Informative Feature Representation Research Articles

An intelligent analysis method for power flow of bulk power grid based on fusion of indicator system feature and image feature

Abstract At present, the adjustment of power flow non-convergence in large power grid is still mostly based on manual trial and error, which relies heavily on expert experience and is inefficient. Therefore, an intelligent power flow analysis method based on index system and image feature fusion is proposed. Firstly, the power flow index system is constructed according to the actual demand, which can provide data support for the following power flow convergence judgment problems. Secondly, taking into account the constraints of similarity and ternary loss, the image feature information is used to guide the output of the information feature representation and to realize the organic interaction of the two modal features. On this basis, the calculation result of the index is correlated with the actual state of power flow to judge the convergence of power flow. Finally, the proposed method is verified on the CEPRI36 nodes system and a large power grid in China. The simulation results show that the accuracy of power flow convergence judgment is 100% for CEPRI36 nodes system and 99.83% for some domestic large power grid. It can effectively predict the convergence of the power flow before the calculation of the power flow required by the project and can provide the basis and theoretical support for the power flow optimization adjustment based on the indicator data.

Pest-ConFormer: A hybrid CNN-Transformer architecture for large-scale multi-class crop pest recognition

Crop pests are acknowledged as the major factors in reducing the yield and quality of agricultural production worldwide. It is an urgent necessity to recognize crop pests accurately to protect the crop in the early stage to reduce the loss for the agricultural economy. Due to the ecological characteristics of the crop pests and the complex background in fields, the crop pests show high inter-class similarity and significant intra-class variation in external morphology appearance, which makes current recognition methods suffer from low classification accuracy and poor generalization ability in complex natural environment recognition tasks. To tackle this problem, a hybrid convolutional neural network and transformer-based model, namely Pest-ConFormer, featured with multi-scale weakly supervised feature selection mechanisms is proposed, which has shown excellent multi-scale discriminative feature extraction in fine-grained visual classification (FGVC) tasks. This method employs a hybrid convolution-transformer encoder architecture pre-training in a self-supervised masked autoencoder manner as a backbone to learn pests’ highly discriminative features across various scales. Next, a dual-path feature aggregation structure with a top-down FPN-like feature pathway and a bottom-up PANet-like feature pathway based on attention mechanisms is designed to learn high-level global context information and low-level local detailed feature representation. Thirdly, a fine-grained classification module using weakly supervised learning is introduced to select the discriminative feature points in different pyramidal levels. Then, these feature points are fed into a graph convolutional network to accomplish classification. Several experiments are conducted on the large-scale multi-class IP102 benchmark dataset, and the proposed method achieves an accuracy of 77.81 % regarding crop pest recognition. The experimental results indicate that our approach outperforms other state-of-the-art methods by nearly 2 percent points, demonstrating that the proposed hybrid architecture with dual-path feature aggregation and fine-grained classification modules can be more effective in the crop pest recognition field than CNN-based methods and can be deployed in the practical natural environment. The source code will be available at https://github.com/mwfang/pestconformer.

PiRNA-disease association prediction based on multi-channel graph variational autoencoder.

Piwi-interacting RNA (piRNA) is a type of non-coding small RNA that is highly expressed in mammalian testis. PiRNA has been implicated in various human diseases, but the experimental validation of piRNA-disease associations is costly and time-consuming. In this article, a novel computational method for predicting piRNA-disease associations using a multi-channel graph variational autoencoder (MC-GVAE) is proposed. This method integrates four types of similarity networks for piRNAs and diseases, which are derived from piRNA sequences, disease semantics, piRNA Gaussian Interaction Profile (GIP) kernel, and disease GIP kernel, respectively. These networks are modeled by a graph VAE framework, which can learn low-dimensional and informative feature representations for piRNAs and diseases. Then, a multi-channel method is used to fuse the feature representations from different networks. Finally, a three-layer neural network classifier is applied to predict the potential associations between piRNAs and diseases. The method was evaluated on a benchmark dataset containing 5,002 experimentally validated associations with 4,350 piRNAs and 21 diseases, constructed from the piRDisease v1.0 database. It achieved state-of-the-art performance, with an average AUC value of 0.9310 and an AUPR value of 0.9247 under five-fold cross-validation. This demonstrates the method's effectiveness and superiority in piRNA-disease association prediction.

