scholarly journals Integration of shared-pathogen networks and machine learning reveals the key aspects of zoonoses and predicts mammalian reservoirs

2020 ◽  
Vol 287 (1920) ◽  
pp. 20192882 ◽  
Author(s):  
Maya Wardeh ◽  
Kieran J. Sharkey ◽  
Matthew Baylis
Keyword(s):  
Machine Learning ◽  
Human Health ◽  
Well Being ◽  
Pathogen Transmission ◽  
Learning Approaches ◽  
Economic Stability ◽  
Ensemble Models ◽  
Network Characteristics ◽  
Key Factor ◽  
Key Aspects

Diseases that spread to humans from animals, zoonoses, pose major threats to human health. Identifying animal reservoirs of zoonoses and predicting future outbreaks are increasingly important to human health and well-being and economic stability, particularly where research and resources are limited. Here, we integrate complex networks and machine learning approaches to develop a new approach to identifying reservoirs. An exhaustive dataset of mammal–pathogen interactions was transformed into networks where hosts are linked via their shared pathogens. We present a methodology for identifying important and influential hosts in these networks. Ensemble models linking network characteristics with phylogeny and life-history traits are then employed to predict those key hosts and quantify the roles they undertake in pathogen transmission. Our models reveal drivers explaining host importance and demonstrate how these drivers vary by pathogen taxa. Host importance is further integrated into ensemble models to predict reservoirs of zoonoses of various pathogen taxa and quantify the extent of pathogen sharing between humans and mammals. We establish predictors of reservoirs of zoonoses, showcasing host influence to be a key factor in determining these reservoirs. Finally, we provide new insight into the determinants of zoonosis-sharing, and contrast these determinants across major pathogen taxa.

scholarly journals Can Machine Learning Predict Stress Reduction Based on Wearable Sensors’ Data Following Relaxation at Workplace? A Pilot Study

Processes ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 448
Author(s):  
Alessandro Tonacci ◽  
Alessandro Dellabate ◽  
Andrea Dieni ◽  
Lorenzo Bachi ◽  
Francesco Sansone ◽  
...  
Keyword(s):  
Machine Learning ◽  
Stress Reduction ◽  
Wearable Sensors ◽  
Well Being ◽  
Significant Heterogeneity ◽  
Learning Approaches ◽  
Significant Difference ◽  
Group A ◽  
Group B ◽  
Audio Video

Nowadays, psychological stress represents a burdensome condition affecting an increasing number of subjects, in turn putting into practice several strategies to cope with this issue, including the administration of relaxation protocols, often performed in non-structured environments, like workplaces, and constrained within short times. Here, we performed a quick relaxation protocol based on a short audio and video, and analyzed physiological signals related to the autonomic nervous system (ANS) activity, including electrocardiogram (ECG) and galvanic skin response (GSR). Based on the features extracted, machine learning was applied to discriminate between subjects benefitting from the protocol and those with negative or no effects. Twenty-four healthy volunteers were enrolled for the protocol, equally and randomly divided into Group A, performing an audio-video + video-only relaxation, and Group B, performing an audio-video + audio-only protocol. From the ANS point of view, Group A subjects displayed a significant difference in the heart rate variability-related parameter SDNN across the test phases, whereas both groups displayed a different GSR response, albeit at different levels, with Group A displaying greater differences across phases with respect to Group B. Overall, the majority of the volunteers enrolled self-reported an improvement of their well-being status, according to structured questionnaires. The use of neural networks helped in discriminating those with a positive effect of the relaxation protocol from those with a negative/neutral impact based on basal autonomic features with a 79.2% accuracy. The results obtained demonstrated a significant heterogeneity in autonomic effects of the relaxation, highlighting the importance of maintaining a structured, well-defined protocol to produce significant benefits at the ANS level. Machine learning approaches can be useful to predict the outcome of such protocols, therefore providing subjects less prone to positive responses with personalized advice that could improve the effect of such protocols on self-relaxation perception.

scholarly journals Machine Learning Approaches Reveal That the Number of Tests Do Not Matter to the Prediction of Global Confirmed COVID-19 Cases

