scholarly journals Machine Learning Detects Anti-DENV Signatures in Antibody Repertoire Sequences

2021 ◽  
Vol 4 ◽  
Author(s):  
Alexander Horst ◽  
Erand Smakaj ◽  
Eriberto Noel Natali ◽  
Deniz Tosoni ◽  
Lmar Marie Babrak ◽  
...  
Keyword(s):  
Machine Learning ◽  
Immune Response ◽  
Dengue Virus ◽  
Dengue Infection ◽  
Vaccine Design ◽  
World Health ◽  
Support Vector ◽  
Antibody Repertoire ◽  
Antibody Discovery ◽  
Antibody Sequence

Dengue infection is a global threat. As of today, there is no universal dengue fever treatment or vaccines unreservedly recommended by the World Health Organization. The investigation of the specific immune response to dengue virus would support antibody discovery as therapeutics for passive immunization and vaccine design. High-throughput sequencing enables the identification of the multitude of antibodies elicited in response to dengue infection at the sequence level. Artificial intelligence can mine the complex data generated and has the potential to uncover patterns in entire antibody repertoires and detect signatures distinctive of single virus-binding antibodies. However, these machine learning have not been harnessed to determine the immune response to dengue virus. In order to enable the application of machine learning, we have benchmarked existing methods for encoding biological and chemical knowledge as inputs and have investigated novel encoding techniques. We have applied different machine learning methods such as neural networks, random forests, and support vector machines and have investigated the parameter space to determine best performing algorithms for the detection and prediction of antibody patterns at the repertoire and antibody sequence levels in dengue-infected individuals. Our results show that immune response signatures to dengue are detectable both at the antibody repertoire and at the antibody sequence levels. By combining machine learning with phylogenies and network analysis, we generated novel sequences that present dengue-binding specific signatures. These results might aid further antibody discovery and support vaccine design.

scholarly journals Machine Learning based Human Fall Detection System

2021 ◽  
Vol 9 (VI) ◽  
pp. 2677-2682
Author(s):  
Nishanth P
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Wearable Sensors ◽  
Fall Detection ◽  
The Elderly ◽  
World Health ◽  
Support Vector ◽  
Data Output ◽  
Health Organization ◽  
Context Features

Falls have become one of the reasons for death. It is common among the elderly. According to World Health Organization (WHO), 3 out of 10 living alone elderly people of age 65 and more tend to fall. This rate may get higher in the upcoming years. In recent years, the safety of elderly residents alone has received increased attention in a number of countries. The fall detection system based on the wearable sensors has made its debut in response to the early indicator of detecting the fall and the usage of the IoT technology, but it has some drawbacks, including high infiltration, low accuracy, poor reliability. This work describes a fall detection that does not reliant on wearable sensors and is related on machine learning and image analysing in Python. The camera's high-frequency pictures are sent to the network, which uses the Convolutional Neural Network technique to identify the main points of the human. The Support Vector Machine technique uses the data output from the feature extraction to classify the fall. Relatives will be notified via mobile message. Rather than modelling individual activities, we use both motion and context information to recognize activities in a scene. This is based on the notion that actions that are spatially and temporally connected rarely occur alone and might serve as background for one another. We propose a hierarchical representation of action segments and activities using a two-layer random field model. The model allows for the simultaneous integration of motion and a variety of context features at multiple levels, as well as the automatic learning of statistics that represent the patterns of the features.

scholarly journals Simulation Model for Dynamics of Dengue with Innate and Humoral Immune Responses

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
S. D. Perera ◽  
S. S. N. Perera
Keyword(s):  
Immune Response ◽  
Immune Responses ◽  
Dengue Virus ◽  
Dengue Infection ◽  
Asymptomatic Infection ◽  
Severe Dengue ◽  
Viral Kinetics ◽  
Humoral Immune ◽  
Humoral Immune Responses ◽  
Kinetics Of

