A novel entropy-based mapping method for determining the protein-protein interactions in viral genomes by using coevolution analysis

Protein-protein interactions have a vital role in DNA transcription, immune system, and signal transmission between cells. Determining the interactions between proteins can give information about the functional structure of a cell and the functions of target organisms. Protein-protein interactions are determined by experimental approaches, yet, there is still a huge gap in specifying all possible protein interactions in an organism. Furthermore, since these approaches use cloning, labeling, and affinity mass spectrometry, the analysis process is time-consuming and expensive. However, analyzing the protein interactions with computational approaches based on coevolution theory eliminate these kinds of limitations, since in the coevolution theory model, interacting proteins show coevolutionary mutations and form similar phylogenetic trees. Current coevolution methods are based on the multiple-sequence alignment process; yet many high false positive interactions arise with these methods. Therefore, it is important to perform computational-based coevolution analysis. Protein-protein interaction using coevolution analysis has been employed in conjunction with experimental approaches to explore new protein interactions. However, in order to predict protein interactions with computational-based coevolution analysis, protein sequences need to be mapped. There are various types of protein mapping methods belonging to certain categories in the literature. These methods are frequently used in studies of predicting protein interactions. In this study, as an alternative to these methods, we proposed a novel entropy-based protein mapping method and predicted protein-protein interactions in viral genomes by using coevolution analysis. The study consists of 5 stages. In the first stage, the protein sequences of viral genomes were mapped using both the proposed numerical mapping method and state-of-arts protein mapping methods. In the second stage, Fourier transform was applied to each mapped protein sequences. In the third stage, the distance matrix was generated by finding the distances between the proteins belonging to the same virus genome. In the fourth stage, Pearson correlation values between the distances were calculated and coevolution analysis was performed. In the last stage, the proposed mapping method was compared with state-of-arts protein mapping methods and MirrorTree approach. Coevolution analysis was performed on two different virus genomes; Ebola virus and Influenza A virus. With the proposed method, a high degree of correlation has been obtained between proteins of the Ebola virus. For Ebola virus, the lowest correlation result (0.75) was obtained between the NP-VP35 protein pair. The highest correlation (0.99) was observed between the NP-VP24 and NP-VP40 protein pairs. For Influenza A, the lowest correlation (0.09) was obtained between the M1-PA(X) protein pair with the proposed method. The highest correlation value (0.98) with the proposed method was calculated between the M1-M2 protein pair. The proposed method verified the interactions between protein pairs, which have been experimentally proven, with a high degree correlation value. These results indicated that the proposed method can be effective in predicting protein interactions.WOS:0006141222000252-s2.0-8509735182

Prediction of Protein-Protein Interactions with LSTM Deep Learning Model

3rd International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2019 -- 11 October 2019 through 13 October 2019 -- 156063Protein-protein interactions (PPI) has a vital role in molecular biology and bioinformatics since they are the key organisms which give information about cellular, its structure and its functions. In recent years many methods and techniques are proposed in order to perform PPI's yet they are suffered from operational time, and large costs as well as low prediction accuracy. In this study, we performed a deep learning approach to resolve these problems. To do that we introduced a LSTM architecture to predict protein-protein interactions by applying both ProtVec and protein signatures methods. VCP (valosin-containing protein) which is associated with H. Pylori is considered in this work. The performance of the method determined by log-loss, ROC, and classification accuracy. The proposed method showed a good predictive ability yet there is still more works need to be performed to improve the results of PPI prediction studies with respect to deep learning and machine learning approaches. © 2019 IEEE.2-s2.0-8507807293

