Prediction of the Drug–Drug Interaction Types with the Unified Embedding Features from Drug Similarity Networks

Drug combination therapies are a promising strategy to overcome drug resistance and improve the efficacy of monotherapy in cancer, and it has been shown to lead to a decrease in dose-related toxicities. Except the synergistic reaction between drugs, some antagonistic drug–drug interactions (DDIs) exist, which is the main cause of adverse drug events. Precisely predicting the type of DDI is important for both drug development and more effective drug combination therapy applications. Recently, numerous text mining– and machine learning–based methods have been developed for predicting DDIs. All these methods implicitly utilize the feature of drugs from diverse drug-related properties. However, how to integrate these features more efficiently and improve the accuracy of classification is still a challenge. In this paper, we proposed a novel method (called NMDADNN) to predict the DDI types by integrating five drug-related heterogeneous information sources to extract the unified drug mapping features. NMDADNN first constructs the similarity networks by using the Jaccard coefficient and then implements random walk with restart algorithm and positive pointwise mutual information for extracting the topological similarities. After that, five network-based similarities are unified by using a multimodel deep autoencoder. Finally, NMDADNN implements the deep neural network (DNN) on the unified drug feature to infer the types of DDIs. In comparison with other recent state-of-the-art DNN-based methods, NMDADNN achieves the best results in terms of accuracy, area under the precision-recall curve, area under the ROC curve, F1 score, precision and recall. In addition, many of the promising types of drug–drug pairs predicted by NMDADNN are also confirmed by using the interactions checker tool. These results demonstrate the effectiveness of our NMDADNN method, indicating that NMDADNN has the great potential for predicting DDI types.

Adaptive Optimization-Enabled Neural Networks to Handle the Imbalance Churn Data in Churn Prediction

The churn prediction based on telecom data has been paid great attention because of the increasing the number telecom providers, but due to inconsistent data, sparsity, and hugeness, the churn prediction becomes complicated and challenging. Hence, an effective and optimal prediction of churns mechanism, named adaptive firefly-spider optimization (adaptive FSO) algorithm, is proposed in this research to predict the churns using the telecom data. The proposed churn prediction method uses telecom data, which is the trending domain of research in predicting the churns; hence, the classification accuracy is increased. However, the proposed adaptive FSO algorithm is designed by integrating the spider monkey optimization (SMO), firefly optimization algorithm (FA), and the adaptive concept. The input data is initially given to the master node of the spark framework. The feature selection is carried out using Kendall’s correlation to select the appropriate features for further processing. Then, the selected unique features are given to the master node to perform churn prediction. Here, the churn prediction is made using a deep convolutional neural network (DCNN), which is trained by the proposed adaptive FSO algorithm. Moreover, the developed model obtained better performance using the metrics, like dice coefficient, accuracy, and Jaccard coefficient by varying the training data percentage and selected features. Thus, the proposed adaptive FSO-based DCNN showed improved results with a dice coefficient of 99.76%, accuracy of 98.65%, Jaccard coefficient of 99.52%.

Implementation of Winnowing Algorithm and Simple Additive Weighting SAW for Publication Reference Journal Search System

Reference journals have several different writing arrangements. In the reference journal, there is some information or words needed in making scientific journals. With this information or word retrieval system, it can help to find journals that match the similarities between input and information or words in the journal that will be used as a reference. In this study, the input process of equations and information or words will be processed using the Winnowing algorithm as an algorithm that can find the similarity of words or texts with N-gram functions, rolling hash, and Jaccard Coefficient. In general, the search only uses the same words or text without any weighting on the composition of the reference journal. To be able to find journals and their level of importance in the order of journals, a weighting method is needed. This study also uses the Simple Additive Weighting (SAW) method as a process to determine the value of the order of urgency in journals so that it can provide results in the form of rankings based on searches and urgency weights in reference journals. The results of the similarity query with documents obtained 60% precision, 77% recall, and 81% accuracy, documents, and documents had 41% precision, 83% recall, and 66% accuracy. using Winnowing Algorithm, the search system can detect the similarity of text.

Genetic Diversity and Population Structure of Moroccan Isolates Belong to Alternaria spp. Causing Black Rot and Brown Spot in Citrus

Alternaria alternata is one of the most important fungi causing various diseases on citrus worldwide. In Morocco, Alternaria black rot (ABR) and Alternaria brown spot (ABS) are two major diseases causing serious losses in commercial cultivars of citrus. The aim of the present work was to study the genetic diversity and the population structure of isolates belonging to sect. Alternaria obtained from infected citrus fruits, collected from seven provinces at different locations in Morocco (markets, packinghouses, and orchards). Forty-five isolates were analyzed by sequence-related amplified polymorphism (SRAP) markers, and cluster analysis of DNA fragments was performed using UPGMA method and Jaccard coefficient. Cluster analysis revealed that isolates were classified in four distinct groups. AMOVA revealed also a large extent of variation within sect. Alternaria isolates (99%). The results demonstrate that no correlation was found among SRAP pattern, host, and geographical origin of these isolates. Population structure analyses showed that the Alternaria isolates from the same collection origin had almost a similar level of admixture.