Dengue virus is a mosquito borne Flavivirus and the most prevalent arbovirus in tropical and subtropical regions around the world. The incidence of dengue has increased drastically over the last few years at an alarming rate. The clinical manifestation of dengue ranges from asymptomatic infection to severe dengue. Even though the viral kinetics of dengue infection is lacking, innate immune response and humoral immune response are thought to play a major role in controlling the virus count. Here, we developed a computer simulation mathematical model including both innate and adaptive immune responses to study the within-host dynamics of dengue virus infection. A sensitivity analysis was carried out to identify key parameters that would contribute towards severe dengue. A detailed stability analysis was carried out to identify relevant range of parameters that contributes to different outcomes of the infection. This study provides a qualitative understanding of the biological factors that can explain the viral kinetics during a dengue infection.

scholarly journals Machine learning versus logistic regression methods for 2-year mortality prognostication in a small, heterogeneous glioma database

2018 ◽  
Author(s):  
Sandip S Panesar ◽  
Rhett N D’Souza ◽  
Fang-Cheng Yeh ◽  
Juan C Fernandez-Miranda
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Machine Learning Techniques ◽  
World Health ◽  
Support Vector ◽  
Molecular Characteristics ◽  
Regression Methods ◽  
Learning Techniques ◽  
The World ◽  
Health Organization

AbstractBackgroundMachine learning (ML) is the application of specialized algorithms to datasets for trend delineation, categorization or prediction. ML techniques have been traditionally applied to large, highly-dimensional databases. Gliomas are a heterogeneous group of primary brain tumors, traditionally graded using histopathological features. Recently the World Health Organization proposed a novel grading system for gliomas incorporating molecular characteristics. We aimed to study whether ML could achieve accurate prognostication of 2-year mortality in a small, highly-dimensional database of glioma patients.MethodsWe applied three machine learning techniques: artificial neural networks (ANN), decision trees (DT), support vector machine (SVM), and classical logistic regression (LR) to a dataset consisting of 76 glioma patients of all grades. We compared the effect of applying the algorithms to the raw database, versus a database where only statistically significant features were included into the algorithmic inputs (feature selection).ResultsRaw input consisted of 21 variables, and achieved performance of (accuracy/AUC): 70.7%/0.70 for ANN, 68%/0.72 for SVM, 66.7%/0.64 for LR and 65%/0.70 for DT. Feature selected input consisted of 14 variables and achieved performance of 73.4%/0.75 for ANN, 73.3%/0.74 for SVM, 69.3%/0.73 for LR and 65.2%/0.63 for DT.ConclusionsWe demonstrate that these techniques can also be applied to small, yet highly-dimensional datasets. Our ML techniques achieved reasonable performance compared to similar studies in the literature. Though local databases may be small versus larger cancer repositories, we demonstrate that ML techniques can still be applied to their analysis, though traditional statistical methods are of similar benefit.

scholarly journals Preparedness and Mitigation by projecting the risk against COVID-19 transmission using Machine Learning Techniques

2020 ◽  
Author(s):  
Akshay Kumar ◽  
Farhan Mohammad Khan ◽  
Rajiv Gupta ◽  
Harish Puppala
Keyword(s):  
Machine Learning ◽  
Gaussian Process Regression ◽  
Machine Learning Techniques ◽  
World Health ◽  
Support Vector ◽  
Learning Tools ◽  
Learning Techniques ◽  
Health Organization ◽  
Respiratory Coronavirus ◽  
Criticality Index

AbstractThe outbreak of COVID-19 is first identified in China, which later spread to various parts of the globe and was pronounced pandemic by the World Health Organization (WHO). The disease of transmissible person-to-person pneumonia caused by the extreme acute respiratory coronavirus 2 syndrome (SARS-COV-2, also known as COVID-19), has sparked a global warning. Thermal screening, quarantining, and later lockdown were methods employed by various nations to contain the spread of the virus. Though exercising various possible plans to contain the spread help in mitigating the effect of COVID-19, projecting the rise and preparing to face the crisis would help in minimizing the effect. In the scenario, this study attempts to use Machine Learning tools to forecast the possible rise in the number of cases by considering the data of daily new cases. To capture the uncertainty, three different techniques: (i) Decision Tree algorithm, (ii) Support Vector Machine algorithm, and (iii) Gaussian process regression are used to project the data and capture the possible deviation. Based on the projection of new cases, recovered cases, deceased cases, medical facilities, population density, number of tests conducted, and facilities of services, are considered to define the criticality index (CI). CI is used to classify all the districts of the country in the regions of high risk, low risk, and moderate risk. An online dashpot is created, which updates the data on daily bases for the next four weeks. The prospective suggestions of this study would aid in planning the strategies to apply the lockdown/ any other plan for any country, which can take other parameters to define the CI.

