Deep Learning-Based Approach for Missing Data Imputation

The missing values in the datasets are a problem that will decrease the machine learning performance. New methods arerecommended every day to overcome this problem. The methods of statistical, machine learning, evolutionary and deeplearning are among these methods. Although deep learning methods is one of the popular subjects of today, there are limitedstudies in the missing data imputation. Several deep learning techniques have been used to handling missing data, one of themis the autoencoder and its denoising and stacked variants. In this study, the missing value in three different real-world datasetswas estimated by using denoising autoencoder (DAE), k-nearest neighbor (kNN) and multivariate imputation by chainedequations (MICE) methods. The estimation success of the methods was compared according to the root mean square error(RMSE) criterion. It was observed that the DAE method was more successful than other statistical methods in estimating themissing values for large datasets

The machine learning approach for predicting the number of intensive care, intubated patients and death: The COVID-19 pandemic in Turkey

The coronavirus infection outbreak started in Wuhan city, China, in December 2019 (COVID-19) and affected more than 200 countries in a year. The number of patients dying from and infected with COVID-19 is increasing at an alarming rate in almost all affected countries. One of the most important factors in the COVID-19 death and case rates is the care of intensive care patients. The management of COVID-19 patients who need acute and/ or critical respiratory care has created a significant difficulty for healthcare systems worldwide. To prevent the further spread of COVID-19 around the world and to fight the disease, non-clinical computer-aided quick solutions such as artificial intelligence and machine learning are needed. Prediction techniques evaluate past situations and enable predictions about the future situation. In this study, using the dataset created from the data received from the World Health Organization and national database, the numbers of intensive care, intubated patients, and deaths from COVID-19 in flukey were predicted by the random forest, bagging, support vector regression, classification and regression trees, and k-nearest neighbors machine learning regression methods. In this study, the random forest method has been the most successful algorithm for predicting the number of intensive care patients (r = 0.8698, RMSE = 188.5, MAE = 135.1, MAPE = 13%), the number of intubated patients (r = 0.9846, RMSE = 47.1, MAE = 39.7, MAPE = 9.2%), and the number of deaths (r = 0.9994, RMSE = 1.2, MAE = 0.9, MAPE = 3.5%). The results in this study, it has been shown that machine learning methods, which have been successfully applied in other epidemic diseases, will be successfully applied in the COVID-19 pandemic

Fuzzy Rule-Based System for Predicting Daily Case in COVID-19 Outbreak

4th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2020 -- 22 October 2020 through 24 October 2020 -- -- 165025The Covid-19 outbreak appeared in Wuhan in December 2019 and spread rapidly all over the world. The Covid-19 disease does not yet have a clinically proven vaccine and drug for treatment. The most important physical factors in reducing the spread of the epidemic are washing hands, reducing social distance and using a mask. Today in addition to clinical studies, computer-aided studies are also widely carried out for Covid-19 outbreak. Artificial intelligence methods are successfully applied in epidemic studies. In this study, fuzzy rule basing system (FRBS) used to predict the number of Covid-19 daily cases. As a result of the study, the number of daily cases was successfully estimated with FRBS (R2 = 0.96, MAE = 186 and RMSE = 254).. © 2020 IEEE

A novel ensemble fuzzy classification model in SARS-CoV-2 B-cell epitope identification for development of protein-based vaccine

B-cell epitope prediction research has received growing interest since the development of the first method. B-cell epitope identification with the aid of an accurate prediction method is one of the most important steps in epitope-based vaccine development, immunodiagnostic testing, antibody production, disease diagnosis, and treatment. Nevertheless, using experimental methods in epitope mapping is very time-consuming, costly, and labor-intensive. Therefore, although successful predictions with in silico methods are very important in epitope prediction, there are limited studies in this area. The aim of this study is to propose a new approach for successfully predicting B-cell epitopes for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this study, the SARS-CoV B-cell epitope prediction performances of different fuzzy learning classification models genetic cooperative competitive learning (GCCL), fuzzy genetics-based machine learning (GBML), Chi's method (CHI), Ishibuchi's method with weight factor (W), structural learning algorithm on vague environment (SLAVE) and the state-of-the-art ensemble fuzzy classification model were compared. The obtained results showed that the proposed ensemble approach has the lowest error in SARS-CoV B-cell epitope estimation compared to the base fuzzy learners (average error rates; ensemble fuzzy=8.33, GCCL=30.42, GBML=23.82, CHI=29.17, W=46.25, and SLAVE=20.42). SARS-CoV and SARS-CoV-2 have high genome similarities. Therefore, the most successful method determined for SARS-CoV B-cell epitope prediction was used in SARS-CoV-2 cell epitope prediction. Finally, the eventual B-cell epitope prediction results obtained for SARS-CoV-2 with the ensemble fuzzy classification model were compared with the epitope sequences predicted by the BepiPred server and immunoinformatics studies in the literature for the same protein sequences according to VaxiJen 2.0 scores. We hope that the developed epitope prediction method will help design effective vaccines and drugs against future outbreaks of the coronavirus family, especially SARS-CoV-2 and its possible mutations. © 2021 Elsevier B.V.121E326This study was supported by The Scientific and Technological Research Council of Turkey-TÜBİTAK (Project Number: 121E326 ).This study was supported by The Scientific and Technological Research Council of Turkey-T?B?TAK (Project Number: 121E326)

