Object detection in very high-resolution (VHR) aerial images is an essential step for a wide range of applications such as military applications, urban planning, and environmental management. Still, ...it is a challenging task due to the different scales and appearances of the objects. On the other hand, object detection task in VHR aerial images has improved remarkably in recent years due to the achieved advances in convolution neural networks (CNN). Most of the proposed methods depend on a two-stage approach, namely: a region proposal stage and a classification stage such as Faster R-CNN. Even though two-stage approaches outperform the traditional methods, their optimization is not easy and they are not suitable for real-time applications. In this paper, a uniform one-stage model for object detection in VHR aerial images has been proposed. In order to tackle the challenge of different scales, a densely connected feature pyramid network has been proposed by which high-level multi-scale semantic feature maps with high-quality information are prepared for object detection. This work has been evaluated on two publicly available datasets and outperformed the current state-of-the-art results on both in terms of mean average precision (mAP) and computation time.
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Condition monitoring is desirable for increasing machinery availability, reducing consequential damage, and improving operational efficiency. Model-based methods are efficient monitoring systems for ...providing warning and predicting certain faults at early stages. However, the conventional methods must work with explicit motor models, and cannot be applied effectively for vibration signal diagnosis due to their nonadaptation and the random nature of vibration signal. In this paper, an analytical redundancy method using neural network modeling of the induction motor in vibration spectra is proposed for machine fault detection and diagnosis. The short-time Fourier transform is used to process the quasi-steady vibration signals to continuous spectra for the neural network model training. The faults are detected from changes in the expectation of vibration spectra modeling error. The effectiveness of the proposed method is demonstrated through experimental results, and it is shown that a robust and automatic induction machine condition monitoring system has been produced
Abstract
Lysine crotonylation (Kcr) is a posttranslational modification widely detected in histone and nonhistone proteins. It plays a vital role in human disease progression and various cellular ...processes, including cell cycle, cell organization, chromatin remodeling and a key mechanism to increase proteomic diversity. Thus, accurate information on such sites is beneficial for both drug development and basic research. Existing computational methods can be improved to more effectively identify Kcr sites in proteins. In this study, we proposed a deep learning model, DeepCap-Kcr, a capsule network (CapsNet) based on a convolutional neural network (CNN) and long short-term memory (LSTM) for robust prediction of Kcr sites on histone and nonhistone proteins (mammals). The proposed model outperformed the existing CNN architecture Deep-Kcr and other well-established tools in most cases and provided promising outcomes for practical use; in particular, the proposed model characterized the internal hierarchical representation as well as the important features from multiple levels of abstraction automatically learned from a small number of samples. The trained model was well generalized in other species (papaya). Moreover, we showed the features and properties generated by the internal capsule layer that can explore the internal data distribution related to biological significance (as a motif detector). The source code and data are freely available at https://github.com/Jhabindra-bioinfo/DeepCap-Kcr.
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The promoter region is located near the transcription start sites and regulates transcription initiation of the gene by controlling the binding of RNA polymerase. Thus, promoter region recognition is ...an important area of interest in the field of bioinformatics. Numerous tools for promoter prediction were proposed. However, the reliability of these tools still needs to be improved. In this work, we propose a robust deep learning model, called DeePromoter, to analyze the characteristics of the short eukaryotic promoter sequences, and accurately recognize the human and mouse promoter sequences. DeePromoter combines a convolutional neural network (CNN) and a long short-term memory (LSTM). Additionally, instead of using non-promoter regions of the genome as a negative set, we derive a more challenging negative set from the promoter sequences. The proposed negative set reconstruction method improves the discrimination ability and significantly reduces the number of false positive predictions. Consequently, DeePromoter outperforms the previously proposed promoter prediction tools. In addition, a web-server for promoter prediction is developed based on the proposed methods and made available at https://home.jbnu.ac.kr/NSCL/deepromoter.htm.
