Predicting lncRNA-protein interactions through deep learning framework employing multiple features and random forest algorithm.

RNA-protein interaction (RPI) is crucial to the life processes of diverse organisms. Various researchers have identified RPI through long-term and high-cost biological experiments. Although numerous machine learning and deep learning-based methods for predicting RPI currently exist, their robustness and generalizability have significant room for improvement. This study proposes LPI-MFF, an RPI prediction model based on multi-source information fusion, to address these issues. The LPI-MFF employed protein-protein interactions features, sequence features, secondary structure features, and physical and chemical properties as the information sources with the corresponding coding scheme, followed by the random forest algorithm for feature screening. Finally, all information was combined and a classification method based on convolutional neural networks is used. The experimental results of fivefold cross-validation demonstrated that the accuracy of LPI-MFF on RPI1807 and NPInter was 97.60% and 97.67%, respectively. In addition, the accuracy rate on the independent test set RPI1168 was 84.9%, and the accuracy rate on the Mus musculus dataset was 90.91%. Accordingly, LPI-MFF demonstrated greater robustness and generalization than other prevalent RPI prediction methods.

Double-Layer Detection Model of Malicious PDF Documents Based on Entropy Method with Multiple Features.

Traditional PDF document detection technology usually builds a rule or feature library for specific vulnerabilities and therefore is only fit for single detection targets and lacks anti-detection ability. To address these shortcomings, we build a double-layer detection model for malicious PDF documents based on an entropy method with multiple features. First, we address the single detection target problem with the fusion of 222 multiple features, including 130 basic features (such as objects, structure, content stream, metadata, etc.) and 82 dangerous features (such as suspicious and encoding function, etc.), which can effectively resist obfuscation and encryption. Second, we generate the best set of features (a total of 153) by creatively applying an entropy method based on RReliefF and MIC (EMBORAM) to PDF samples with 37 typical document vulnerabilities, which can effectively resist anti-detection methods, such as filling data and imitation attacks. Finally, we build a double-layer processing framework to detect samples efficiently through the AdaBoost-optimized random forest algorithm and the robustness-optimized support vector machine algorithm. Compared to the traditional static detection method, this model performs better for various evaluation criteria. The average time of document detection is 1.3 ms, while the accuracy rate reaches 95.9%.

CNN-DDI: a learning-based method for predicting drug-drug interactions using convolution neural networks.

BACKGROUND: Drug-drug interactions (DDIs) are the reactions between drugs. They are compartmentalized into three types: synergistic, antagonistic and no reaction. As a rapidly developing technology, predicting DDIs-associated events is getting more and more attention and application in drug development and disease diagnosis fields. In this work, we study not only whether the two drugs interact, but also specific interaction types. And we propose a learning-based method using convolution neural networks to learn feature representations and predict DDIs. RESULTS: In this paper, we proposed a novel algorithm using a CNN architecture, named CNN-DDI, to predict drug-drug interactions. First, we extract feature interactions from drug categories, targets, pathways and enzymes as feature vectors and employ the Jaccard similarity as the measurement of drugs similarity. Then, based on the representation of features, we build a new convolution neural network as the DDIs' predictor. CONCLUSION: The experimental results indicate that drug categories is effective as a new feature type applied to CNN-DDI method. And using multiple features is more informative and more effective than single feature. It can be concluded that CNN-DDI has more superiority than other existing algorithms on task of predicting DDIs.

Identification of DNA N4-methylcytosine sites based on multi-source features and gradient boosting decision tree.

N4-methylcytosine (4 mC) is an important and common methylation which widely exists in prokaryotes. It plays a crucial role in correcting DNA replication errors and protecting host DNA against degradation by restrictive enzymes. Hence, the accurate identification for 4 mC sites is greatly significant for understanding biological functions and treating gene diseases. In this paper, a novel model is designed for identifying 4 mC sites. Firstly, we extract features from original sequences by multi-source feature representation methods, which are mono-nucleotide binary and k-mer frequency, dinucleotide binary and position-specific frequency, ring-function-hydrogen-chemical properties, dinucleotide-based DNA properties and trinucleotide-based DNA properties. Subsequently, gradient boosting decision tree is applied to select the optimal feature set and remove redundant information. Finally, support vector machine is employed to predict 4 mC or non-4mC sites. The accuracies of six datasets reach 0.851, 0.859, 0.801, 0.87, 0.859 and 0.901, respectively, which are superior to previous prediction methods. Therefore, the results show that our predictor is a feasible and effective tool for identifying 4 mC sites. Furthermore, an online web server is established at http://dnan4c.zhanglab.site.