2020 ◽  
Vol 3 ◽  
Author(s):  
Md Hasinur Rahaman Khan ◽  
Ahmed Hossain
Keyword(s):  
Machine Learning ◽  
Well Being ◽  
Vital Role ◽  
Health Care Facilities ◽  
Public Health Care ◽  
Machine Learning Techniques ◽  
Disease Spread ◽  
Cumulative Number ◽  
Learning Approaches ◽  
Learning Techniques

Coronavirus disease 2019 (COVID-19) has developed into a global pandemic, affecting every nation and territory in the world. Machine learning-based approaches are useful when trying to understand the complexity behind the spread of the disease and how to contain its spread effectively. The unsupervised learning method could be useful to evaluate the shortcomings of health facilities in areas of increased infection as well as what strategies are necessary to prevent disease spread within or outside of the country. To contribute toward the well-being of society, this paper focusses on the implementation of machine learning techniques for identifying common prevailing public health care facilities and concerns related to COVID-19 as well as attitudes to infection prevention strategies held by people from different countries concerning the current pandemic situation. Regression tree, random forest, cluster analysis and principal component machine learning techniques are used to analyze the global COVID-19 data of 133 countries obtained from the Worldometer website as of April 17, 2020. The analysis revealed that there are four major clusters among the countries. Eight countries having the highest cumulative infected cases and deaths, forming the first cluster. Seven countries, United States, Spain, Italy, France, Germany, United Kingdom, and Iran, play a vital role in explaining the 60% variation of the total variations by us of the first component characterized by all variables except for the rate variables. The remaining countries explain only 20% of the variation of the total variation by use of the second component characterized by only rate variables. Most strikingly, the analysis found that the variable number of tests by the country did not play a vital role in the prediction of the cumulative number of confirmed cases.

Recent trends on the development of machine learning approaches for the prediction of lysine acetylation sites

2021 ◽  
Vol 28 ◽  
Author(s):  
Shaherin Basith ◽  
Hye Jin Chang ◽  
Saraswathy Nithiyanandam ◽  
Tae Hwan Shin ◽  
Balachandran Manavalan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Process Time ◽  
Great Promise ◽  
Lysine Acetylation ◽  
Learning Approaches ◽  
Regulatory Processes ◽  
Acetylation Site ◽  
Lysine Residues ◽  
Software Utility ◽  
Key Aspects

: Acetylation on lysine residues is considered as one of the most potent protein post-translational modifications owing to its crucial role in cellular metabolism and regulatory processes. Recent advances in experimental techniques has unraveled several lysine acetylation substrates and sites. However, towing to its cost-ineffectiveness, cumbersome process, time-consumption, and labor-intensiveness, several efforts have geared towards the development of computational tools. In particular, machine learning (ML)-based approaches hold great promise in the rapid discovery of lysine acetylation modification sites, which could be witnessed by the growing number of prediction tools. Recently, several ML methods have been developed for the prediction of lysine acetylation sites owing to their time- and cost-effectiveness. In this review, we present a complete survey of the state-of-the-art ML predictors for lysine acetylation. We discuss about a variety of key aspects for developing a successful predictor, including operating ML algorithms, feature selection methods, validation techniques, and software utility. Initially, we review about lysine acetylation site databases, current ML approaches, working principles, and their performances. Lastly, we discuss the shortcomings and future directions of ML approaches in the prediction of lysine acetylation sites. This review may act as a useful guide for the experimentalists in choosing a right ML tool for their research. Moreover, it may help bioinformaticians in the development of more accurate and advanced ML-based predictors in protein research.

scholarly journals Weak-lensing shear measurement with machine learning

2019 ◽  
Vol 621 ◽  
pp. A36 ◽  
Author(s):  
M. Tewes ◽  
T. Kuntzer ◽  
R. Nakajima ◽  
F. Courbin ◽  
H. Hildebrandt ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gravitational Lensing ◽  
Large Scale ◽  
Supervised Machine Learning ◽  
Selection Function ◽  
Learning Approaches ◽  
Inverse Regression ◽  
Imaging Data ◽  
Machine Learning Applications ◽  
Key Aspects