EEG-Based Emotion Estimation with Different Deep Learning Models

4th International Conference on Computer Science and Engineering (UBMK) -- SEP 11-15, 2019 -- Samsun, TURKEY -- IEEE, IEEE Turkey SectEmotion has a vital role in people's routine lives. It can be expressed via voice, facial expressions, body languages, mimics with intentionally or unintentionally to interact with the environment. In this regard, it is required to understand the emotion better to interpret the emotions. Emotion is generally used in many areas including rehabilitation applications, braincomputer interactions, genome-wide applications, healthcare services etc. There are many studies exist about emotion recognition with different approaches based on facial expression, voice and physiological signals. Yet, the first two of them can give incorrect information about emotions since these approaches can be manipulated by subjects easily. Thus, the more reliable and more durable approach proposed including EEG signals. Although it gives valuable information on emotion, EEG-based emotion estimation applications have not reached the desired level since its abstract and pattern recognition methods (falsified feature extraction methods, false classifier algorithms, big data, etc.) used for that applications. EEG-based emotion estimation is a complicated assignment which requires deep features, many EEG channels, clear signals and classifier algorithms. Determining the features and analyzing them requires time, thus in this study, we applied deep learning to discriminate the positive/negative emotional states. Our proposed method includes three parts; i) Collecting EEG data ii) Preprocessed the EEG data to denoise the signal iii) Deep learning with AlexNet and VGG-16 We collected EEG signals from 28 various subjects aged between 21-28 via portable and wearable EEG device called Emotiv Epoc+ 14 channel. In order to collect the signals, we applied four different video games as stimuli (2 negative and 2 positive labelled games) and collected signals totally 20 minutes long for each subject. At the end of the EEG collection process, we obtained 1568 number of EEG samples (14x28x4). To collect more reliable and healthy information from signals we preprocessed our signals. Finally, we performed two different deep learning algorithms to determine the positive-negative emotions and to compare their results. It is observed that the classification accuracies differ with different algorithms and the classification performance was found 92,09% with VGG16 which is superior to AlexNet algorithm 87,76%.Firat University Scientific Research ProjectFirat University [TEKF.17.21]This study was supported by Firat University Scientific Research Project. Unit with Project Number: TEKF.17.21. Also, we would like to thank Asc. Prof. Murat Goner for his participation in the experimental setup and for interpreting the brain signalsWOS:0006098799000072-s2.0-8507622251

Comparison of deep learning approaches to predict COVID-19 infection

The SARS-CoV2 virus, which causes COVID-19 (coronavirus disease) has become a pandemic and has expanded all over the world. Because of increasing number of cases day by day, it takes time to interpret the laboratory findings thus the limitations in terms of both treatment and findings are emerged. Due to such limitations, the need for clinical decisions making system with predictive algorithms has arisen. Predictive algorithms could potentially ease the strain on healthcare systems by identifying the diseases. In this study, we perform clinical predictive models that estimate, using deep learning and laboratory data, which patients are likely to receive a COVID-19 disease. To evaluate the predictive performance of our models, precision, F1-score, recall, AUC, and accuracy scores calculated. Models were tested with 18 laboratory findings from 600 patients and validated with 10 fold cross-validation and train-test split approaches. The experimental results indicate that our predictive models identify patients that have COVID-19 disease at an accuracy of 86.66%, F1-score of 91.89%, precision of 86.75%, recall of 99.42%, and AUC of 62.50%. It is observed that predictive models trained on laboratory findings could be used to predict COVID-19 infection, and can be helpful for medical experts to prioritize the resources correctly. Our models (available at (https://github.com/burakalakuss/COVID-19-Clinical)) can be employed to assists medical experts in validating their initial laboratory findings, and can also be used for clinical prediction studies. (c) 2020 Elsevier Ltd. All rights reserved.WOS:0005963054000132-s2.0-85087932862PubMed: 3351910

Feature Selection with Sequential Forward Selection Algorithm from Emotion Estimation Based on EEG Signals

In this study, we conducted EEG-based emotion recognition on arousal-valence emotion model.We collected our own EEG data with mobile EEG device Emotiv Epoc+ 14 channel by applyingthe visual-aural stimulus. After collection we performed information measurement techniques,statistical methods and time-frequency attribute to obtain key features and created feature space.We wanted to observe the effect of features thus, we performed Sequential Forward Selectionalgorithm to reduce the feature space and compared the performance of accuracies for both allfeatures and diminished features. In the last part, we applied QSVM (Quadratic Support VectorMachines) to classify the features and contrasted the accuracies. We observed that diminishingthe feature space increased our average performance accuracy for arousal-valence dimensionfrom 55% to 65%

A novel Fibonacci hash method for protein family identification by using recurrent neural networks

