Mbl-2 gene polymorphisms in pediatric Burkitt lymphoma: an approach based on machine learning techniques

Introduction: Burkitt lymphoma belongs to the group of non-Hodgkin lymphomas. Although curable in 80% of less advanced stages, it presents in advanced stages in about 75% of cases in Brazil’s Northeast region, requiring urgent and intensive care in the early stages of treatment. Objectives: therefore, this study aimed to verify the participation of MBL-2 gene polymorphisms in the development of Burkitt lymphoma. Methods: In this article, computational approaches based on the Machine Learning technique were used, where we implemented the Random Forest and KMeans algorithms to classify patterns of individuals diagnosed with the disease and, therefore, differentiate them from healthy individuals. A group of 56 patients aged 0 to 18 years, with Burkitt lymphoma, from a reference hospital in the treatment of childhood cancer, was evaluated, together with a control group consisting of 150 samples, all of which were tested for exon 1 polymorphisms and the MBL2 gene -221 and -550 regions. Results: At first, an unsupervised classification was performed, which identified as two the number of groups that best represent the data present in our database, reaching 72.81% accuracy in the separation of patients and controls. Then, the supervised classification was performed, where the classifier obtained a 70.97% success rate, being possible to reach 75% accuracy in the best GridSearch configuration when performing a cross validation. Conclusion: It was not yet possible to conclude about the participation of the evaluated polymorphisms in the development of the BL, however the computational techniques used proved to be very promising for carrying out studies of this nature.

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Recent Advances in Machine Learning Based Prediction of RNA-protein Interactions

Protein and Peptide Letters ◽

10.2174/0929866526666190619103853 ◽

2019 ◽

Vol 26 (8) ◽

pp. 601-619 ◽

Cited By ~ 1

Author(s):

Amit Sagar ◽

Bin Xue

Keyword(s):

Machine Learning ◽

Protein Interaction ◽

Protein Interactions ◽

Experimental Methods ◽

Machine Learning Techniques ◽

Computational Techniques ◽

Biological Processes ◽

Complete Spectrum ◽

Future Developments ◽

Learning Techniques

The interactions between RNAs and proteins play critical roles in many biological processes. Therefore, characterizing these interactions becomes critical for mechanistic, biomedical, and clinical studies. Many experimental methods can be used to determine RNA-protein interactions in multiple aspects. However, due to the facts that RNA-protein interactions are tissuespecific and condition-specific, as well as these interactions are weak and frequently compete with each other, those experimental techniques can not be made full use of to discover the complete spectrum of RNA-protein interactions. To moderate these issues, continuous efforts have been devoted to developing high quality computational techniques to study the interactions between RNAs and proteins. Many important progresses have been achieved with the application of novel techniques and strategies, such as machine learning techniques. Especially, with the development and application of CLIP techniques, more and more experimental data on RNA-protein interaction under specific biological conditions are available. These CLIP data altogether provide a rich source for developing advanced machine learning predictors. In this review, recent progresses on computational predictors for RNA-protein interaction were summarized in the following aspects: dataset, prediction strategies, and input features. Possible future developments were also discussed at the end of the review.

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Automated Tongue Feature Extraction for ZHENG Classification in Traditional Chinese Medicine

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2012/912852 ◽

2012 ◽

Vol 2012 ◽

pp. 1-14 ◽

Cited By ~ 33

Author(s):

Ratchadaporn Kanawong ◽

Tayo Obafemi-Ajayi ◽

Tao Ma ◽

Dong Xu ◽

Shao Li ◽

...

Keyword(s):

Machine Learning ◽

Chinese Medicine ◽

Traditional Chinese Medicine ◽

Color Space ◽

Image Features ◽

Machine Learning Algorithms ◽

Disease Classification ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Control Group

ZHENG, Traditional Chinese Medicine syndrome, is an integral and essential part of Traditional Chinese Medicine theory. It defines the theoretical abstraction of the symptom profiles of individual patients and thus, used as a guideline in disease classification in Chinese medicine. For example, patients suffering from gastritis may be classified as Cold or Hot ZHENG, whereas patients with different diseases may be classified under the same ZHENG. Tongue appearance is a valuable diagnostic tool for determining ZHENG in patients. In this paper, we explore new modalities for the clinical characterization of ZHENG using various supervised machine learning algorithms. We propose a novel-color-space-based feature set, which can be extracted from tongue images of clinical patients to build an automated ZHENG classification system. Given that Chinese medical practitioners usually observe the tongue color and coating to determine a ZHENG type and to diagnose different stomach disorders including gastritis, we propose using machine-learning techniques to establish the relationship between the tongue image features and ZHENG by learning through examples. The experimental results obtained over a set of 263 gastritis patients, most of whom suffering Cold Zheng or Hot ZHENG, and a control group of 48 healthy volunteers demonstrate an excellent performance of our proposed system.