Risk Factors of Restroke in Patients with Lacunar Cerebral Infarction Using Magnetic Resonance Imaging Image Features under Deep Learning Algorithm

This study was aimed to explore the magnetic resonance imaging (MRI) image features based on the fuzzy local information C-means clustering (FLICM) image segmentation method to analyze the risk factors of restroke in patients with lacunar infarction. In this study, based on the FLICM algorithm, the Canny edge detection algorithm and the Fourier shape descriptor were introduced to optimize the algorithm. The difference of Jaccard coefficient, Dice coefficient, peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), running time, and segmentation accuracy of the optimized FLICM algorithm and other algorithms when the brain tissue MRI images were segmented was studied. 36 patients with lacunar infarction were selected as the research objects, and they were divided into a control group (no restroke, 20 cases) and a stroke group (restroke, 16 cases) according to whether the patients had restroke. The differences in MRI imaging characteristics of the two groups of patients were compared, and the risk factors for restroke in lacunar infarction were analyzed by logistic multivariate regression. The results showed that the Jaccard coefficient, Dice coefficient, PSNR value, and SSIM value of the optimized FLICM algorithm for segmenting brain tissue were all higher than those of other algorithms. The shortest running time was 26 s, and the highest accuracy rate was 97.86%. The proportion of patients with a history of hypertension, the proportion of patients with paraventricular white matter lesion (WML) score greater than 2 in the stroke group, the proportion of patients with a deep WML score of 2, and the average age of patients in the stroke group were much higher than those in the control group ( P < 0.05 ). Logistic multivariate regression showed that age and history of hypertension were risk factors for restroke after lacunar infarction ( P < 0.05 ). It showed that the optimized FLICM algorithm can effectively segment brain MRI images, and the risk factors for restroke in patients with lacunar infarction were age and hypertension history. This study could provide a reference for the diagnosis and prognosis of lacunar infarction.

Segmentation of Prefrontal Lobe Based on Improved Clustering Algorithm in Patients with Diabetes

Diabetics are prone to postoperative cognitive dysfunction (POCD). The occurrence may be related to the damage of the prefrontal lobe. In this study, the prefrontal lobe was segmented based on an improved clustering algorithm in patients with diabetes, in order to evaluate the relationship between prefrontal lobe volume and COPD. In this study, a total of 48 diabetics who underwent selective noncardiac surgery were selected. Preoperative magnetic resonance imaging (MRI) images of the patients were segmented based on the improved clustering algorithm, and their prefrontal volume was measured. The correlation between the volume of the prefrontal lobe and Z -score or blood glucose was analyzed. Qualitative analysis shows that the gray matter, white matter, and cerebrospinal fluid based on the improved clustering algorithm were easy to distinguish. Quantitative evaluation results show that the proposed segmentation algorithm can obtain the optimal Jaccard coefficient and the least average segmentation time. There was a negative correlation between the volume of the prefrontal lobe and the Z -score. The cut-off value of prefrontal lobe volume for predicting POCD was <179.8, with the high specificity. There was a negative correlation between blood glucose and volume of the prefrontal lobe. From the results, we concluded that the segmentation of the prefrontal lobe based on an improved clustering algorithm before operation may predict the occurrence of POCD in diabetics.

Training neural networks for automotive computer vision systems considering types of false estimations

Introduction (problem statement and relevance). This article deals with the problem of training artificial neural networks intended to analyze images of the surrounding space in automotive computer vision systems. The conventional training approach implies using loss functions that only improve the overall identification quality making no distinction between types of possible false predictions. However, traffic safety risks associated with different types of prediction errors are unequal being higher for false positive estimations.The purpose of this work is to propose improved loss functions, which include penalties for false positive predictions, and to study how using these functions affects the behavior of a convolutional neural network when estimating the drivable space.Methodology and research methods. The proposed loss functions are based on the Sørensen-Dice coefficient differing from each other in the approaches to penalizing false positive errors. The performance of the trained neural networks is evaluated using three metrics, namely, the Jaccard coefficient, False Positive Rate and False Negative Rate. The proposed solutions are compared with the conventional one by calculating the ratios of their respective metrics.Scientific novelty and results. The improved loss functions have been proposed to train computer vision algorithms featuring penalties for false positive estimations. The experimental study of the trained neural networks using a test dataset has shown that the improved loss functions allow reducing the False Positive Rate by 21%.The practical significance of this work is constituted by the proposed method of training neural networks that allows to increase the safety of automated driving through an improved accuracy of analyzing the surrounding space using computer vision systems.

