scholarly journals Transmission analysis of a large TB outbreak in London: mathematical modelling study using genomic data

2019 ◽  
Author(s):  
Yuanwei Xu ◽  
Hollie Topliffe ◽  
James Stimson ◽  
Helen R. Stagg ◽  
Ibrahim Abubakar ◽  
...  
Keyword(s):  
Mathematical Modelling ◽  
Disease Transmission ◽  
Machine Learning Techniques ◽  
Reconstruction Method ◽  
Nucleotide Polymorphism ◽  
Single Nucleotide ◽  
Patient Level ◽  
Learning Techniques ◽  
Level Data ◽  
History Of

AbstractOutbreaks of tuberculosis- such as the large isoniazid-resistant outbreak centered on London, United Kingdom, which originated in 1995- provide excellent opportunities to model transmission of this devastating disease. Transmission chains for tuberculosis are notoriously difficult to ascertain, but mathematical modelling approaches, combined with whole-genome sequencing (WGS) data, have strong potential to contribute to transmission analyses. Using such data, we aimed to reconstruct transmission histories for the outbreak using a Bayesian approach, and to use machine learning techniques with patient-level data to identify the key covariates associated with transmission. By using our transmission reconstruction method that accounts for phylogenetic uncertainty, we are able to identify 24 transmission events with reasonable confidence, 11 of which have zero single nucleotide polymorphism (SNP) distance, and as maximum distance of 3. Patient age, alcohol abuse and history of homelessness were found to be the most important predictors of being credible tuberculosis transmitters.

scholarly journals Transmission analysis of a large tuberculosis outbreak in London: a mathematical modelling study using genomic data

2020 ◽  
Vol 6 (11) ◽  
Author(s):  
Yuanwei Xu ◽  
Jessica E. Stockdale ◽  
Vijay Naidu ◽  
Hollie Hatherell ◽  
James Stimson ◽  
...  
Keyword(s):  
Mathematical Modelling ◽  
Disease Transmission ◽  
Machine Learning Techniques ◽  
Whole Genome Sequencing Data ◽  
Reconstruction Method ◽  
Sequencing Data ◽  
Patient Level ◽  
Learning Techniques ◽  
Level Data ◽  
History Of

Outbreaks of tuberculosis (TB) – such as the large isoniazid-resistant outbreak centred on London, UK, which originated in 1995 – provide excellent opportunities to model transmission of this devastating disease. Transmission chains for TB are notoriously difficult to ascertain, but mathematical modelling approaches, combined with whole-genome sequencing data, have strong potential to contribute to transmission analyses. Using such data, we aimed to reconstruct transmission histories for the outbreak using a Bayesian approach, and to use machine-learning techniques with patient-level data to identify the key covariates associated with transmission. By using our transmission reconstruction method that accounts for phylogenetic uncertainty, we are able to identify 21 transmission events with reasonable confidence, 9 of which have zero SNP distance, and a maximum distance of 3. Patient age, alcohol abuse and history of homelessness were found to be the most important predictors of being credible TB transmitters.

scholarly journals Machine Learning Techniques for Single Nucleotide Polymorphism—Disease Classification Models in Schizophrenia

Molecules ◽  
2010 ◽  
Vol 15 (7) ◽  
pp. 4875-4889 ◽  
Author(s):  
Vanessa Aguiar-Pulido ◽  
José A. Seoane ◽  
Juan R. Rabuñal ◽  
Julián Dorado ◽  
Alejandro Pazos ◽  
...  
Keyword(s):  
Machine Learning ◽  
Single Nucleotide Polymorphism ◽  
Disease Classification ◽  
Machine Learning Techniques ◽  
Classification Models ◽  
Nucleotide Polymorphism ◽  
Single Nucleotide ◽  
Learning Techniques

scholarly journals Temporal fluctuations of Y-chromosomal variation in Bos taurus

2008 ◽  
Vol 4 (6) ◽  
pp. 752-754 ◽  
Author(s):  
Emma Svensson ◽  
Anders Götherström
Keyword(s):  
Single Nucleotide Polymorphism ◽  
Bos Taurus ◽  
Demographic History ◽  
Northern Europe ◽  
Chromosomal Gene ◽  
Chromosomal Variation ◽  
Nucleotide Polymorphism ◽  
Single Nucleotide ◽  
Domestic Species ◽  
History Of

Phylogeography has recently become more abundant in studies of demographic history of both wild and domestic species. A single nucleotide polymorphism (SNP) in the intron of the Y-chromosomal gene UTY19 displays a north–south gradient in modern cattle. Support for this geographical distribution of haplogroups has previously also been seen in ancient cattle from Germany. However, when analysing 38 historic remains of domestic bulls and three aurochs from northern Europe for this SNP we found no such association. Instead, we noted extensive amounts of temporal variation that can be attributed to transportation of cattle and late breed formation.

scholarly journals Addressing Complexities of Machine Learning in Big Data: Principles, Trends and Challenges from Systematical Perspectives

