Protein Classification using Machine Learning and Statistical Techniques

2019 ◽  
Vol 13 ◽  
Author(s):  
Chhote Lal Prasad Gupta ◽  
Anand Bihari ◽  
Sudhakar Tripathi
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Protein Classification ◽  
Classification Techniques ◽  
Random Forest Classification ◽  
Machine Learning Classification ◽  
Classification Technique ◽  
Human Enzyme ◽  
Clinical Verification ◽  
Enzyme Class

Background: In recent era prediction of enzyme class from an unknown protein is one of the challenging tasks in bioinformatics. Day to day the number of proteins increases that causes difficulties in clinical verification and classification; as a result, the prediction of enzyme class gives a new opportunity to bioinformatics scholars. The machine learning classification technique helps in protein classification and predictions. But it is imperative to know which classification technique is more suited for protein classification. This study used human proteins data that is extracted from UniProtKB databank. Total 4368 protein data with 45 identified features has been used for experimental analysis. Objective: The prime objective of this article is to find an appropriate classification technique to classify the reviewed as well as un-reviewed human enzyme class of protein data. Also find the significance of different features in protein classification and prediction. Method: In this article, the ten most significant classification techniques such as CRT, QUEST, CHAID, C5.0, ANN, SVM, Bayesian, Random Forest, XgBoost and CatBoost has been used to classify the data and know the importance of features. To validate the result of different classification technique, the accuracy, precision, recall, F-measures, sensitivity, specificity, MCC, ROC and AUROC has been used. All experiment has been done with the help of SPSS Clementine and Python. Result: Above discussed classification techniques give different results and found that the data are imbalanced for class C4, C5, and C6. As a result, all of the classification technique gives acceptable accuracy above of 60% for these classes of data, but their precision value is very less or negligible. The experimental results highlight that the Random forest gives highest accuracy as well as AUROC among all, i.e., 96.84% and 0.945 respectively. And also have high precision and recall value. Conclusion: The experiment conducted and analyzed in this article highlight that the Random Forest classification technique can be used for protein of human enzyme classification and predictions.

scholarly journals Machine learning classification of significant tornadoes and hail in the U.S. using ERA5 proximity soundings

2021 ◽  
Author(s):  
Vittorio A. Gensini ◽  
Cody Converse ◽  
Walker S. Ashley ◽  
Mateusz Taszarek
Keyword(s):  
Machine Learning ◽  
Wind Speed ◽  
Random Forest ◽  
Characteristic Curve ◽  
Grid Point ◽  
Severe Weather ◽  
Theoretic Approach ◽  
Random Forest Classification ◽  
Machine Learning Classification ◽  
Forest Classification

AbstractPrevious studies have identified environmental characteristics that skillfully discriminate between severe and significant-severe weather events, but they have largely been limited by sample size and/or population of predictor variables. Given the heightened societal impacts of significant-severe weather, this topic was revisited using over 150 000 ERA5 reanalysis-derived vertical profiles extracted at the grid-point nearest—and just prior to—tornado and hail reports during the period 1996–2019. Profiles were quality-controlled and used to calculate 84 variables. Several machine learning classification algorithms were trained, tested, and cross-validated on these data to assess skill in predicting severe or significant-severe reports for tornadoes and hail. Random forest classification outperformed all tested methods as measured by cross-validated critical success index scores and area under the receiver operating characteristic curve values. In addition, random forest classification was found to be more reliable than other methods and exhibited negligible frequency bias. The top three most important random forest classification variables for tornadoes were wind speed at 500 hPa, wind speed at 850 hPa, and 0–500-m storm-relative helicity. For hail, storm-relative helicity in the 3–6 km and -10 to -30 °C layers, along with 0–6-km bulk wind shear, were found to be most important. A game theoretic approach was used to help explain the output of the random forest classifiers and establish critical feature thresholds for operational nowcasting and forecasting. A use case of spatial applicability of the random forest model is also presented, demonstrating the potential utility for operational forecasting. Overall, this research supports a growing number of weather and climate studies finding admirable skill in random forest classification applications.

Comparison of different Artificial Intelligence techniques to predict Diabetic Kidney Disease (Preprint)

2020 ◽  
Author(s):  
Satish Kumar ◽  
Mohamed Rafiullah ◽  
Khalid Siddiqui
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
High Risk ◽  
Random Forest ◽  
Diabetic Kidney Disease ◽  
Mean Squared Error ◽  
Confusion Matrix ◽  
Classification Techniques ◽  
Squared Error ◽  
Classification Technique

BACKGROUND Diabetic kidney disease (DKD) is a progressive disease that leads to loss of kidney function. As early intervention improves patient outcomes, it is essential to identify the patients who are at high risk of developing DKD. Artificial Intelligence methods apply different machine learning classification techniques to identify high-risk patients by building a predictive model from a given dataset. OBJECTIVE This study aims to find an accurate classification technique for predicting DKD by comparing different classification techniques applied to a DKD dataset using WEKA machine learning software. METHODS We analyzed the performance of nine different classification techniques on a DKD dataset with 410 instances and 18 attributes. 66% of the dataset was used to build a model, and 33% of the data was used for evaluating the model. The performance of classification techniques were assessed based on their execution time, accuracy, correctly and incorrectly classified instances, kappa statistics (K), mean absolute error, root mean squared error and true values of the confusion matrix. RESULTS Random Forest classifier was found to be the best performing technique with an accuracy of 76.5854% and a higher K value (0.5306) in comparison to other classifiers. Besides, it also showed the lowest root mean squared error rate (0.4007). From the confusion matrix, it was found that there were 46 false-positive instances and 50 false-negative instances from the Random Forest technique. CONCLUSIONS This study identified the Random Forest classification technique as the best performing classifier and accurate prediction method for DKD. CLINICALTRIAL NA