A new approach for determining SARS-CoV-2 epitopes using machine learning-based in silico methods

The emergence of machine learning-based in silico tools has enabled rapid and high-quality predictions in the biomedical field. In the COVID-19 pandemic, machine learning methods have been used in many topics such as predicting the death of patients, modeling the spread of infection, determining future effects, diagnosis with medical image analysis, and forecasting the vaccination rate. However, there is a gap in the literature regarding identifying epitopes that can be used in fast, useful, and effective vaccine design using machine learning methods and bioinformatics tools. Machine learning methods can give medical biotechnologists an advantage in designing a faster and more successful vaccine. The motivation of this study is to propose a successful hybrid machine learning method for SARS-CoV-2 epitope prediction and to identify nonallergen, nontoxic, antigen peptides that can be used in vaccine design from the predicted epitopes with bioinformatics tools. The identified epitopes will be effective not only in the design of the COVID-19 vaccine but also against viruses from the SARS family that may be encountered in the future. For this purpose, epitope prediction performances of random forest, support vector machine, logistic regression, bagging with decision tree, k-nearest neighbor and decision tree methods were examined. In the SARS-CoV and B-cell datasets used for education in the study, epitope estimation was performed again after the datasets were balanced with the synthetic minority oversampling technique (SMOTE) method since the epitope class samples were in the minority compared to the nonepitope class. The experimental results obtained were compared and the most successful predictions were obtained with the random forest (RF) method. The epitope prediction performance in balanced datasets was found to be higher than that in the original datasets (94.0% AUC and 94.4% PRC for the SMOTE-SARS-CoV dataset; 95.6% AUC and 95.3% PRC for the SMOTE-B-cell dataset). In this study, 252 peptides out of 20312 peptides were determined to be epitopes with the SMOTE-RF-SVM hybrid method proposed for SARS-CoV-2 epitope prediction. Determined epitopes were analyzed with AllerTOP 2.0, VaxiJen 2.0 and ToxinPred tools, and allergic, nonantigen, and toxic epitopes were eliminated. As a result, 11 possible nonallergic, high antigen and nontoxic epitope candidates were proposed that could be used in protein-based COVID-19 vaccine design (“VGGNYNY”, “VNFNFNGLTG”, “RQIAPGQTGKI”, “QIAPGQTGKIA”, “SYECDIPIGAGI”, “STFKCYGVSPTKL”, “GVVFLHVTYVPAQ”, “KNHTSPDVDLGDI”, “NHTSPDVDLGDIS”, “AGAAAYYVGYLQPR”, “KKSTNLVKNKCVNF”). It is predicted that the few epitopes determined by machine learning-based in silico methods will help biotechnologists design fast and accurate vaccines by reducing the number of trials in the laboratory environment. © 2022 Elsevier LtdTürkiye Bilimsel ve Teknolojik Araştirma Kurumu, TÜBITAK: 121E326This study was supported by Turkish Scientific and Technical Research Council, Turkey-TÜBİTAK (Project Number: 121E326).This study was supported by Turkish Scientific and Technical Research Council, Turkey -TÜBİTAK (Project Number: 121E326 )

Effect of Imputation Methods in the Classifier Performance

Missing values in a dataset present an important problem for almost any traditional and modernstatistical method since most of these methods were developed under the assumption that thedataset was complete. However, in the real world, no complete datasets are available and theissue of missing data is frequently encountered in veterinary field studies as in other fields.While the imputation of missing data is important in veterinary field studies where data miningis newly starting to be implemented, another important issue is how it should be imputed. Thisis because in many studies observations with any variables having missing values are beingremoved or they are completed by traditional methods. In recent years, while alternativeapproaches are widely available to prevent the removal of observations with missing values,they are being used rarely. The aim of this study is to examine mean, median, nearest neighbors,MICE and missForest methods to impute the simulated missing data which is the randomlyremoved with varying frequencies (5 to 25% by 5%) from the original veterinary dataset. Thenhighly accurate methods selected to impute the original dataset for observation of influence inclassifier performance and to determine the optimal imputation method for the original dataset

Computer-aided diagnosis in neonatal lambs

In our country, the number of small ruminant animals is decreasing day by day due to various reasons. In parallel with the decrease in the number of small ruminants, significant decreases are seen in animal production. One way to prevent the reduction in the number of small ruminants is to be able to make successful predictions and analysis related to the diagnosis. Thanks to computer-aided diagnostic studies performed with machine learning, the quality of health services increases while the costs of the health sector decrease. The aim of this study is to perform computer aided diagnosis in neonatal lambs using machine learning methods. Hence in study, decision tree, naive bayes, k-nearest neighbors, artificial neural networks and random forest methods were used. The performances of these classification methods were analyzed with accuracy, balanced accuracy, specifity, recall, F-measure, kappa and area under the ROC curve (AUC) criteria. As a result of the study, the Naive bayes method more successful results than other methods for computer aided diagnosis produced. It is very important that, the Naive bayes method is simple and easy to apply, achieves more successful results than other complex methods