Data combination method in Remote Sensing tasks in case of conflicting information sources

Nowadays technologies of UAV-based Remote Sensing are used in different areas, such as: ecological monitoring, agriculture tasks, exploring for minerals, oil and gas, forest monitoring and warfare. Drones provide information more rapidly than piloted aerial vehicles and give images of a very high resolution, sufficiently low cost and high precision.Let’s note, that processing of conflicting information is the most important task in remote sensing. Dempster’s rule of data combination is widely used in solution of different remote sensing tasks, because it can processes incomplete and vague information. However, Dempster’s rule has some disadvantage, it can not deal with highly conflicted data. This rule of data combination yields wrong results, when bodies of evidence highly conflict with each other. That’s why it was proposed a data combination method in UAV-based Remote Sensing. This method has several important advantages: simple calculation and high accuracy. In this paper data combination method based on application of Jaccard coefficient and Dempster’s rule of combination is proposed. The described method can deal with conflicting sources of information. This data combination method based on application of evidence theory and Jaccard coefficient takes into consideration the associative relationship of the evidences and can efficiently handle highly conflicting sources of data (spectral bands).The frequency approach to determine basic probability assignment and formula to determine Jaccard coefficient are described in this paper too. Jaccard coefficient is defined as the size of the intersection divided by the size of the union of the sample sets. Jaccard coefficient measures similarity between finite sets. Some numerical examples of calculation of Jaccard coefficient and basic probability assignments are considered in this work too.This data combination method based on application of Jaccard coefficient and Dempster’s rule of combination can be applied in exploring for minerals, different agricultural, practical and ecological tasks.

Convolutional Neural Network-Processed MRI Images in the Diagnosis of Plastic Bronchitis in Children

Objective. The study focused on the features of the convolutional neural networks- (CNN-) processed magnetic resonance imaging (MRI) images for plastic bronchitis (PB) in children. Methods. 30 PB children were selected as subjects, including 19 boys and 11 girls. They all received the MRI examination for the chest. Then, a CNN-based algorithm was constructed and compared with Active Appearance Model (AAM) algorithm for segmentation effects of MRI images in 30 PB children, factoring into occurring simultaneously than (OST), Dice, and Jaccard coefficient. Results. The maximum Dice coefficient of CNN algorithm reached 0.946, while that of active AAM was 0.843, and the Jaccard coefficient of CNN algorithm was also higher (0.894 vs. 0.758, P < 0.05 ). The MRI images showed pulmonary inflammation in all subjects. Of 30 patients, 14 (46.66%) had complicated pulmonary atelectasis, 9 (30%) had the complicated pleural effusion, 3 (10%) had pneumothorax, 2 (6.67%) had complicated mediastinal emphysema, and 2 (6.67%) had complicated pneumopericardium. Also, of 30 patients, 19 (63.33%) had lung consolidation and atelectasis in a single lung lobe and 11 (36.67%) in both two lung lobes. Conclusion. The algorithm based on CNN can significantly improve the segmentation accuracy of MRI images for plastic bronchitis in children. The pleural effusion was a dangerous factor for the occurrence and development of PB.

Characterization of Sonic Events Present in Natural-Urban Hybrid Habitats Using UMAP and SEDnet: The Case of the Urban Wetlands

We investigated whether the use of technological tools can effectively help in manipulating the increasing volume of audio data available through the use of long field recordings. We also explored whether we can address, by using these recordings and tools, audio data analysis, feature extraction and determine predominant patterns in the data. Similarly, we explored whether we can visualize feature clusters in the data and automatically detect sonic events. Our focus was primarily on enhancing the importance of natural-urban hybrid habitats within cities, which benefit communities in various ways, specifically through the natural soundscapes of these habitats that evoke memories and reinforce a sense of belonging for inhabitants. The loss of sonic heritage can be a precursor to the extinction of biodiversity within these habitats. By quantifying changes in the soundscape of these habitats over long periods of time, we can collect relevant information linked to this eventual loss. In this respect, we developed two approaches. The first was the comparison among habitats that progressively changed from natural to urban. The second was the optimization of the field recordings’ labeling process. This was performed with labels corresponding to the annotations of classes of sonic events and their respective start and end times, including events temporarily superimposed on one another. We compared three habitats over time by using their sonic characteristics collected in field conditions. Comparisons of sonic similarity or dissimilarity among patches were made based on the Jaccard coefficient and uniform manifold approximation and projection (UMAP). Our SEDnet model achieves a F1-score of 0.79 with error rate 0.377 and with the area under PSD-ROC curve of 71.0. In terms of computational efficiency, the model is able to detect sound events from an audio file in a time of 14.49 s. With these results, we confirm the usefulness of the methods used in this work for the process of labeling field recordings.