Online Boosting-Based Target Identification among Similar Appearance for Person-Following Robots.

It is challenging for a mobile robot to follow a specific target person in a dynamic environment, comprising people wearing similar-colored clothes and having the same or similar height. This study describes a novel framework for a person identification model that identifies a target person by merging multiple features into a single joint feature online. The proposed framework exploits the deep learning output to extract four features for tracking the target person without prior knowledge making it generalizable and more robust. A modified intersection over union between the current frame and the last frame is proposed as a feature to distinguish people, in addition to color, height, and location. To improve the performance of target identification in a dynamic environment, an online boosting method was adapted by continuously updating the features in every frame. Through extensive real-life experiments, the effectiveness of the proposed method was demonstrated by showing experimental results that it outperformed the previous methods.

Non-Destructive Detection of Strawberry Quality Using Multi-Features of Hyperspectral Imaging and Multivariate Methods.

Soluble solid content (SSC), pH, and vitamin C (VC) are considered as key parameters for strawberry quality. Spectral, color, and textural features from hyperspectral reflectance imaging of 400-1000 nm was to develop the non-destructive detection approaches for SSC, pH, and VC of strawberries by integrating various multivariate methods as partial least-squares regression (PLSR), support vector regression, and locally weighted regression (LWR). SSC, pH, and VC of 120 strawberries were statistically analyzed to facilitate the partitioning of data sets, which helped optimize the model. PLSR, with spectral and color features, obtained the optimal prediction of SSC with determination coefficient of prediction (Rp2) of 0.9370 and the root mean square error of prediction (RMSEP) of 0.1145. Through spectral features, the best prediction for pH was obtained by LWR with Rp2 = 0.8493 and RMSEP = 0.0501. Combination of spectral and textural features with PLSR provided the best results of VC with Rp2 = 0.8769 and RMSEP = 0.0279. Competitive adaptive reweighted sampling and uninformative variable elimination (UVE) were used to select important variables from the above features. Based on the important variables, the accuracy of SSC, pH, and VC prediction both gain the promotion. Finally, the distribution maps of SSC, pH, and VC over time were generated, and the change trend of three quality parameters was observed. Thus, the proposed method can nondestructively and accurately determine SSC, pH, and VC of strawberries and is expected to design and construct the simple sensors for the above quality parameters of strawberries.

Multi-Feature Nonlinear Optimization Motion Estimation Based on RGB-D and Inertial Fusion.

To achieve a high precision estimation of indoor robot motion, a tightly coupled RGB-D visual-inertial SLAM system is proposed herein based on multiple features. Most of the traditional visual SLAM methods only rely on points for feature matching and they often underperform in low textured scenes. Besides point features, line segments can also provide geometrical structure information of the environment. This paper utilized both points and lines in low-textured scenes to increase the robustness of RGB-D SLAM system. In addition, we implemented a fast initialization process based on the RGB-D camera to improve the real-time performance of the proposed system and designed a new backend nonlinear optimization framework. By minimizing the cost function formed by the pre-integrated IMU residuals and re-projection errors of points and lines in sliding windows, the state vector is optimized. The experiments evaluated on public datasets show that our system achieves higher accuracy and robustness on trajectories and in pose estimation compared with several state-of-the-art visual SLAM systems.

Low-Rate DoS Attacks Detection Based on MAF-ADM.

Low-rate denial of service (LDoS) attacks reduce the quality of network service by sending periodical packet bursts to the bottleneck routers. It is difficult to detect by counter-DoS mechanisms due to its stealthy and low average attack traffic behavior. In this paper, we propose an anomaly detection method based on adaptive fusion of multiple features (MAF-ADM) for LDoS attacks. This study is based on the fact that the time-frequency joint distribution of the legitimate transmission control protocol (TCP) traffic would be changed under LDoS attacks. Several statistical metrics of the time-frequency joint distribution are chosen to generate isolation trees, which can simultaneously reflect the anomalies in time domain and frequency domain. Then we calculate anomaly score by fusing the results of all isolation trees according to their ability to isolate samples containing LDoS attacks. Finally, the anomaly score is smoothed by weighted moving average algorithm to avoid errors caused by noise in the network. Experimental results of Network Simulator 2 (NS2), testbed, and public datasets (WIDE2018 and LBNL) demonstrate that this method does detect LDoS attacks effectively with lower false negative rate.