Dynamically Enhanced lane detection with multi-scale semantic feature fusion

Lane detection plays a crucial role in autonomous driving technology, and there have been many recent advancements in 3D lane detection methods. One common approach is simplifying the problem by transforming images into Bird's Eye View (BEV) space. However, existing methods still have limitations, particularly in accurately identifying lanes in many complex autonomous driving scenarios, such as slopes and extreme weather conditions. Therefore, this paper proposes a dynamically updated lane detection method called DynamicallyLane. This method utilizes ConvNeXt V2-N deformations as the backbone for feature extraction, employs dynamic learnable encoding and deformable attention mechanisms, achieves precise conversion from the frontal view to BEV through a novel representation of 3D reference points, and utilizes the Enhanced BEV Features module for feature fusion, obtaining richer and more semantically informative feature representations to facilitate the model learning process better. Experimental results demonstrate that DynamicallyLane achieves an F-score of 56.2 % on the OpenLane dataset and performs excellently on the Apollo 3D Synthetic dataset. Our code is available at https://github.com/Tafble/lane_detection.

Maturity discrimination of tobacco leaves for tobacco harvesting robots based on a Multi-Scale branch attention neural network

Traditional machine vision methods are susceptible to environmental influences, low recognition accuracy, and poor real-time performance in identifying tobacco leaf maturity. Currently, the maturity of tobacco leaves heavily relies on manual observation, and existing research on tobacco maturity discrimination focuses on standard light sources and standard background, which difficult to be applied to tobacco harvesting robots in natural environment. To tackle this problem, a maturity discrimination method based on the branch attention mechanism is proposed. Firstly, the region of interest in the extracted tobacco leaf images is obtained as a foundation for subsequent image processing. Then, a deep learning network model called the Multi-Scale Branch Attention Network (MSBANet) is designed for identification of tobacco leaf maturity. The effectiveness of MSBANet stems from its newly designed modules for multi-scale information extraction and branch attention. These modules help overcome the impact of lighting conditions and enable the extraction of more comprehensive and informative feature representations, capturing the relationship between phenotype and maturity. On the self-constructed dataset of tobacco leaf maturity (color images are 576 × 768 pixels), MSBANet-L achieves 89.6 % accuracy with 7.1GFLOPs and MSBANet-S achieves 88.2 % accuracy with 1.7 GFLOPs. When compared with widely used BP and MobileNet, as well as state-of-the-art structures such as YOLOv7-tiny-SiLU and FasterNet-T0, MSBANet demonstrates excellent performance. This method preliminarily explores the maturity discrimination of tobacco leaves in actual environments and shows a potential for development of tobacco harvesting robots.

Combining dual attention mechanism and efficient feature aggregation for road and vehicle segmentation from UAV imagery

Unmanned aerial vehicles (UAVs) have gained significant popularity in recent years due to their ability to capture high-resolution aerial imagery for various applications, including traffic monitoring, urban planning, and disaster management. Accurate road and vehicle segmentation from UAV imagery plays a crucial role in these applications. In this paper, we propose a novel approach combining dual attention mechanisms and efficient multi-layer feature aggregation to enhance the performance of road and vehicle segmentation from UAV imagery. Our approach integrates a spatial attention mechanism and a channel-wise attention mechanism to enable the model to selectively focus on relevant features for segmentation tasks. In conjunction with these attention mechanisms, we introduce an efficient multi-layer feature aggregation method that synthesizes and integrates multi-scale features at different levels of the network, resulting in a more robust and informative feature representation. Our proposed method is evaluated on the UAVid semantic segmentation dataset, showcasing its exceptional performance in comparison to renowned approaches such as U-Net, DeepLabv3+, and SegNet. The experimental results affirm that our approach surpasses these state-of-the-art methods in terms of segmentation accuracy.