Angiogenesis plays a vital role in the pathogenesis of several human diseases, particularly in the case of solid tumors. In the realm of cancer treatment, recent investigations into peptides with ...anti-angiogenic properties have yielded encouraging outcomes, thereby creating a hopeful therapeutic avenue for the treatment of cancer. Therefore, correctly identifying the anti-angiogenic peptides is extremely important in comprehending their biophysical and biochemical traits, laying the groundwork for uncovering novel drugs to combat cancer.
In this work, we present a novel ensemble-learning-based model, Stack-AAgP, specifically designed for the accurate identification and interpretation of anti-angiogenic peptides (AAPs). Initially, a feature representation approach is employed, generating 24 baseline models through six machine learning algorithms (random forest RF, extra tree classifier ETC, extreme gradient boosting XGB, light gradient boosting machine LGBM, CatBoost, and SVM) and four feature encodings (pseudo-amino acid composition PAAC, amphiphilic pseudo-amino acid composition APAAC, composition of k-spaced amino acid pairs CKSAAP, and quasi-sequence-order QSOrder). Subsequently, the output (predicted probabilities) from 24 baseline models was inputted into the same six machine-learning classifiers to generate their respective meta-classifiers. Finally, the meta-classifiers were stacked together using the ensemble-learning framework to construct the final predictive model.
Findings from the independent test demonstrate that Stack-AAgP outperforms the state-of-the-art methods by a considerable margin. Systematic experiments were conducted to assess the influence of hyperparameters on the proposed model. Our model, Stack-AAgP, was evaluated on the independent NT15 dataset, revealing superiority over existing predictors with an accuracy improvement ranging from 5% to 7.5% and an increase in Matthews Correlation Coefficient (MCC) from 7.2% to 12.2%.
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•Probabilistic vectors improve predictive model performance.•Stack-AAgP ensemble predicts anti-angiogenic peptides.•Stack-AAgP offers stability and precision.•Stack-AAgP surpasses state-of-the-art methods.
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Aging and temperature changes in the passive components of an LCL -filter grid connected converter system (GCCs) may lead to parameter uncertainties, which can in turn influence its modeling accuracy ...for finite-control-set model predictive control (FCS-MPC). The presence of model errors will change the resonance point and deteriorate the power quality of the grid current, in turn degrading the active damping performance. In this situation, there is a serious possibility that the GCCs may malfunction and automatically disconnect from the grid, causing great challenges to the system stability. To solve this problem, first, prediction error analysis in FCS-MPC due to the model parameter errors is presented. Second, to achieve high accuracy and fast filter parameter estimation in utility, an adaptive online parameter identification method based on gradient descent optimization (GDO) has been proposed. Finally, to further reduce the searching time needed by the optimal iteration step, a variable iteration step searching method based on the root-mean-square-prop (RMSprop) GDO method is proposed. Experimental studies of an LCL -GCCs prototype in the laboratory have been conducted to validate the effectiveness of the proposed method.
Drug combinations are frequently used to treat cancer to reduce side effects and increase efficacy. The experimental discovery of drug combination synergy is time-consuming and expensive for large ...datasets. Therefore, an efficient and reliable computational approach is required to investigate these drug combinations. Advancements in deep learning can handle large datasets with various biological problems. In this study, we developed a SynergyGTN model based on the Graph Transformer Network to predict the synergistic drug combinations against an untreated cancer cell line expression profile. We represent the drug via a graph, with each node and edge of the graph containing nine types of atomic feature vectors and four bonds features, respectively. The cell lines represent based on their gene expression profiles. The drug graph was passed through the GTN layers to extract a generalized feature map for each drug pairs. The drug pair extracted features and cell-line gene expression profiles were concatenated and subsequently subjected to processing through multiple densely connected layers. SynergyGTN outperformed the state-of-the-art methods, with a receiver operating characteristic area under the curve improvement of 5% on the 5-fold cross-validation. The accuracy of SynergyGTN was further verified through three types of cross-validation tests strategies namely leave-drug-out, leave-combination-out, and leave-tissue-out, resulting in improvement in accuracy of 8%, 1%, and 2%, respectively. The Astrazeneca Dream dataset was utilized as an independent dataset to validate and assess the generalizability of the proposed method, resulting in an improvement in balanced accuracy of 13%. In conclusion, SynergyGTN is a reliable and efficient computational approach for predicting drug combination synergy in cancer treatment. Finally, we developed a web server tool to facilitate the pharmaceutical industry and researchers, as available at: http://nsclbio.jbnu.ac.kr/tools/SynergyGTN/.