Identification and recognition of animals from biometric markers using computer vision approaches: a review

Although classic methods (such as ear tagging, marking, etc.) are generally used for animal identification and recognition, biometric methods have gained popularity in recent years due to the advantages they offer. Systems utilizing biometric markers have been developed for various purposes in animal management, including more effective and accurate tracking of animals, vaccination, disease management, and prevention of theft and fraud. Animals" irises, retinas, faces, muzzle, and body patterns contain unique biometric markers. The use of these markers in computer vision approaches for animal identification and tracking systems has become a highly effective and promising research area in recent years. This review aims to provide a general overview of the latest developments in image processing approaches for animal identification and recognition applications. In this review, we examined in detail all relevant studies we could access from different electronic databases for each biometric method. Afterward, the opportunities and challenges of classical and biometric methods were compared. We anticipate that this study, which conducts a literature review on animal identification and recognition based on computer vision approaches, will shed light on future research towards developing automated systems with biometric methods

A Primary Leiomyosarcoma of the Thyroid Gland: A Case and Literature Review

Introduction: We want to present a rare case of Primary leiomyosarcoma of the thyroid (PLT) gland and review the literature on PLT including the differential diagnoses, pathology, and alternative treatment strategies.Presentation of Case: A 56-year-old man who underwent left side total thyroidectomy with diagnosis of substernal goiter. On pathologic examination, three different pathology clinics had a common opinion that this was a grade 3 pleomorphic sarcoma of thyroid itself. Positron Emission Tomography (PET/CT) obtained one month after surgery displayed no distant metastases. Loco regional radiotherapy (RT) to the thyroid bed was delivered up to a dose of 59.4 Gray (Gy) in 1.8 Gy daily fractions. PET/CT obtained three months after RT showed bilateral multiple lung metastases without loco regional recurrence. The patient received 6 courses of doxorubicin and cyclophosphamide based chemotherapy. A new PET/CT scan showed only two metabolically active metastases on both lungs. Because of disappearance of small metastases, the patient underwent sequential bilateral metastasectomy in one month interval. Pathology results verified the metastases of PLT. The patient is still alive without any signs of disease 6 years after RT and he is the only long surviving case reported up to now.Conclusion: The treatment protocols for PLT have not been well established yet, because of their rareness and poor prognosis. We believe that our case may be directive for PLT treatment

Crizotinib efficacy and safety in patients with advanced NSCLC harboring MET alterations: A real-life data of Turkish Oncology Group

Crizotinib is a multikinase inhibitor, effective in non-small cell lung cancer (NSCLC) harboring mesenchymal-epidermal transition (MET) alterations. Although small prospective studies showed efficacy and safety of crizotinib in NSCLC with MET alterations, there is limited real-life data. Aim of this study is to investigate real-life efficacy and safety of crizotinib in patients with advanced NSCLC harboring MET alterations. This was a retrospective, multicenter (17 centers) study of Turkish Oncology Group. Patients' demographic, histological data, treatment, response rates, survival outcomes, and toxicity data were collected. Outcomes were presented for the study population and compared between MET alteration types. Total of 62 patients were included with a median age of 58.5 (range, 26-78). Major histological type was adenocarcinoma, and 3 patients (4.8%) had sarcomatoid component. The most common MET analyzing method was next generation sequencing (90.3%). MET amplification and mutation frequencies were 53.2% (n = 33) and 46.8% (n = 29), respectively. Overall response rate and disease control rate were 56.5% and 74.2% in whole study population, respectively. Median progression free survival (PFS) was 7.2 months (95% confidence interval [CI]: 3.8-10.5), and median overall survival (OS) was 18.7 months (95% CI: 13.7-23.7), regardless of treatment line. Median PFS was 6.1 months (95% CI: 5.6-6.4) for patients with MET amplification, whereas 14.3 months (95% CI: 6.7-21.7) for patients with MET mutation (P = .217). Median PFS was significantly longer in patients who have never smoked (P = .040), have good performance score (P < .001), and responded to the treatment (P < .001). OS was significantly longer in patients with MET mutation (25.6 months, 95% CI: 15.9-35.3) compared to the patients with MET amplification (11.0 months; 95% CI: 5.2-16.8) (P = .049). In never-smokers, median OS was longer than smoker patients (25.6 months [95% CI: 11.8-39.3] vs 16.5 months [95% CI: 9.3-23.6]; P = .049). The most common adverse effects were fatigue (50%), peripheral edema (21%), nausea (29%) and diarrhea (19.4%). Grade 3 or 4 adverse effects were observed in 6.5% of the patients. This real-life data confirms efficacy and safety of crizotinib in the treatment of advanced NSCLC harboring MET alteration