Dual attention-enhanced SSD: A novel deep learning model for object detection

Object detection is a fundamental task in computer vision with significant implications across various applications, including autonomous driving, surveillance, and image understanding. The accurate and efficient detection of objects within images is crucial for enabling machines to interpret visual information and make informed decisions. In this paper, we present an enhanced version of the Single Shot MultiBox Detector (SSD) for object detection, leveraging the concept of dual attention mechanisms. Our proposed approach, named SSD-Dual Attention, integrates dual attention layers into the SSD framework. These dual attention layers are strategically positioned between feature maps and prediction convolutions, enhancing the model's ability to capture informative feature representations across a wide range of object scales and backgrounds. Experimental results on the PASCAL VOC 2007 and 2012 datasets validate the effectiveness of our approach. Notably, SSD-Dual Attention achieves an impressive mean Average Precision (mAP) of 78.1%, surpassing the performance of SSD models enhanced with attention mechanisms such as SSD-ECA, SSD-CBAM, SSD-Non-local attention and SSD-SE attention, as well as the original SSD. These results underscore the enhanced accuracy and precision of our object detection system, marking a substantial advancement in the field of computer vision. Code is available at https://github.com/AlexHunterLeo/Dual-attention-Enhanced-SSD-A-Novel-Deep-Learning-Model-for-Object-Detection

Learning with privileged knowledge of multiple kernels via joint prediction for CT Kernel conversion.

Most existing models for CT kernel conversion take images reconstructed with a single predetermined source kernel as input and convert them to images that are reconstructed with a target kernel. However, these models can achieve even better performance if they leverage complementary information obtained from images reconstructed with multiple different kernels. In many clinical practice scenarios, only images with one kernel can beacquired. We propose a privileged knowledge learning framework that learns privileged knowledge of other source kernels available only in the training data (called privileged information) to guide the conversion from a specific single source kernel to the target kernel, via a joint prediction (JP)task. We construct an ensemble of kernel-specific (KS) tasks where a KS network (KSNet) takes images reconstructed with a specific source kernel as input and converts them to images reconstructed with the target kernel. Then, a JP task is designed to provide extra regularization, which helps each KSNet learn more informative feature representations for kernel conversion, such as detail and structure representations. Meanwhile, we use a cross-shaped window-based attention mechanism in the JP task to highlight the most relevant features to strengthen privileged knowledge learning, thereby alleviating the problems of redundant noise unrelated to images reconstructed with target kernel and inconsistent features that arise from images reconstructed with different kernels. All KSNets can be trained collaboratively by using a JP task to improve the performance of each individual KSNet. We extensively evaluate our method on a clinical dataset with scanners from three manufacturers, that is, Siemens, GE and Philips. The experimental results demonstrate that our privileged knowledge learning framework is effective in improving CT kernel conversion. Through both quantitative and qualitative research, our privileged knowledge learning framework improves the kernel conversion results, thereby contributing to the improvement of diagnostic accuracy and the advancement of comparative research in quantitativemeasurements.