Realizing drug repositioning by adapting a recommendation system to handle the process.

BACKGROUND: Drug repositioning is the process of identifying new targets for known drugs. It can be used to overcome problems associated with traditional drug discovery by adapting existing drugs to treat new discovered diseases. Thus, it may reduce associated risk, cost and time required to identify and verify new drugs. Nowadays, drug repositioning has received more attention from industry and academia. To tackle this problem, researchers have applied many different computational methods and have used various features of drugs and diseases. RESULTS: In this study, we contribute to the ongoing research efforts by combining multiple features, namely chemical structures, protein interactions and side-effects to predict new indications of target drugs. To achieve our target, we realize drug repositioning as a recommendation process and this leads to a new perspective in tackling the problem. The utilized recommendation method is based on Pareto dominance and collaborative filtering. It can also integrate multiple data-sources and multiple features. For the computation part, we applied several settings and we compared their performance. Evaluation results show that the proposed method can achieve more concentrated predictions with high precision, where nearly half of the predictions are true. CONCLUSIONS: Compared to other state of the art methods described in the literature, the proposed method is better at making right predictions by having higher precision. The reported results demonstrate the applicability and effectiveness of recommendation methods for drug repositioning.

Detecting Succinylation sites from protein sequences using ensemble support vector machine.

BACKGROUND: Lysine succinylation is a new kind of post-translational modification which plays a key role in protein conformation regulation and cellular function control. To understand the mechanism of succinylation profoundly, it is necessary to identify succinylation sites in proteins accurately. However, traditional methods, experimental approaches, are labor-intensive and time-consuming. Computational prediction methods have been proposed recent years, and they are popular because of their convenience and high speed. In this study, we developed a new method to predict succinylation sites in protein combining multiple features, including amino acid composition, binary encoding, physicochemical property and grey pseudo amino acid composition, with a feature selection scheme (information gain). And then, it was trained using SVM (Support Vector Machine) and an ensemble learning algorithm. RESULTS: The performance of this method was measured with an accuracy of 89.14% and a MCC (Matthew Correlation Coefficient) of 0.79 using 10-fold cross validation on training dataset and an accuracy of 84.5% and a MCC of 0.2 on independent dataset. CONCLUSIONS: The conclusions made from this study can help to understand more of the succinylation mechanism. These results suggest that our method was very promising for predicting succinylation sites. The source code and data of this paper are freely available at https://github.com/ningq669/PSuccE .

Object Tracking Using Local Multiple Features and a Posterior Probability Measure.

Object tracking has remained a challenging problem in recent years. Most of the trackers can not work well, especially when dealing with problems such as similarly colored backgrounds, object occlusions, low illumination, or sudden illumination changes in real scenes. A centroid iteration algorithm using multiple features and a posterior probability criterion is presented to solve these problems. The model representation of the object and the similarity measure are two key factors that greatly influence the performance of the tracker. Firstly, this paper propose using a local texture feature which is a generalization of the local binary pattern (LBP) descriptor, which we call the double center-symmetric local binary pattern (DCS-LBP). This feature shows great discrimination between similar regions and high robustness to noise. By analyzing DCS-LBP patterns, a simplified DCS-LBP is used to improve the object texture model called the SDCS-LBP. The SDCS-LBP is able to describe the primitive structural information of the local image such as edges and corners. Then, the SDCS-LBP and the color are combined to generate the multiple features as the target model. Secondly, a posterior probability measure is introduced to reduce the rate of matching mistakes. Three strategies of target model update are employed. Experimental results show that our proposed algorithm is effective in improving tracking performance in complicated real scenarios compared with some state-of-the-art methods.

Identification of S-glutathionylation sites in species-specific proteins by incorporating five sequence-derived features into the general pseudo-amino acid composition.