scholarly journals Human Activity Recognition using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 3553-3556
Author(s):  
Chaudhari Shraddha
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Activity Recognition ◽  
Human Activity ◽  
Recognition System ◽  
Human Activity Recognition ◽  
Machine Learning Techniques ◽  
K Nearest Neighbors ◽  
Random Forest Classification ◽  
Medical Health Care

Activity recognition in humans is one of the active challenges that find its application in numerous fields such as, medical health care, military, manufacturing, assistive techniques and gaming. Due to the advancements in technologies the usage of smartphones in human lives has become inevitable. The sensors in the smartphones help us to measure the essential vital parameters. These measured parameters enable us to monitor the activities of humans, which we call as human activity recognition. We have applied machine learning techniques on a publicly available dataset. K-Nearest Neighbors and Random Forest classification algorithms are applied. In this paper, we have designed and implemented an automatic human activity recognition system that independently recognizes the actions of the humans. This system is able to recognize the activities such as Laying, Sitting, Standing, Walking, Walking downstairs and Walking upstairs. The results obtained show that, the KNN and Random Forest Algorithms gives 90.22% and 92.70% respectively of overall accuracy in detecting the activities.

scholarly journals Classifications of Breast Cancer Diagnosis using Machine Learning

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Random Forest ◽  
Breast Cancer Diagnosis ◽  
Performance Comparison ◽  
Support Vector ◽  
Breast Cancer Dataset ◽  
K Nearest Neighbors ◽  
Cancer Dataset ◽  
Machine Learning Classification

Breast Cancer (BC) is amongst the most common and leading causes of deaths in women throughout the world. Recently, classification and data analysis tools are being widely used in the medical field for diagnosis, prognosis and decision making to help lower down the risks of people dying or suffering from diseases. Advanced machine learning methods have proven to give hope for patients as this has helped the doctors in early detection of diseases like Breast Cancer that can be fatal, in support with providing accurate outcomes. However, the results highly depend on the techniques used for feature selection and classification which will produce a strong machine learning model. In this paper, a performance comparison is conducted using four classifiers which are Multilayer Perceptron (MLP), Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Random Forest on the Wisconsin Breast Cancer dataset to spot the most effective predictors. The main goal is to apply best machine learning classification methods to predict the Breast Cancer as benign or malignant using terms such as accuracy, f-measure, precision and recall. Experimental results show that Random forest is proven to achieve the highest accuracy of 99.26% on this dataset and features, while SVM and KNN show 97.78% and 97.04% accuracy respectively. MLP shows the least accuracy of 94.07%. All the experiments are conducted using RStudio as the data mining tool platform.

PSIX-15 Assessment of machine learning algorithms for prediction of Aleutian disease in American mink

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 264-265
Author(s):  
Duy Ngoc Do ◽  
Guoyu Hu ◽  
Younes Miar
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Linear Models ◽  
American Mink ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Enzyme Linked Immunosorbent Assay ◽  
Linear Discriminant ◽  
Machine Learning Classification

Abstract American mink (Neovison vison) is the major source of fur for the fur industries worldwide and Aleutian disease (AD) is causing severe financial losses to the mink industry. Different methods have been used to diagnose the AD in mink, but the combination of several methods can be the most appropriate approach for the selection of AD resilient mink. Iodine agglutination test (IAT) and counterimmunoelectrophoresis (CIEP) methods are commonly employed in test-and-remove strategy; meanwhile, enzyme-linked immunosorbent assay (ELISA) and packed-cell volume (PCV) methods are complementary. However, using multiple methods are expensive; and therefore, hindering the corrected use of AD tests in selection. This research presented the assessments of the AD classification based on machine learning algorithms. The Aleutian disease was tested on 1,830 individuals using these tests in an AD positive mink farm (Canadian Centre for Fur Animal Research, NS, Canada). The accuracy of classification for CIEP was evaluated based on the sex information, and IAT, ELISA and PCV test results implemented in seven machine learning classification algorithms (Random Forest, Artificial Neural Networks, C50Tree, Naive Bayes, Generalized Linear Models, Boost, and Linear Discriminant Analysis) using the Caret package in R. The accuracy of prediction varied among the methods. Overall, the Random Forest was the best-performing algorithm for the current dataset with an accuracy of 0.89 in the training data and 0.94 in the testing data. Our work demonstrated the utility and relative ease of using machine learning algorithms to assess the CIEP information, and consequently reducing the cost of AD tests. However, further works require the inclusion of production and reproduction information in the models and extension of phenotypic collection to increase the accuracy of current methods.

Applying machine learning classification techniques to automate sky object cataloguing

1993 ◽  
Author(s):  
Usama M. Fayyad ◽  
Richard J. Doyle ◽  
W. Nick Weir ◽  
Stanislav Djorgovski
Keyword(s):  
Machine Learning ◽  
Classification Techniques ◽  
Machine Learning Classification
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