scholarly journals Characterization of the IL-17 and CD4+ Th17 Cells in the Clinical Course of Dengue Virus Infections

Viruses ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 1435
Author(s):  
Luis Alberto Sánchez-Vargas ◽  
Karina Guadalupe Hernández-Flores ◽  
Pablo Thomas-Dupont ◽  
Irma Yadira Izaguirre-Hernández ◽  
Elvis Efraín Sánchez-Marce ◽  
...  
Keyword(s):  
Immune Response ◽  
Dengue Virus ◽  
Mononuclear Cells ◽  
Ex Vivo ◽  
Hemorrhagic Fever ◽  
High Serum ◽  
World Health ◽  
Interleukin 17 ◽  
Peripheral Blood Mononuclear ◽  
Blood Samples

The aims of this study were to determine the involvement of interleukin 17 (IL-17) and IL-17-producing cells in dengue pathogenesis. Blood samples from dengue virus (DENV)-infected patients were collected on different days after the onset of symptoms. Patients were classified according to 1997 World Health Organization guidelines. Our study examined 152 blood samples from dengue fever (DF, n = 109) and dengue hemorrhagic fever (DHF, n = 43) patients and 90 blood samples from healthy controls (HC). High serum concentrations of IL-17A and IL-22 were also associated with DHF (IL-17A [DHF vs. DF, p < 0.01; DHF vs. HC, p < 0.0001]; IL-22 [DHF vs. DF, p < 0.05; DHF vs. HC, p < 0.0001]). Moreover, there was a positive correlation between serum levels of IL-17A and IL-23, a key cytokine that promotes IL-17-based immune responses (r = 0.4089, p < 0.0001). Consistent with the IL-17-biased immune response in DHF patients, we performed ex vivo activation of peripheral blood mononuclear cells (PBMCs) from DHF patients and flow cytometry analysis showed a robust IL-17-biased immune response, characterized by a high frequency of CD4+IL-17+ producing cells. Our results suggests IL-17-producing cells and their related cytokines can play a prominent role in this viral disease.

scholarly journals Transcranial magnetic stimulation language mapping analysis revisited: Machine learning classification of 90 patients reveals distinct reorganization pattern in aphasic patients

2020 ◽  
Author(s):  
Ziqian Wang ◽  
Lucius Fekonja ◽  
Felix Dreyer ◽  
Peter Vajkoczy ◽  
Thomas Picht
Keyword(s):  
Machine Learning ◽  
Inferior Frontal Gyrus ◽  
Univariate Analysis ◽  
World Health ◽  
Support Vector ◽  
Who Grade ◽  
Machine Learning Classification ◽  
Svm Model ◽  
The Right ◽  
Language Network

AbstractRepetitive TMS (rTMS) allows to non-invasively and transiently disrupt local neuronal functioning. Its potential for mapping of language function is currently explored. Given the inter-individual heterogeneity of tumor impact on the language network and resulting rTMS derived functional mapping, we propose to use machine learning strategies to classify potential patterns of functional reorganization. We retrospectively included 90 patients with left perisylvian glioma tumors, world health organization (WHO) grade II-IV, affecting the language network. All patients underwent navigated rTMS language mappings. The severity of aphasia was assessed preoperatively using the Berlin Aphasia Score (BAS), which is adapted to the Aachener Aphasia Test (AAT). After spatial normalization to MNI 152 of all rTMS spots, we calculated the error rate (ER) in each cortical area by automated anatomical labeling parcellation (AAL) and used support vector machine (SVM) as a classifier for significant areas in relation to aphasia. 29 of 90 (32.2%) patients suffered from aphasia. Univariate analysis revealed 11 perisylvian AVOIs’ ERs (eight left, three right hemispheric) that were significantly higher in the aphasic than non-aphasic group (p < 0.05), depicting a broad, bihemispheric language network. After feeding the significant AVOIs into the SVM model, it showed that additional to age (w = 2.95), the ERs of right Frontal_Inf_Tri (w = 2.06) and left SupraMarginal (w = 2.05) and Parietal_Inf (w= 1.80) contributed more than other features to the model. The model’s sensitivity was 89.7%, the specificity was 82.0%, the overall accuracy was 81.1% and AUC was 88.7%. Our results demonstrate an increased vulnerability of the right inferior frontal gyrus to rTMS in patients suffering from aphasia due to left perisylvian gliomas. This confirms a functional relevant involvement of the right frontal area in relation to aphasia. While age as a feature improved our SVM model the most, the tumor location feature didn’t affect the SVM model. This finding indicates that general tumor induced network disconnection is relevant to aphasia and not necessarily related to specific lesion locations. Additionally, our results emphasize the decreasing potential for neuroplasticity with age.