As a selective and reversible protein post-translational modification, S-glutathionylation generates mixed disulfides between glutathione (GSH) and cysteine residues, and plays an important role in regulating protein activity, stability, and redox regulation. To fully understand S-glutathionylation mechanisms, identification of substrates and specific S-Glutathionylated sites is crucial. Experimental identification of S-glutathionylated sites is labor-intensive and time consuming, so establishing an effective computational method is much desirable due to their convenient and fast speed. Therefore, in this study, a new bioinformatics tool named SSGlu (Species-Specific identification of Protein S-glutathionylation Sites) was developed to identify species-specific protein S-glutathionylated sites, utilizing support vector machines that combine multiple sequence-derived features with a two-step feature selection. By 5-fold cross validation, the performance of SSGlu was measured with an AUC of 0.8105 and 0.8041 for Homo sapiens and Mus musculus, respectively. Additionally, SSGlu was compared with the existing methods, and the higher MCC and AUC of SSGlu demonstrated that SSGlu was very promising to predict S-glutathionylated sites. Furthermore, a site-specific analysis showed that S-glutathionylation intimately correlated with the features derived from its surrounding sites. The conclusions derived from this study might help to understand more of the S-glutathionylation mechanism and guide the related experimental validation. For public access, SSGlu is freely accessible at http://59.73.198.144:8080/SSGlu/.

Accurate in silico identification of protein succinylation sites using an iterative semi-supervised learning technique.

As a widespread type of protein post-translational modifications (PTMs), succinylation plays an important role in regulating protein conformation, function and physicochemical properties. Compared with the labor-intensive and time-consuming experimental approaches, computational predictions of succinylation sites are much desirable due to their convenient and fast speed. Currently, numerous computational models have been developed to identify PTMs sites through various types of two-class machine learning algorithms. These methods require both positive and negative samples for training. However, designation of the negative samples of PTMs was difficult and if it is not properly done can affect the performance of computational models dramatically. So that in this work, we implemented the first application of positive samples only learning (PSoL) algorithm to succinylation sites prediction problem, which was a special class of semi-supervised machine learning that used positive samples and unlabeled samples to train the model. Meanwhile, we proposed a novel succinylation sites computational predictor called SucPred (succinylation site predictor) by using multiple feature encoding schemes. Promising results were obtained by the SucPred predictor with an accuracy of 88.65% using 5-fold cross validation on the training dataset and an accuracy of 84.40% on the independent testing dataset, which demonstrated that the positive samples only learning algorithm presented here was particularly useful for identification of protein succinylation sites. Besides, the positive samples only learning algorithm can be applied to build predictors for other types of PTMs sites with ease. A web server for predicting succinylation sites was developed and was freely accessible at http://59.73.198.144:8088/SucPred/.

iACVP-MR: Accurate Identification of Anti-coronavirus Peptide based on Multiple Features Information and Recurrent Neural Network.

BACKGROUND: Over the years, viruses have caused human illness and threatened human health. Therefore, it is pressing to develop anti-coronavirus infection drugs with clear function, low cost, and high safety. Anti-coronavirus peptide (ACVP) is a key therapeutic agent against coronavirus. Traditional methods for finding ACVP need a great deal of money and man power. Hence, it is a significant task to establish intelligent computational tools to able rapid, efficient and accurate identification of ACVP. METHODS: In this paper, we construct an excellent model named iACVP-MR to identify ACVP based on multiple features and recurrent neural networks. Multiple features are extracted by using reduced amino acid component and dipeptide component, compositions of k-spaced amino acid pairs, BLOSUM62 encoder according to the N5C5 sequence, as well as second-order moving average approach based on 16 physicochemical properties. Then, two recurrent neural networks named long-short term memory (LSTM) and bidirectional gated recurrent unit (BiGRU) combined attention mechanism are used for feature fusion and classification, respectively. RESULTS: The accuracies of ENNAVIA-C and ENNAVIA-D datasets under the 10-fold cross-validation are 99.15% and 98.92%, respectively, and other evaluation indexes have also obtained satisfactory results. The experimental results show that our model is superior to other existing models. CONCLUSION: The iACVP-MR model can be viewed as a powerful and intelligent tool for the accurate identification of ACVP. The datasets and source codes for iACVP-MR are freely downloaded at https://github.com/yunyunliang88/iACVP-MR.

Windows malware detection based on static analysis with multiple features.