Hybrid-FHR: a multi-modal AI approach for automated fetal acidosis diagnosis

BackgroundIn clinical medicine, fetal heart rate (FHR) monitoring using cardiotocography (CTG) is one of the most commonly used methods for assessing fetal acidosis. However, as the visual interpretation of CTG depends on the subjective judgment of the clinician, this has led to high inter-observer and intra-observer variability, making it necessary to introduce automated diagnostic techniques.MethodsIn this study, we propose a computer-aided diagnostic algorithm (Hybrid-FHR) for fetal acidosis to assist physicians in making objective decisions and taking timely interventions. Hybrid-FHR uses multi-modal features, including one-dimensional FHR signals and three types of expert features designed based on prior knowledge (morphological time domain, frequency domain, and nonlinear). To extract the spatiotemporal feature representation of one-dimensional FHR signals, we designed a multi-scale squeeze and excitation temporal convolutional network (SE-TCN) backbone model based on dilated causal convolution, which can effectively capture the long-term dependence of FHR signals by expanding the receptive field of each layer’s convolution kernel while maintaining a relatively small parameter size. In addition, we proposed a cross-modal feature fusion (CMFF) method that uses multi-head attention mechanisms to explore the relationships between different modalities, obtaining more informative feature representations and improving diagnostic accuracy.ResultsOur ablation experiments show that the Hybrid-FHR outperforms traditional previous methods, with average accuracy, specificity, sensitivity, precision, and F1 score of 96.8, 97.5, 96, 97.5, and 96.7%, respectively.ConclusionsOur algorithm enables automated CTG analysis, assisting healthcare professionals in the early identification of fetal acidosis and the prompt implementation of interventions.

Deep learning based joint fusion approach to exploit anatomical and functional brain information in autism spectrum disorders

Background:The integration of the information encoded in multiparametric MRI images can enhance the performance of machine-learning classifiers. In this study, we investigate whether the combination of structural and functional MRI might improve the performances of a deep learning (DL) model trained to discriminate subjects with Autism Spectrum Disorders (ASD) with respect to typically developing controls (TD).Material and methodsWe analyzed both structural and functional MRI brain scans publicly available within the ABIDE I and II data collections. We considered 1383 male subjects with age between 5 and 40 years, including 680 subjects with ASD and 703 TD from 35 different acquisition sites. We extracted morphometric and functional brain features from MRI scans with the Freesurfer and the CPAC analysis packages, respectively. Then, due to the multisite nature of the dataset, we implemented a data harmonization protocol. The ASD vs. TD classification was carried out with a multiple-input DL model, consisting in a neural network which generates a fixed-length feature representation of the data of each modality (FR-NN), and a Dense Neural Network for classification (C-NN). Specifically, we implemented a joint fusion approach to multiple source data integration. The main advantage of the latter is that the loss is propagated back to the FR-NN during the training, thus creating informative feature representations for each data modality. Then, a C-NN, with a number of layers and neurons per layer to be optimized during the model training, performs the ASD-TD discrimination. The performance was evaluated by computing the Area under the Receiver Operating Characteristic curve within a nested 10-fold cross-validation. The brain features that drive the DL classification were identified by the SHAP explainability framework.ResultsThe AUC values of 0.66±0.05 and of 0.76±0.04 were obtained in the ASD vs. TD discrimination when only structural or functional features are considered, respectively. The joint fusion approach led to an AUC of 0.78±0.04. The set of structural and functional connectivity features identified as the most important for the two-class discrimination supports the idea that brain changes tend to occur in individuals with ASD in regions belonging to the Default Mode Network and to the Social Brain.ConclusionsOur results demonstrate that the multimodal joint fusion approach outperforms the classification results obtained with data acquired by a single MRI modality as it efficiently exploits the complementarity of structural and functional brain information.

Multimodal integration for fake news detection on social media platforms

The widespread dissemination of fake news on social media platforms can cause serious social impact, making the detection of fake news on social media platforms an urgent problem to be solved. Up to now, scholars have proposed various methods ranging from traditional manual feature extraction to deep learning algorithms for detecting fake news. However, these methods still have some limitations and two difficult problems: (1) How to learn informative news feature representations without losing information as much as possible? (2) How to effectively fuse multi-modal information to obtain high-order complementary information about news and enhance fake news detection? To overcome these two difficulties, this article proposes a multi-modal fusion fake news detection model. Firstly, the model uses BERT and VGG-19 to obtain the text and image feature representations of news content, respectively, and then further fuses multi-modal information through a multi-modal attention mechanism module to obtain high-order complementary information between different modalities, thereby obtaining informative news feature representations for fake news detection. Experimental results on two real-world public datasets demonstrate the effectiveness of our model compared to mainstream detection methods.

