Background Wet-lab experiments for identification of interactions between drugs and target proteins are time-consuming, costly and labor-intensive. The use of computational prediction of drug–target interactions (DTIs), which is one of the significant points in drug discovery, has been considered by many researchers in recent years. It also reduces the search space of interactions by proposing potential interaction candidates. Results In this paper, a new approach based on unifying matrix factorization and nuclear norm minimization is proposed to find a low-rank interaction. In this combined method, to solve the low-rank matrix approximation, the terms in the DTI problem are used in such a way that the nuclear norm regularized problem is optimized by a bilinear factorization based on Rank-Restricted Soft Singular Value Decomposition (RRSSVD). In the proposed method, adjacencies between drugs and targets are encoded by graphs. Drug–target interaction, drug-drug similarity, target-target, and combination of similarities have also been used as input. Conclusions The proposed method is evaluated on four benchmark datasets known as Enzymes (E), Ion channels (ICs), G protein-coupled receptors (GPCRs) and nuclear receptors (NRs) based on AUC, AUPR, and time measure. The results show an improvement in the performance of the proposed method compared to the state-of-the-art techniques.
Lysine malonylation is one of the most important post-translational modifications (PTMs). It affects the functionality of cells. Malonylation site prediction in proteins can unfold the mechanisms of cellular functionalities. Experimental methods are one of the due prediction approaches. But they are typically costly and time-consuming to implement. Recently, methods based on machine-learning solutions have been proposed to tackle this problem. Such practices have been shown to reduce costs and time complexities and increase accuracy. However, these approaches also have specific shortcomings, including inappropriate feature extraction out of protein sequences, high-dimensional features, and inefficient underlying classifiers. A machine learning-based method is proposed in this paper to cope with these problems. In the proposed approach, seven different features are extracted. Then, the extracted features are combined, ranked based on the Fisher’s score (F-score), and the most efficient ones are selected. Afterward, malonylation sites are predicted using various classifiers. Simulation results show that the proposed method has acceptable performance compared with some state-of-the-art approaches. In addition, the XGBOOST classifier, founded on extracted features such as TFCRF, has a higher prediction rate than the other methods. The codes are publicly available at: https://github.com/jimy2020/Malonylation-site-prediction
Accurate and fast recognition of license plates is one of the most important challenges in the field of license plate recognition systems. Due to the high frame rate of surveillance cameras, old license plate recognition systems cannot be used in real-time applications. On the other hand, the presence of natural and artificial noise and different light and weather conditions make the detection and recognition process of these systems challenging. In this paper, an end-to-end method for efficiently detecting and recognizing plates is presented. In the proposed method, vehicles are first detected using a single-shot detector- (SSD-) based deep learning model in the video frames and the input images. This will increase the speed and accuracy in identifying the location of the plate in the given images. Then, the location of the plate is identified using the proposed architecture based on convolutional networks. Finally, using a deep convolutional network and long short-term memory (LSTM), the characters related to the plate are recognized. An advantage of our method is that the proposed deep network is trained using different images with different qualities that leads to high performance in detecting and recognizing plates. Also, considering that in the proposed method the vehicles are first detected and then the plate is detected in the vehicle image, there is no limit in the number of identified plates. Moreover, plate detection in the vehicle rectangle, instead of the whole frame, speeds up our method. The proposed method is evaluated using several databases. The first part of the evaluation focuses on robustness and recognition speed. The proposed method has the accuracy of 100% for vehicle detection, 100% for plate detection, and 99.37% for character recognition. In the second part of evaluation, the proposed method is evaluated in terms of overall speed. The experimental results witness that the proposed method is capable of processing 30 frames per second without losing any data and also outperforms several methods proposed in recent years, in terms of time and accuracy.
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