Cosmic shear, that is weak gravitational lensing by the large-scale matter structure of the Universe, is a primary cosmological probe for several present and upcoming surveys investigating dark matter and dark energy, such as Euclid or WFIRST. The probe requires an extremely accurate measurement of the shapes of millions of galaxies based on imaging data. Crucially, the shear measurement must address and compensate for a range of interwoven nuisance effects related to the instrument optics and detector, noise in the images, unknown galaxy morphologies, colors, blending of sources, and selection effects. This paper explores the use of supervised machine learning as a tool to solve this inverse problem. We present a simple architecture that learns to regress shear point estimates and weights via shallow artificial neural networks. The networks are trained on simulations of the forward observing process, and take combinations of moments of the galaxy images as inputs. A challenging peculiarity of the shear measurement task, in terms of machine learning applications, is the combination of the noisiness of the input features and the requirements on the statistical accuracy of the inverse regression. To address this issue, the proposed training algorithm minimizes bias over multiple realizations of individual source galaxies, reducing the sensitivity to properties of the overall sample of source galaxies. Importantly, an observational selection function of these source galaxies can be straightforwardly taken into account via the weights. We first introduce key aspects of our approach using toy-model simulations, and then demonstrate its potential on images mimicking Euclid data. Finally, we analyze images from the GREAT3 challenge, obtaining competitively low multiplicative and additive shear biases despite the use of a simple training set. We conclude that the further development of suited machine learning approaches is of high interest to meet the stringent requirements on the shear measurement in current and future surveys. We make a demonstration implementation of our technique publicly available.

What Are the Most Important Predictors of Subjective Well-Being? Insights From Machine Learning and Linear Regression Approaches on the MIDUS Datasets

2021 ◽  
Author(s):  
Seth Margolis ◽  
Jacob Elder ◽  
Brent Hughes ◽  
Sonja Lyubomirsky
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Regression Models ◽  
Stress Reactivity ◽  
A Priori ◽  
Life Quality ◽  
Well Being ◽  
The United States ◽  
Learning Approaches ◽  
Subjective Well Being

What are the most important predictors of subjective well-being? Using a nationally representative publicly available dataset from the Midlife in the United States project (N = 4,378), we applied linear regression, which often relies on assumptions of linearity and a priori interactions, and advanced machine learning approaches, which maximize prediction by thoroughly exploring nonlinear effects and higher-order interactions, to determine the ordering and characteristics of predictors of well-being. Advanced machine learning models generally did not predict well-being more accurately than did regression models, suggesting that many predictors of well-being may be linear and non-interactive. Consistent with this implication, the introduction of product and squared terms in regression models improved prediction, but only nominally. Our findings replicated previous research, with sociability, physical health, disengagement from goals, sex life quality, wealth, and religious activity emerging as the strongest predictors of well-being, and demographic factors emerging as relatively weak predictors. Furthermore, self-reported “aches” (the strongest “objective” predictor of well-being), stress reactivity, and disengagement negatively predicted well-being, reinforcing the role of stress in psychological maladjustment. Finally, unlike prior research, control over one’s life—and control over financial and work matters in particular—strongly predicted well-being.

Multiple vehicles detection and tracking for intelligent transport systems using machine learning approaches

2019 ◽  
Vol 70 (3) ◽  
pp. 214-224
Author(s):  
Bui Ngoc Dung ◽  
Manh Dzung Lai ◽  
Tran Vu Hieu ◽  
Nguyen Binh T. H.
Keyword(s):  
Machine Learning ◽  
Gaussian Mixture ◽  
Research Field ◽  
Transport Systems ◽  
Learning Approaches ◽  
Subtraction Method ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Detection And Tracking ◽  
Multiple Vehicles

Video surveillance is emerging research field of intelligent transport systems. This paper presents some techniques which use machine learning and computer vision in vehicles detection and tracking. Firstly the machine learning approaches using Haar-like features and Ada-Boost algorithm for vehicle detection are presented. Secondly approaches to detect vehicles using the background subtraction method based on Gaussian Mixture Model and to track vehicles using optical flow and multiple Kalman filters were given. The method takes advantages of distinguish and tracking multiple vehicles individually. The experimental results demonstrate high accurately of the method.

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

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