Semi-Supervised Adversarial Learning for Attribute-Aware Photo Aesthetic Assessment

Aesthetic attributes are crucial for aesthetics because they explicitly present some photo quality cues that a human expert might use to evaluate a photo's aesthetic quality. However, annotating aesthetic attributes is a time-consuming, costly, and error-prone task, which leads to the issue that photos available are partially annotated with attributes. To alleviate this issue, we propose a novel semi-supervised adversarial learning method for photo aesthetic assessment from partially attribute-annotated photos, which can greatly reduce the reliance on manual attribute annotation. Specifically, the proposed method consists of a score-attributes generator R, a photo generator G, and a discriminator D. The score-attributes generator learns the aesthetic score and attributes simultaneously to capture their dependencies and construct better feature representations. The photo generator reconstructs the photo by feeding aesthetic attributes, score, and informative feature representation. A discriminator is used to force the convergence of the features-attributes-score tuples generated from the score-attributes generator, the photo generator, and the ground-truth distribution in labeled data for training data. The proposed method significantly outperforms the state of the art, increasing the Spearman rank-order correlation coefficient (SRCC) from the existing best reported of 0.726 to 0.761 on Aesthetic and attributes database and 0.756 to 0.774 on Aesthetic visual analysis database, respectively.

Local Dependency-Enhanced Graph Convolutional Network for Aspect-Based Sentiment Analysis

The task of aspect-based sentiment analysis (ABSA) is to detect the sentiment polarity toward given aspects. Contemporary methods predominantly utilize graph neural networks and incorporate attention mechanisms to dynamically connect aspect terms with their surrounding contexts, resulting in more informative feature representations. However, these methods only consider whether there are dependencies between words when introducing dependencies, ignoring that dependencies between different sentiment words have different effects. Neglecting this could introduce noise and negatively impact the model’s performance. To overcome this limitation, we introduce a novel approach called the local dependency-enhanced graph convolutional network (LDEGCN). Our method combines semantic information and dependency relationships to better capture the affective relationships between words. Specifically, we integrate sentiment knowledge from SenticNet to enrich the sentence’s dependency graph and thoroughly explore the dependency types between contexts and aspects to focus on particular dependency types. The local context weight (LCW) method is employed on the dependency-enhanced graph to emphasize the importance of local contexts, thereby mitigating the issue of long-distance dependencies. Through extensive evaluations of five public datasets, the LDEGCN model demonstrates significant improvements over mainstream models.

FMAP: Learning robust and accurate local feature matching with anchor points

Local feature matching involves the task of establishing the pixel-wise correspondences between a pair of images. As an integral component of plentiful computer vision applications (e.g., visual localization), this task has been successfully performed using Transformer-based methods. However, these methods typically extract numerous keypoints from sparse texture regions to construct a densely-connected graph neural network (GNN) for long-range feature aggregation, which inevitably triggers redundant message exchange and hampers the learning process. Furthermore, they employ transformer encoder layers that consider images as 1D sequences, leaving them incapable of extracting multiscale local structural information from the images, which is critical for establishing correspondence in image pairs with significantly scales shifts. In this study, we develop FMAP, an innovative detector-free approach that enables accurate local feature matching. For the first issue, FMAP employs an anchor points feature aggregation module (APAM) that captures representative keypoints and discards the extraneous keypoints to build a sparsified GNN for compact yet clean message exchange, with the key insight that the keypoints containing abundant visual information are distinguishable from their neighbors. For the second issue, FMAP proposes a global–local multiscale perception module (GMPM), which incorporates abundant multiscale local context information into global feature representation by employing multiple depth-wise convolutions with varying kernel sizes, thereby generating discriminative features that are robust to scale shifts. In addition, the depth-wise convolutions are utilized in the feed-forward network of the GMPM to further fuse the global context information and local feature representation. Extensive experiments on several standard benchmarks demonstrate that the proposed FMAP method significantly outperforms state-of-the-art methods. Compared to the cutting-edge methods MatchFormer, QuadTree, and TopicFM in relative pose estimation task, FMAP surpasses them by 2.27%, 0.58%, and 1.08% in terms of AUC@5°. Besides, FMAP noticeably outperforms the baseline LoFTR by (2.38%,1.89%,1.45%) in terms of AUC@(5°, 10°, 20°). Moreover, we integrate FMAP into an official visual localization framework and conduct a visual localization experiment, with the results showing that FMAP exceeds LoFTR by 2.3% in terms of AP.