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MBL2 gene polymorphisms are not related to the occurrence of cerebrovascular disease in sickle cell anemia

Research Society and Development ◽

10.33448/rsd-v9i7.4240 ◽

2020 ◽

Vol 9 (7) ◽

pp. e439974240

Author(s):

Isabela Cristina Cordeiro Farias ◽

Taciana Furtado Mendonça-Belmont ◽

Patrícia Muniz Mendes Freire Moura ◽

Igor Farias Domingos ◽

Diego Arruda Falcão ◽

...

Keyword(s):

Cerebrovascular Disease ◽

Sickle Cell ◽

Sickle Cell Anemia ◽

Promoter Region ◽

Gene Polymorphisms ◽

Control Group ◽

Mbl2 Gene ◽

Number Of Patients ◽

Exon 1 ◽

The Impact

Objective: This study has as objective to verify whether MBL2 gene polymorphisms are related to the occurrence of cerebrovascular disease (CD) in sickle cell anemia (SCA) patients. Methods: Overall, 259 unrelated SCA patients were enrolled. The patients were divided into three groups: control group, stroke group ad range of risk group. Peripheral blood samples were collected and DNA extraction was performed. All patients were genotyped for exon 1, promoter region -221 and promoter region -550 of MBL2 gene, along with β-globin gene haplotypes. Results: Concerning the genotyping of the MBL2, there was no difference in the frequency of allelic and genotypic variants of the exon 1 and the promoter regions -221 and -550 of the MBL2 gene among the studied groups. Conclusion: Despite the small number of patients, and the lack of association between MBL2 polymorphisms and CD, our study represents an effort to understand the impact of MBL2 polymorphisms in the clinical outcome of patients with SCA.

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Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances

Journal of The Royal Society Interface ◽

10.1098/rsif.2017.0821 ◽

2018 ◽

Vol 15 (138) ◽

pp. 20170821 ◽

Cited By ~ 51

Author(s):

Aurore Lyon ◽

Ana Mincholé ◽

Juan Pablo Martínez ◽

Pablo Laguna ◽

Blanca Rodriguez

Keyword(s):

Machine Learning ◽

Computer Simulations ◽

Computational Methods ◽

Computational Modelling ◽

The Body ◽

Machine Learning Techniques ◽

Computational Techniques ◽

Ecg Analysis ◽

Surface Ecg ◽

Medical Advances

Widely developed for clinical screening, electrocardiogram (ECG) recordings capture the cardiac electrical activity from the body surface. ECG analysis can therefore be a crucial first step to help diagnose, understand and predict cardiovascular disorders responsible for 30% of deaths worldwide. Computational techniques, and more specifically machine learning techniques and computational modelling are powerful tools for classification, clustering and simulation, and they have recently been applied to address the analysis of medical data, especially ECG data. This review describes the computational methods in use for ECG analysis, with a focus on machine learning and 3D computer simulations, as well as their accuracy, clinical implications and contributions to medical advances. The first section focuses on heartbeat classification and the techniques developed to extract and classify abnormal from regular beats. The second section focuses on patient diagnosis from whole recordings, applied to different diseases. The third section presents real-time diagnosis and applications to wearable devices. The fourth section highlights the recent field of personalized ECG computer simulations and their interpretation. Finally, the discussion section outlines the challenges of ECG analysis and provides a critical assessment of the methods presented. The computational methods reported in this review are a strong asset for medical discoveries and their translation to the clinical world may lead to promising advances.

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Artificial Neural Networks and Machine Learning techniques applied to Ground Penetrating Radar: A review

Applied Computing and Informatics ◽

10.1016/j.aci.2018.10.001 ◽

2020 ◽

Vol ahead-of-print (ahead-of-print) ◽

Cited By ~ 7

Author(s):

Xisto L. Travassos ◽

Sérgio L. Avila ◽

Nathan Ida

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Artificial Neural Networks ◽

Ground Penetrating Radar ◽

Data Interpretation ◽

Machine Learning Techniques ◽

Computational Techniques ◽

Data Detection ◽

Artificial Neural ◽

Ground Penetrating

Ground Penetrating Radar is a multidisciplinary Nondestructive Evaluation technique that requires knowledge of electromagnetic wave propagation, material properties and antenna theory. Under some circumstances this tool may require auxiliary algorithms to improve the interpretation of the collected data. Detection, location and definition of target’s geometrical and physical properties with a low false alarm rate are the objectives of these signal post-processing methods. Basic approaches are focused in the first two objectives while more robust and complex techniques deal with all objectives at once. This work reviews the use of Artificial Neural Networks and Machine Learning for data interpretation of Ground Penetrating Radar surveys. We show that these computational techniques have progressed GPR forward from locating and testing to imaging and diagnosis approaches.