Malware or malicious software is an intrusive software that infects or performs harmful activities on a computer under attack. Malware has been a threat to individuals and organizations since the dawn of computers and the research community has been struggling to develop efficient methods to detect malware. In this work, we present a static malware detection system to detect Portable Executable (PE) malware in Windows environment and classify them as benign or malware with high accuracy. First, we collect a total of 27,920 Windows PE malware samples divided into six categories and create a new dataset by extracting four types of information including the list of imported DLLs and API functions called by these samples, values of 52 attributes from PE Header and 100 attributes of PE Section. We also amalgamate this information to create two integrated feature sets. Second, we apply seven machine learning models; gradient boosting, decision tree, random forest, support vector machine, K-nearest neighbor, naive Bayes, and nearest centroid, and three ensemble learning techniques including Majority Voting, Stack Generalization, and AdaBoost to classify the malware. Third, to further improve the performance of our malware detection system, we also deploy two dimensionality reduction techniques: Information Gain and Principal Component Analysis. We perform a number of experiments to test the performance and robustness of our system on both raw and selected features and show its supremacy over previous studies. By combining machine learning, ensemble learning and dimensionality reduction techniques, we construct a static malware detection system which achieves a detection rate of 99.5% and error rate of only 0.47%.

OSC-CO2: coattention and cosegmentation framework for plant state change with multiple features.

Cosegmentation and coattention are extensions of traditional segmentation methods aimed at detecting a common object (or objects) in a group of images. Current cosegmentation and coattention methods are ineffective for objects, such as plants, that change their morphological state while being captured in different modalities and views. The Object State Change using Coattention-Cosegmentation (OSC-CO2) is an end-to-end unsupervised deep-learning framework that enhances traditional segmentation techniques, processing, analyzing, selecting, and combining suitable segmentation results that may contain most of our target object's pixels, and then displaying a final segmented image. The framework leverages coattention-based convolutional neural networks (CNNs) and cosegmentation-based dense Conditional Random Fields (CRFs) to address segmentation accuracy in high-dimensional plant imagery with evolving plant objects. The efficacy of OSC-CO2 is demonstrated using plant growth sequences imaged with infrared, visible, and fluorescence cameras in multiple views using a remote sensing, high-throughput phenotyping platform, and is evaluated using Jaccard index and precision measures. We also introduce CosegPP+, a dataset that is structured and can provide quantitative information on the efficacy of our framework. Results show that OSC-CO2 out performed state-of-the art segmentation and cosegmentation methods by improving segementation accuracy by 3% to 45%.

Multiple color representation and fusion for diabetes mellitus diagnosis based on back tongue images.

Tongue images have been proved to be effective in diabetes mellitus (DM) diagnosis. Without requirement of collecting blood sample, tongue image based diagnosis approach is non-invasive and convenient for the patients. Meanwhile, the colors of tongues play an important in aiding accurate diagnosis. However, the tongues' colors fall on a small color gamut that makes it difficult for the existing color descripts to identify and distinguish the tiny difference of the tongues. To tackle this problem, we introduce a novel color descriptor by representing the colors with the clustering centers, namely color centroid points, of the color points sampled from tongue images. In order to boost the capacity of the descriptor, we extend it into three color spaces, i.e., RGB, HSV and LAB to mine a rich set of color information and exploit the complementary information among the three spaces. Since there exist correlation and complementarity among the features extracted from the three color spaces, we propose a novel multiple color features fusion method for DM diagnosis. Particularly, two projections are learned to project the multiple features to their corresponding shared and specific subspaces, in which their similarity and diversity are firstly measured by the Euclidean Distance and Hilbert Schmidt Independence Criterion (HSIC), respectively. To fully exploit the similar and complementary information, the two components are jointly transformed to their label vector, efficiently embedding the discriminant prior into the model, leading to significant improvement in the diagnosis outcomes. Experimental results on clinical tongue dataset substantiated the effectiveness of our proposed clustering-based color descriptor and the proposed multiple colors fusion approach. Overall, the proposed pipeline for the diagnosis of DM using back tongue images, achieved an average accuracy of up to 93.38%, indicating its potential toward realization of a clinical diagnostic tool for DM. Without loss generality, we also assessed the performance of the novel multiple features fusion method on two public datasets. The experiments prove the superiority of our multiple features learning model on general real-life application.