An efficient hyperspectral image classification method for limited training data

AbstractHyperspectral image classification has gained great progress in recent years based on deep learning model and massive training data. However, it is expensive and unpractical to label hyperspectral image data and implement model in constrained environment. To address this problem, this paper proposes an effective ghost module based spectral network for hyperspectral image classification. First, Ghost3D module is adopted to reduce the size of model parameter dramatically by redundant feature maps generation with linear transformation. Then Ghost2D module with channel‐wise attention is used to explore informative spectral feature representation. For large field covering, the non‐local operation is utilized to promote self‐attention. Compared with the state‐of‐the‐art hyperspectral image classification methods, the proposed approach achieves superior performance on three hyperspectral image data sets with fewer sample labelling and less resource consumption.

Multi‐view short‐term photovoltaic power prediction combining satellite images feature learning and graph mutual information feature representation

AbstractWith the introduction of national policies, photovoltaic (PV) power forecasting requirements for PV power plants are becoming increasingly stringent. It is particularly critical that PV power predictions are accurate while new energy is being consumed. It is also important to consider the satellite imagery of the location of the PV power plant and the meteorological information of the plant itself. The authors aim to explore the impact of these two elements on PV power prediction to better support PV power prediction. Therefore, this paper explores the cloud information elements of the satellite images from a multi‐view perspective and performs feature extraction and processing of the meteorological information to learn the impact of cloud cover on PV power prediction. Meanwhile, this paper introduces the mutual information mechanism for the influence of meteorological factors on PV power generation. It constructs the mutual information matrix and adopts the graph neural network for representation learning. A time‐series prediction model for short‐term PV power prediction is constructed and more accurate prediction results are obtained. The experimental results demonstrate that the proposed method is effective, has generalisation ability, and improved performance compared with the traditional model. The proposed method can also provide a novel approach and solution for short‐term PV power prediction.

Class-aware tiny object recognition over large-scale 3D point clouds

Although tremendous strides have been made in object recognition over large-scale 3D point clouds, one of the remaining open challenges is detecting tiny objects. The signal and appearance information of tiny objects are generally sparse and insufficient due to their tiny size, which makes the tiny object difficult to be detected with existing studies both in tiny 2D object recognition and general 3D object recognition since 3D topology relations are lost due to feature projection in tiny 2D object recognition and massive background may dominate the feature learner in general 3D object recognition. To the best of our knowledge, we explore tiny object recognition for the first time over large-scale 3D point clouds by designing deep neural network. To cope with this problem, a novel two-stage approach is proposed in this paper, which can effectively learn an informative and discriminative feature representation and efficiently process large-scale point clouds. Specifically, a class-aware filtering strategy is designed to dispense with the redundant background information. After that, we introduce a novel aggregating and sampling scheme in a supervised manner to progressively increase the receptive field for each 3D point, which can adaptively capture useful signals from tiny objects and learn complex geometric structures. Last, we propose a biased loss function that is inversely correlated to the number of tiny object points, thereby effectively handling the sparsity of tiny objects. Extensive experimental results over two real-world data sets validate the effectiveness and efficiency of the proposed approach.