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Big data and democratic speech: Predicting deliberative quality using machine learning techniques

Methodological Innovations ◽

10.1177/20597991211010416 ◽

2021 ◽

Vol 14 (2) ◽

pp. 205979912110104

Author(s):

Eleonore Fournier-Tombs ◽

Michael K. MacKenzie

Keyword(s):

Machine Learning ◽

Speech Acts ◽

Large Scale ◽

Large Datasets ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Computational Techniques ◽

Learning Techniques ◽

Campaign Speeches ◽

Northern Territories

This article explores techniques for using supervised machine learning to study discourse quality in large datasets. We explain and illustrate the computational techniques that we have developed to facilitate a large-scale study of deliberative quality in Canada’s three northern territories: Yukon, Northwest Territories, and Nunavut. This larger study involves conducting comparative analyses of hundreds of thousands of parliamentary speech acts since the creation of Nunavut 20 years ago. Without computational techniques, we would be unable to conduct such an ambitious and comprehensive analysis of deliberative quality. The purpose of this article is to demonstrate the machine learning techniques that we have developed with the hope that they might be used and improved by other communications scholars who are interested in conducting textual analyses using large datasets. Other possible applications of these techniques might include analyses of campaign speeches, party platforms, legislation, judicial rulings, online comments, newspaper articles, and television or radio commentaries.

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Machine learning associated with respiratory oscillometry: a computer-aided diagnosis system for the detection of respiratory abnormalities in systemic sclerosis

BioMedical Engineering OnLine ◽

10.1186/s12938-021-00865-9 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Domingos S. M. Andrade ◽

Luigi Maciel Ribeiro ◽

Agnaldo J. Lopes ◽

Jorge L. M. Amaral ◽

Pedro L. Melo

Keyword(s):

Machine Learning ◽

Systemic Sclerosis ◽

Diagnostic Accuracy ◽

Group Versus ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Gradient Boosting ◽

Control Group ◽

Extreme Gradient Boosting

Abstract Introduction The use of machine learning (ML) methods would improve the diagnosis of respiratory changes in systemic sclerosis (SSc). This paper evaluates the performance of several ML algorithms associated with the respiratory oscillometry analysis to aid in the diagnostic of respiratory changes in SSc. We also find out the best configuration for this task. Methods Oscillometric and spirometric exams were performed in 82 individuals, including controls (n = 30) and patients with systemic sclerosis with normal (n = 22) and abnormal (n = 30) spirometry. Multiple instance classifiers and different supervised machine learning techniques were investigated, including k-Nearest Neighbors (KNN), Random Forests (RF), AdaBoost with decision trees (ADAB), and Extreme Gradient Boosting (XGB). Results and discussion The first experiment of this study showed that the best oscillometric parameter (BOP) was dynamic compliance, which provided moderate accuracy (AUC = 0.77) in the scenario control group versus patients with sclerosis and normal spirometry (CGvsPSNS). In the scenario control group versus patients with sclerosis and altered spirometry (CGvsPSAS), the BOP obtained high accuracy (AUC = 0.94). In the second experiment, the ML techniques were used. In CGvsPSNS, KNN achieved the best result (AUC = 0.90), significantly improving the accuracy in comparison with the BOP (p < 0.01), while in CGvsPSAS, RF obtained the best results (AUC = 0.97), also significantly improving the diagnostic accuracy (p < 0.05). In the third, fourth, fifth, and sixth experiments, different feature selection techniques allowed us to spot the best oscillometric parameters. They resulted in a small increase in diagnostic accuracy in CGvsPSNS (respectively, 0.87, 0.86, 0.82, and 0.84), while in the CGvsPSAS, the best classifier's performance remained the same (AUC = 0.97). Conclusions Oscillometric principles combined with machine learning algorithms provide a new method for diagnosing respiratory changes in patients with systemic sclerosis. The present study's findings provide evidence that this combination may help in the early diagnosis of respiratory changes in these patients.