scholarly journals Comparing of Data Mining Techniques for Predicting In-Hospital Mortality Among Patients with COVID-19

2021 ◽  
Author(s):  
Mostafa Shanbehzadeh ◽  
Azam Orooji ◽  
Hadi Kazemi-Arpanahi
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Hospital Mortality ◽  
Health Care Providers ◽  
Prediction Models ◽  
Confusion Matrix ◽  
World Health ◽  
Medical Decision ◽  
Support Vector ◽  
Care Providers

Introduction: The COVID-19 epidemic is currently fronting the worldwide health care systems with many qualms and unexpected challenges in medical decision-making and the effective sharing of medical resources. Machine Learning (ML)-based prediction models can be potentially advantageous to overcome these uncertainties. Objective: This study aims to train several ML algorithms to predict the COVID-19 in-hospital mortality and compare their performance to choose the best performing algorithm. Finally, the contributing factors scored using some feature selection methods. Material and Methods: Using a single-center registry, we studied the records of 1353 confirmed COVID-19 hospitalized patients from Ayatollah Taleghani hospital, Abadan city, Iran. We applied six feature scoring techniques and nine well-known ML algorithms. To evaluate the models’ performances, the metrics derived from the confusion matrix calculated. Results: The study participants were 1353 patients, the male sex found to be higher than the women (742 vs. 611), and the median age was 57.25 (interquartile 18-100). After feature scoring, out of 54 variables, absolute neutrophil/lymphocyte count and loss of taste and smell were found the top three predictors. On the other hand, platelet count, magnesium, and headache gained the lowest importance for predicting the COVID-19 mortality. Experimental results indicated that the Bayesian network algorithm with an accuracy of 89.31% and a sensitivity of 64.2 % has been more successful in predicting mortality. Conclusion: ML provides a reasonable level of accuracy in predicting. So, using the ML-based prediction models facilitate more responsive health systems and would be beneficial for timely identification of vulnerable patients to inform appropriate judgment by the health care providers. Abbreviation: Coronavirus Disease 2019 (COVID‐19), World Health Organization (WHO), Machine Learning (ML), Artificial Intelligence (AI), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Locally Weighted Learning (LWL), Clinical Decision Support System (CDSS)

scholarly journals Impact of Humoral Immune Response and Absorption Effect on Dynamics of Dengue Virus

2017 ◽  
Vol 13 (12) ◽  
pp. 157
Author(s):  
S.D. Perera ◽  
S.S.N. Perera
Keyword(s):  
Immune Response ◽  
Dengue Virus ◽  
Humoral Immune Response ◽  
Dengue Infection ◽  
Endemic Equilibrium ◽  
Absorption Effect ◽  
Globally Asymptotically Stable ◽  
Humoral Immune ◽  
Proposed Model ◽  
Global Threat

Dengue infection represents a global threat causing 50-100 million infections per year and placing half of the world’s population at risk. Even though how infection is controlled and cured rather remains a mystery, antibodies are thought to play a major role in clearing the virus. In this paper, we study the dynamics of dengue virus with humoral immune response and absorption effect. The proposed model incorporates a time delay in production of antibodies. The basic reproduction number R0 is computed and a detailed stability analysis is done. It was found that the model has 3 steady states, namely, infection free equilibrium, no immune equilibrium and the endemic equilibrium. Conditions for R0 were developed for the local stability of these 3 equilibrium states. The global stability was studied using appropriate Lyapunov function and LaSalle’s invariance principle. We then established a condition for which the endemic equilibrium point is globally asymptotically stable. Also it was observed that the virus count goes to negligible levels within 7-14 days after the onset of symptoms.

