approach for consumer sentiment analysis.

There is an exponential growth in textual content generation every day in today's world. In-app messaging such as Telegram and WhatsApp, social media websites such as Instagram and Facebook, e-commerce websites like Amazon, Google searches, news publishing websites, and a variety of additional sources are the possible suppliers. Every instant, all these sources produce massive amounts of text data. The interpretation of such data can help business owners analyze the social outlook of their product, brand, or service and take necessary steps. The development of a consumer review summarization model using Natural Language Processing (NLP) techniques and Long short-term memory (LSTM) to present summarized data and help businesses obtain substantial insights into their consumers' behavior and choices is the topic of this research. A hybrid approach for analyzing sentiments is presented in this paper. The process comprises pre-processing, feature extraction, and sentiment classification. Using NLP techniques, the pre-processing stage eliminates the undesirable data from input text reviews. For extracting the features effectively, a hybrid method comprising review-related features and aspect-related features has been introduced for constructing the distinctive hybrid feature vector corresponding to each review. The sentiment classification is performed using the deep learning classifier LSTM. We experimentally evaluated the proposed model using three different research datasets. The model achieves the average precision, average recall, and average F1-score of 94.46%, 91.63%, and 92.81%, respectively.

CD8TCEI-EukPath: A Novel Predictor to Rapidly Identify CD8+ T-Cell Epitopes of Eukaryotic Pathogens Using a Hybrid Feature Selection Approach.

Computational prediction to screen potential vaccine candidates has been proven to be a reliable way to provide guarantees for vaccine discovery in infectious diseases. As an important class of organisms causing infectious diseases, pathogenic eukaryotes (such as parasitic protozoans) have evolved the ability to colonize a wide range of hosts, including humans and animals; meanwhile, protective vaccines are urgently needed. Inspired by the immunological idea that pathogen-derived epitopes are able to mediate the CD8+ T-cell-related host adaptive immune response and with the available positive and negative CD8+ T-cell epitopes (TCEs), we proposed a novel predictor called CD8TCEI-EukPath to detect CD8+ TCEs of eukaryotic pathogens. Our method integrated multiple amino acid sequence-based hybrid features, employed a well-established feature selection technique, and eventually built an efficient machine learning classifier to differentiate CD8+ TCEs from non-CD8+ TCEs. Based on the feature selection results, 520 optimal hybrid features were used for modeling by utilizing the LightGBM algorithm. CD8TCEI-EukPath achieved impressive performance, with an accuracy of 79.255% in ten-fold cross-validation and an accuracy of 78.169% in the independent test. Collectively, CD8TCEI-EukPath will contribute to rapidly screening epitope-based vaccine candidates, particularly from large peptide-coding datasets. To conduct the prediction of CD8+ TCEs conveniently, an online web server is freely accessible (http://lab.malab.cn/∼hrs/CD8TCEI-EukPath/).

A framework for in-vivo human brain tumor detection using image augmentation and hybrid features.

Brain tumor is caused by the uncontrolled and accelerated multiplication of cells in the brain. If not treated early enough, it can lead to death. Despite multiple significant efforts and promising research outcomes, accurate segmentation and classification of tumors remain a challenge. The changes in tumor location, shape, and size make brain tumor identification extremely difficult. An Extreme Gradient Boosting (XGBoost) algorithm using is proposed in this work to classify four subtypes of brain tumor-normal, gliomas, meningiomas, and pituitary tumors. Because the dataset was limited in size, image augmentation using a conditional Generative Adversarial Network (cGAN) was used to expand the training data. Deep features, Two-Dimensional Fractional Fourier Transform (2D-FrFT) features, and geometric features are fused together to extract both global and local information from the Magnetic Resonance Imaging (MRI) scans. The model attained enhanced performance with a classification accuracy of 98.79% and sensitivity of 98.77% for the test images. In comparison to state-of-the-art algorithms employing the Kaggle brain tumor dataset, the suggested model showed a considerable improvement. The improved results show the prominence of feature fusion and establish XGBoost as an appropriate classifier for brain tumor detection in terms on testing accuracy, sensitivity and Area under receiver operating characteristic (AUROC) curve.

SortPred: The first machine learning based predictor to identify bacterial sortases and their classes using sequence-derived information.

Sortase enzymes are cysteine transpeptidases that embellish the surface of Gram-positive bacteria with various proteins thereby allowing these microorganisms to interact with their neighboring environment. It is known that several of their substrates can cause pathological implications, so researchers have focused on the development of sortase inhibitors. Currently, six different classes of sortases (A-F) are recognized. However, with the extensive application of bacterial genome sequencing projects, the number of potential sortases in the public databases has exploded, presenting considerable challenges in annotating these sequences. It is very laborious and time-consuming to characterize these sortase classes experimentally. Therefore, this study developed the first machine-learning-based two-layer predictor called SortPred, where the first layer predicts the sortase from the given sequence and the second layer predicts their class from the predicted sortase. To develop SortPred, we constructed an original benchmarking dataset and investigated 31 feature descriptors, primarily on five feature encoding algorithms. Afterward, each of these descriptors were trained using a random forest classifier and their robustness was evaluated with an independent dataset. Finally, we selected the final model independently for both layers depending on the performance consistency between cross-validation and independent evaluation. SortPred is expected to be an effective tool for identifying bacterial sortases, which in turn may aid in designing sortase inhibitors and exploring their functions. The SortPred webserver and a standalone version are freely accessible at: https://procarb.org/sortpred.