Nutrition Literacy Measurement Tool With Multiple Features for Chinese Adults.

BACKGROUND: Nutrition literacy is an emerging term which is increasingly used in policy and research. Progression is limited by the lack of an accepted method to measure nutrition literacy in Chinese adults, even as research in this area is growing. OBJECTIVE: The objective of this study is to develop a valid instrument to assess nutrition literacy in Chinese adults. METHODS: The process involved 2 steps: constructed nutrition literacy conceptual framework, and developed potential items of scale based on literature review; and conducted 2 rounds of Delphi consultation to select items of the preliminary questionnaire. RESULTS: In the Delphi survey, the content validity index for each domain, level, and dimension of nutrition literacy was 1.0, coefficient of variation was less than 0.10, and Kendall's coefficient of concordance was greater than 0.83. All of the 2 domains, 3 levels, and 6 dimensions initially formulated by our research team were reserved in the conceptual framework of nutrition literacy. Furthermore, a 43-item nutrition literacy measurement scale was established. Each item kept in the final scale reaches a high degree of concentration and a high degree of coordination, with the mean of importance ranging from 4.38 to 5.00. CONCLUSIONS: A nutrition literacy measurement scale with multiple features was established for Chinese adults, providing an operationalized tool to assess comprehensively nutrition literacy for research and practice in the field of nutrition, diet, and health.

Cyclic reactivation of distinct feature dimensions in human visual working memory.

Several recent behavioral studies have observed 4-10 Hz rhythmic fluctuations in attention-related performance over time. So far, this rhythmic attentional sampling has predominantly been demonstrated with regards to external visual attention, directed toward one single feature dimension. Whether and how attention might sample from concurrent internal representations of different feature dimensions held in working memory (WM) is currently largely unknown. To elucidate this issue, we conducted a human behavioral dense-sampling experiment, in which participants had to hold representations of two distinct feature dimensions (color and orientation) in WM. By querying the contents of WM at 72 time-points after encoding, we estimated the activity time course of the individual feature representations. Our results demonstrate an oscillatory component at 9.4 Hz in the joint time courses of both representations, presumably reflecting a common early perceptual sampling process in the alpha-frequency range. Furthermore, we observed an oscillatory component at 3.5 Hz in the time course difference between the two representations. This likely corresponds to a later attentional sampling process and indicates that internal representations of distinct features are activated in alteration. In summary, we demonstrate the cyclic reactivation of internal WM representations of distinct feature dimensions, as well as the co-occurrence of behavioral fluctuations at distinct frequencies, presumably associated to internal perceptual- and attentional rhythms. In addition, our findings also challenge a model of strict parallel processing in visual search, thus, providing novel input to the ongoing debate on whether search for more than one target feature constitutes a parallel- or a sequential mechanism.

Identifying Pupylation Proteins and Sites by Incorporating Multiple Methods.

Pupylation is an important posttranslational modification in proteins and plays a key role in the cell function of microorganisms; an accurate prediction of pupylation proteins and specified sites is of great significance for the study of basic biological processes and development of related drugs since it would greatly save experimental costs and improve work efficiency. In this work, we first constructed a model for identifying pupylation proteins. To improve the pupylation protein prediction model, the KNN scoring matrix model based on functional domain GO annotation and the Word Embedding model were used to extract the features and Random Under-sampling (RUS) and Synthetic Minority Over-sampling Technique (SMOTE) were applied to balance the dataset. Finally, the balanced data sets were input into Extreme Gradient Boosting (XGBoost). The performance of 10-fold cross-validation shows that accuracy (ACC), Matthew's correlation coefficient (MCC), and area under the ROC curve (AUC) are 95.23%, 0.8100, and 0.9864, respectively. For the pupylation site prediction model, six feature extraction codes (i.e., TPC, AAI, One-hot, PseAAC, CKSAAP, and Word Embedding) served to extract protein sequence features, and the chi-square test was employed for feature selection. Rigorous 10-fold cross-validations indicated that the accuracies are very high and outperformed its existing counterparts. Finally, for the convenience of researchers, PUP-PS-Fuse has been established at https://bioinfo.jcu.edu.cn/PUP-PS-Fuse and http://121.36.221.79/PUP-PS-Fuse/as a backup.