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Bayesian Analysis of Energy and Count Rate Data for Detection of Low Count Rate Radioactive Sources

Volume 2: Facility Decontamination and Decommissioning; Environmental Remediation; Environmental Management/Public Involvement/Crosscutting Issues/Global Partnering ◽

10.1115/icem2013-96357 ◽

2013 ◽

Author(s):

John Klumpp

Keyword(s):

Machine Learning ◽

Count Rate ◽

Detection System ◽

Radiation Detection ◽

Radiation Detectors ◽

Sampling Distribution ◽

Machine Learning Techniques ◽

Computational Techniques ◽

Multiple Time ◽

Background Count

We propose a radiation detection system which generates its own discrete sampling distribution based on past measurements of background. The advantage to this approach is that it can take into account variations in background with respect to time, location, energy spectra, detector-specific characteristics (i.e. different efficiencies at different count rates and energies), etc. This would therefore be a “machine learning” approach, in which the algorithm updates and improves its characterization of background over time. The system would have a “learning mode,” in which it measures and analyzes background count rates, and a “detection mode,” in which it compares measurements from an unknown source against its unique background distribution. By characterizing and accounting for variations in the background, general purpose radiation detectors can be improved with little or no increase in cost. The statistical and computational techniques to perform this kind of analysis have already been developed. The necessary signal analysis can be accomplished using existing Bayesian algorithms which account for multiple channels, multiple detectors, and multiple time intervals. Furthermore, Bayesian machine-learning techniques have already been developed which, with trivial modifications, can generate appropriate decision thresholds based on the comparison of new measurements against a non-parametric sampling distribution.

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A critical review on predicting drug-drug reactions using machine learning techniques

Research Journal of Biotechnology ◽

10.25303/1610rjbt186188 ◽

2021 ◽

Vol 16 (10) ◽

pp. 186-188

Author(s):

A. Saran Kumar ◽

R. Rekha

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Computational Techniques ◽

Learning Approaches ◽

Drug Reactions ◽

Advantages And Disadvantages ◽

Learning Techniques ◽

Comprehensive Survey

Drug-Drug interaction (DDI) refers to change in the reaction of a drug when the person consumes other drug. It is the main cause of avertable bad drug reactions causing major issues on the patient’s health and the information systems. Many computational techniques have been used to predict the adverse effects of drug-drug interactions. However, these methods do not provide adequate information required for the prediction of DDI. Machine learning algorithms provide a set of methods which can increase the accuracy and success rate for well-defined issues with abundant data. This study provides a comprehensive survey on most popular machine learning and deep learning algorithms used by the researchers to predict DDI. In addition, the advantages and disadvantages of various machine learning approaches have also been discussed here.

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Machine Learning Associated With Respiratory Oscillometry: A Computer-Aided Diagnosis System for the Detection of Respiratory Abnormalities in Systemic Sclerosis

10.21203/rs.3.rs-144194/v1 ◽

2021 ◽

Author(s):

Domingos Andrade ◽

Luigi Ribeiro ◽

Agnaldo Lopes ◽

Jorge Amaral ◽

Pedro Lopes de Melo

Keyword(s):

Machine Learning ◽

Systemic Sclerosis ◽

Diagnostic Accuracy ◽

Learning Algorithm ◽

Group Versus ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Gradient Boosting ◽

Control Group ◽

Extreme Gradient Boosting

Abstract BackgroundThe use of machine learning (ML) methods would improve the diagnosis of respiratory changes in systemic sclerosis (SSc). This paper evaluates the performance of several ML algorithms associated with the respiratory oscillometry analysis to aid in the diagnostic of respiratory changes in SSc. We also find out the best configuration for this task.MethodsOscillometric and spirometric exams were performed in 82 individuals, including controls (n=30), and patients with systemic sclerosis with normal (n=22) and abnormal (n=30) spirometry. Multiple instance classifiers and different supervised machine learning techniques were investigated, including k-nearest neighbours (KNN), random forests (RF), AdaBoost with decision trees (ADAB), and Extreme Gradient Boosting (XGB).Results and discussionThe first experiment of this study showed that the best oscillometric parameter (BOP) was dynamic compliance. In the scenario Control Group versus Patients with Sclerosis and normal spirometry (CGvsPSNS), it provided moderate accuracy (AUC=0.77). In the scenario Control Group versus Patients with Sclerosis and Altered spirometry (CGvsPSAS), the BOP obtained high accuracy (AUC=0.94). In the second experiment, the ML techniques were used. In CGvsPSNS, KNN achieved the best result (AUC=0.90), significantly improving the accuracy in comparison with the BOP (p<0.01), while in CGvsPSAS, RF obtained the best results (AUC=0.97), also significantly improving the diagnostic accuracy (p<0.05). In the third, fourth, fifth, and sixth experiments, the use of different feature selection techniques allowed us to spot the best oscillometric parameters. They all show a small increase in diagnostic accuracy in CGvsPSNS, respectively 0.87,0.86, 0.82, 0.84, while in the CGvsPSAS, the performance of the best classifier remained the same (AUC=0.97). ConclusionsOscillometric principles combined with machine learning algorithm provides a new method for the diagnosis of respiratory changes in patients with systemic sclerosis. The findings of the present study provide evidence that this combination may play an important role in the early diagnosis of respiratory changes in these patients.

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