Multi-Stage Spatio-Temporal Aggregation Transformer for Video Person Re-Identification

In recent years, the Transformer architecture has shown its superiority in the video-based person re-identification task. Inspired by video representation learning, these methods mainly focus on designing modules to extract informative spatial and temporal features. However, they are still limited in extracting local attributes and global identity information, which are critical for the person re-identification task. In this paper, we propose a novel Multi-Stage Spatial-Temporal Aggregation Transformer (MSTAT) with two novel designed proxy embedding modules to address the above issue. Specifically, MSTAT consists of three stages to encode the attribute-associated, the identity-associated, and the attribute-identity-associated information from the video clips, respectively, achieving the holistic perception of the input person. We combine the outputs of all the stages for the final identification. In practice, to save the computational cost, the Spatial-Temporal Aggregation (STA) modules are first adopted in each stage to conduct the self-attention operations along the spatial and temporal dimensions separately. We further introduce the Attribute-Aware and Identity-Aware Proxy embedding modules (AAP and IAP) to extract the informative and discriminative feature representations at different stages. All of them are realized by employing newly designed self-attention operations with specific meanings. Moreover, temporal patch shuffling is also introduced to further improve the robustness of the model. Extensive experimental results demonstrate the effectiveness of the proposed modules in extracting the informative and discriminative information from the videos, and illustrate the MSTAT can achieve state-of-the-art accuracies on various standard benchmarks.

Learning High-Dimensional Evolving Data Streams With Limited Labels.

In the context of streaming data, learning algorithms often need to confront several unique challenges, such as concept drift, label scarcity, and high dimensionality. Several concept drift-aware data stream learning algorithms have been proposed to tackle these issues over the past decades. However, most existing algorithms utilize a supervised learning framework and require all true class labels to update their models. Unfortunately, in the streaming environment, requiring all labels is unfeasible and not realistic in many real-world applications. Therefore, learning data streams with minimal labels is a more practical scenario. Considering the problem of the curse of dimensionality and label scarcity, in this article, we present a new semisupervised learning technique for streaming data. To cure the curse of dimensionality, we employ a denoising autoencoder to transform the high-dimensional feature space into a reduced, compact, and more informative feature representation. Furthermore, we use a cluster-and-label technique to reduce the dependency on true class labels. We employ a synchronization-based dynamic clustering technique to summarize the streaming data into a set of dynamic microclusters that are further used for classification. In addition, we employ a disagreement-based learning method to cope with concept drift. Extensive experiments performed on many real-world datasets demonstrate the superior performance of the proposed method compared to several state-of-the-art methods.

PredML-Site: Predicting Multiple Lysine PTM Sites With Optimal Feature Representation and Data Imbalance Minimization.

Identifying of post-translational modifications (PTM) is crucial in the study of computational proteomics, cell biology, pathogenesis, and drug development due to its role in many bio-molecular mechanisms. Computational methods for predicting multiple PTM at the same lysine residues, often referred to as K-PTM, is still evolving. This paper presents a novel computational tool, abbreviated as predML-Site, for predicting KPTM, such as acetylation, crotonylation, methylation, succinylation from an uncategorized peptide sample involving single, multiple, or no modification. For informative feature representation, multiple sequence encoding schemes, such as the sequence-coupling, binary encoding, k-spaced amino acid pairs, amino acid factor have been used with ANOVA and incremental feature selection. As a core predictor, a cost-sensitive SVM classifier has been adopted which effectively mitigates the effect of class-label imbalance in the dataset. predML-Site predicts multi-label PTM sites with 84.18% accuracy using the top 91 features. It has also achieved 85.34% aiming and 86.58% coverage rate which are much better than the existing state-of-the-art predictors on the same rigorous validation test. This performance indicates that predML-Site can be used as a supportive tool for further K-PTM study. For the convenience of the experimental scientists, predML-Site has been deployed as a user-friendly web-server at http://103.99.176.239/predML-Site.