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•Drug combinations have increased the potential for cancer treatment by concurrently targeting.•Novel synergistic drug combination prediction for cancer cell-lines.•Graph transformers network accurately predict the drug combination.
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Sliding-mode control (SMC) has been widely used in grid-connected converter system (GCC) systems because of its robustness to parameter variations and external disturbances. However, chattering in ...SMC may deteriorate the tracking accuracy and can easily excite high-frequency unmodeled dynamics. To solve this problem, this article presents a fuzzy-fractional-order nonsingular terminal sliding-mode controller (Fuzzy-FONTSMC) for the grid current control of LCL-GCCs. First, the system modeling, design of the integer-order NTSMC controller, and state estimation based on the Kalman filter to minimize the sampling sensors are described. Second, the Fuzzy-FONTSMC controller is introduced for optimal fraction-order selection and chattering mitigation, this controller exhibits fast convergence with high tracking accuracy and strong robustness. Finally, the Lyapunov theorem is used to analyze the system stability. Experimental comparisons on a 10-kVA laboratory prototype validate the superior performance and effectiveness of the proposed method under many scenarios.
Background
Predicting protein-ligand binding sites is a fundamental step in understanding the functional characteristics of proteins, which plays a vital role in elucidating different biological ...functions and is a crucial step in drug discovery. A protein exhibits its true nature after binding to its interacting molecule known as a ligand that binds only in the favorable binding site of the protein structure. Different computational methods exploiting the features of proteins have been developed to identify the binding sites in the protein structure, but none seems to provide promising results, and therefore, further investigation is required.
Results
In this study, we present a deep learning model PUResNet and a novel data cleaning process based on structural similarity for predicting protein-ligand binding sites. From the whole scPDB (an annotated database of druggable binding sites extracted from the Protein DataBank) database, 5020 protein structures were selected to address this problem, which were used to train PUResNet. With this, we achieved better and justifiable performance than the existing methods while evaluating two independent sets using distance, volume and proportion metrics.
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Epigenetic modifications have a vital role in gene expression and are linked to cellular processes such as differentiation, development, and tumorigenesis. Thus, the availability of reliable and ...accurate methods for identifying and defining these changes facilitates greater insights into the regulatory mechanisms that rely on epigenetic modifications. The current experimental methods provide a genome-wide identification of epigenetic modifications; however, they are expensive and time-consuming. To date, several machine learning methods have been proposed for identifying modifications such as DNA N6-Methyladenine (6mA), RNA N6-Methyladenosine (m6A), DNA N4-methylcytosine (4mC), and RNA pseudouridine (<inline-formula><tex-math notation="LaTeX">\Psi</tex-math> <mml:math><mml:mi>Ψ</mml:mi></mml:math><inline-graphic xlink:href="chong-ieq1-3083789.gif"/> </inline-formula>). However, these methods are task-specific computational tools and require different encoding representations of DNA/RNA sequences. In this study, we propose a unified deep learning model, called ZayyuNet, for the identification of various epigenetic modifications. The proposed model is based on an architecture called, SpinalNet, inspired by the human somatosensory system that can efficiently receive large inputs and achieve better performance. The proposed model has been evaluated on various epigenetic modifications such as 6mA, m6A, 4mC, and <inline-formula><tex-math notation="LaTeX">\Psi</tex-math> <mml:math><mml:mi>Ψ</mml:mi></mml:math><inline-graphic xlink:href="chong-ieq2-3083789.gif"/> </inline-formula> and the results achieved outperform current state-of-the-art models. A user-friendly web server has been built and made freely available at http://nsclbio.jbnu.ac.kr/tools/ZayyuNet/ .
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