Identification and classification of protein families are one of the most significant problem in bioinformatics and protein studies. It is essential to specify the family of a protein since proteins are highly used in smart drug therapies, protein functions, and, in some cases, phylogenetic trees. Some sequencing techniques provide researchers to identify the biological similarities of protein families and functions. Yet, determining these families with sequencing applications requires huge amount of time. Thus, a computer and artificial intelligence based classification system is needed to save time and avoid complexity in protein classification process. In order to designate the protein families with computer aided systems, protein sequences need to be converted to the numerical representations. In this paper, we provide a novel protein mapping method based on Fibonacci numbers and hashing table (FIBHASH). Each amino acid code is assigned to the Fibonacci numbers based on integer representations respectively. Later, these amino acid codes are inserted a hashing table with the size of 20 to be classified with recurrent neural networks. To determine the performance of the proposed mapping method, we used accuracy, f1-score, recall, precision, and AUC evaluation criteria. In addition, the results of evaluation metrics with other protein mapping techniques including EIIP, hydrophobicity, CPNR, Atchley factors, BLOSUM62, PAM250, binary one-hot encoding, and randomly encoded representations are compared. The proposed method showed a promising result with an accuracy of 92.77%, and 0.98 AUC score.WOS:0006144347000042-s2.0-8510104276

A Novel Protein Mapping Method for Predicting the Protein Interactions in COVID-19 Disease by Deep Learning

The new type of corona virus (SARS-COV-2) emerging in Wuhan, China has spread rapidly to the world and has become a pandemic. In addition to having a significant impact on daily life, it also shows its effect in different areas, including public health and economy. Currently, there is no vaccine or antiviral drug available to prevent the COVID-19 disease. Therefore, determination of protein interactions of new types of corona virus is vital in clinical studies, drug therapy, identification of preclinical compounds and protein functions. Protein-protein interactions are important to examine protein functions and pathways involved in various biological processes and to determine the cause and progression of diseases. Various high-throughput experimental methods have been used to identify protein-protein interactions in organisms, yet, there is still a huge gap in specifying all possible protein interactions in an organism. In addition, since the experimental methods used include cloning, labeling, affinity purification mass spectrometry, the processes take a long time. Determining these interactions with artificial intelligence-based methods rather than experimental approaches may help to identify protein functions faster. Thus, protein-protein interaction prediction using deep-learning algorithms has been employed in conjunction with experimental method to explore new protein interactions. However, to predict protein interactions with artificial intelligence techniques, protein sequences need to be mapped. There are various types and numbers of protein-mapping methods in the literature. In this study, we wanted to contribute to the literature by proposing a novel protein-mapping method based on the AVL tree. The proposed method was inspired by the fast search performance on the dictionary structure of AVL tree and was used to verify the protein interactions between SARS-COV-2 virus and human. First, protein sequences were mapped by both the proposed method and various protein-mapping methods. Then, the mapped protein sequences were normalized and classified by bidirectional recurrent neural networks. The performance of the proposed method was evaluated with accuracy, f1-score, precision, recall, and AUC scores. Our results indicated that our mapping method predicts the protein interactions between SARS-COV-2 virus proteins and human proteins at an accuracy of 97.76%, precision of 97.60%, recall of 98.33%, f1-score of 79.42%, and with AUC 89% in average.WOS:0006073343000012-s2.0-85101990062PubMed: 3343378

Emotion recognition with deep learning using GAMEEMO data set

Emotion recognition is actively used in brain-computer interface, health care, security, e-commerce, education and entertainment applications to increase and control human-machine interaction. Therefore, emotions affect people's lives and decision-making mechanisms throughout their lives. However, the fact that emotions vary from person to person, being an abstract concept and being dependent on internal and external factors makes the studies in this field difficult. In recent years, studies based on electroencephalography (EEG) signals, which perform emotion analysis in a more robust and reliable way, have gained momentum. In this article, emotion analysis based on EEG signals was performed to predict positive and negative emotions. The study consists of four parts. In the first part, EEG signals were obtained from the GAMEEMO data set. In the second stage, the spectral entropy values of the EEG signals of all channels were calculated and these values were classified by the bidirectional long-short term memory architecture in the third stage. In the last stage, the performance of the deep-learning architecture was evaluated with accuracy, sensitivity, specificity and receiver operating characteristic (ROC) curve. With the proposed method, an accuracy of 76.91% and a ROC value of 90% were obtained.WOS:0006049577000042-s2.0-8509891532