scholarly journals Identification of Significant Climatic Risk Factors and Machine Learning Models in Dengue Outbreak Prediction

2019 ◽  
Author(s):  
Felestin Yavari Nejad ◽  
Kasturi Dewi Varathan
Keyword(s):  
Machine Learning ◽  
Risk Factor ◽  
Dengue Fever ◽  
World Health ◽  
Support Vector ◽  
Strong Impact ◽  
Learning Models ◽  
Dengue Outbreak ◽  
Depth Analysis ◽  
Machine Learning Models

Abstract Background: Dengue fever is a widespread viral disease and one of the world’s main pandemic vector-borne infections and serious hazard to humanity. According to the World Health Organization (WHO), the incidence of dengue has grown dramatically worldwide in recent decades. The WHO currently estimates an annual incidence of 50–100 million dengue infections worldwide. Until today there is no tested vaccine or treatment to stop or prevent dengue fever thus the importance of dengue outbreak prediction is significant. The current issue in dengue outbreak prediction is accuracy. There are a limited number of studies that look at in depth analysis of climate factors in dengue outbreak prediction. Methods: In this study, the most significant and important climatic factors that contribute to dengue outbreak were identified. These factors were used as input parameters on machine learning models. The models were trained and evaluated based on four-year data from January 2010 to December 2013 in Malaysia. Results: This work provides two main contributions. A new risk factor, which was called TempeRain Factor (TRF), was determined and used as an input parameter for dengue prediction outbreak model. Moreover, the TRF was applied to demonstrate that its strong impact on dengue outbreaks. Experimental results showed that Support Vector Machine (SVM) with the newly identified meteorological risk factor in this study resulted in higher accuracy of 98.09% and reduced the root mean square error to 0.098 for predicting dengue outbreak. Conclusions: This research managed to explore on the factors that are being used in dengue outbreak prediction systems. The main contribution of this paper is in identifying new significant factors that contribute in dengue outbreak prediction. From the evaluation, we managed to obtain a significant improvement in accuracy of the machine-learning model in dengue outbreak prediction.

Role of machine learning algorithms in timely prediction of the infectious outbreak: COVID-19 (Preprint)

2020 ◽  
Author(s):  
Atika Qazi ◽  
Khulla Naseer ◽  
Javaria Qazi ◽  
Muhammad Abo
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Data ◽  
Learning Algorithms ◽  
Epidemiological Data ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Series Data ◽  
Systems Science ◽  
Support Vector

UNSTRUCTURED Well-timed forecast of infectious outbreaks using time-series data can help in proper planning of public health measures. If the forecasts are generated from machine learning algorithms, they can be used to manage resources where most needed. Here we present a support vector machine (SVM) model using epidemiological data provided by Johns Hopkins University Centre for Systems Science and Engineering (JHU CCSE), world health organization (WHO), Center for Disease Control and Prevention (CDC) to predict upcoming data before official declaration by WHO. Our study conducted on the time series data available from 22nd January till 10th March 2020 reveals that COVID-19 was spreading at an alarming rate and progressing towards a pandemic. If machine learning algorithms are used to predict the dynamics of an infectious outbreak future strategies can help in better management. Besides exploratory data analysis (EDA) highlights the importance of quarantine measures taken at the onset of this endemic by China and world leadership in containing the initial COVID-19 transmission. Nevertheless, when quarantine measures were relaxed due to extreme scrutiny a sharp upsurge was seen in COVID-19 transmission. The initial insight that confirmed COVID-19 cases are increasing as these got the highest number of effects for our selected dataset from 22nd January-10th March 2020 i.e. 126,344 (64%). The recovered cases are 68289 (34%) and the death rate is around 2%. The model presented here is flexible and can include uncertainty about outbreak dynamics and can be a significant tool for combating future outbreaks.

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