2017 ◽  
Author(s):  
Qi Wang ◽  
Xia Zhao ◽  
Jincai Huang ◽  
Yanghe Feng ◽  
Zhong Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Concept Drift ◽  
Model Development ◽  
Machine Learning Techniques ◽  
Comprehensive Overview ◽  
Robust Learning ◽  
Human Interactions ◽  
Learning Techniques ◽  
History Of

The concept of ‘big data’ has been widely discussed, and its value has been illuminated throughout a variety of domains. To quickly mine potential values and alleviate the ever-increasing volume of information, machine learning is playing an increasingly important role and faces more challenges than ever. Because few studies exist regarding how to modify machine learning techniques to accommodate big data environments, we provide a comprehensive overview of the history of the evolution of big data, the foundations of machine learning, and the bottlenecks and trends of machine learning in the big data era. More specifically, based on learning principals, we discuss regularization to enhance generalization. The challenges of quality in big data are reduced to the curse of dimensionality, class imbalances, concept drift and label noise, and the underlying reasons and mainstream methodologies to address these challenges are introduced. Learning model development has been driven by domain specifics, dataset complexities, and the presence or absence of human involvement. In this paper, we propose a robust learning paradigm by aggregating the aforementioned factors. Over the next few decades, we believe that these perspectives will lead to novel ideas and encourage more studies aimed at incorporating knowledge and establishing data-driven learning systems that involve both data quality considerations and human interactions.

scholarly journals Sentiment analysis and predictors of optimism: The AUA Presidential addresses, 1902-2019

2021 ◽  
Author(s):  
Akhil Saji
Keyword(s):  
Language Processing ◽  
Independent Predictor ◽  
Presidential Address ◽  
Machine Learning Techniques ◽  
Significant Independent Predictor ◽  
Searchable Database ◽  
Learning Techniques ◽  
History Of ◽  
Factual Content ◽  
American Urological Association

Objectives The annual addresses of the President of the American Urological Association (AUA) may articulate and reflect the contemporary goals, values, and concerns of contemporary AUA membership. There is no organized archive of such addresses. We aimed to create a searchable database of all AUA Presidents and their addresses to determine variables associated with speech sentiment including positivity, negativity, and emotional tone through the 117 years of the AUA’s history. Methods We queried AUA archives, journals, recorded tape, and personal records, to create a database of all existing AUA Presidential addresses and biographic data. We applied natural language processing and machine learning techniques to evaluate the addresses for overall sentiment with validation using analog analyses (i.e reading and annotation). Multivariable logistic regression was performed to identify significant predictors of Presidential address sentiment. Results Between 1902-2019, a total of 113 AUA meetings were held. A total of 85 of 113 (75.22%) presidential addresses were transcribed and archived in the database representing 254,124 words by male presidents with a median (IQR) age of 61.43 (53.1-66.5) years. AUA Presidents during the second half of the history of the AUA (1960-2019) were significantly older at time of inauguration and gave more positive speeches in the active voice than presidents during the first half (1902-1959) (p < .05). The only significant independent predictor of the degree of positivity in an AUA President’s annual address was speaker age (95% CI 1.007-1.119). Conclusions We created the first digital, searchable database of all AUA Presidential speeches from 1902-2019 and aim to add additional addresses prospectively. Artificial intelligence analyses mirrored the findings of human reading and demonstrated that from 1902-2019 AUA Presidential addresses became more positive and optimistic with increasing speaker age but without consistent predictors of a speech’s emotional or factual content.

Application of Machine Learning Techniques for Enuresis Prediction in Children

2020 ◽  
Author(s):  
Baran Tokar ◽  
Mukaddes Baskaya ◽  
Ozer Celik ◽  
Fatih Cemrek ◽  
Ayfer Acikgoz
Keyword(s):  
Machine Learning ◽  
Evaluation Process ◽  
Toilet Training ◽  
Machine Learning Techniques ◽  
School Students ◽  
Importance Value ◽  
Independent Variables ◽  
Learning Techniques ◽  
History Of ◽  
Feature Values

Abstract Introduction As a subset of artificial intelligence, machine learning techniques (MLTs) may evaluate very large and raw datasets. In this study, the aim is to establish a model by MLT for the prediction of enuresis in children. Materials and Methods The study included 8,071 elementary school students. A total of 704 children had enuresis. For analysis of data with MLT, another group including 704 nonenuretic children was structured with stratified sampling. Out of 34 independent variables, 14 with high feature values significantly affecting enuresis were selected. A model of estimation was created by training the data. Results Fourteen independent variables in order of feature importance value were starting age of toilet training, having urinary urgency, holding maneuvers to prevent voiding, frequency of defecation, history of enuresis in mother and father, having child's own room, parent's education level, history of enuresis in siblings, consanguineous marriage, incomplete bladder emptying, frequent voiding, gender, history of urinary tract infection, and surgery in the past. The best MLT algorithm for the prediction of enuresis was determined as logistic regression algorithm. The total accuracy rate of the model in prediction was 81.3%. Conclusion MLT might provide a faster and easier evaluation process for studies on enuresis with a large dataset. The model in this study may suggest that selected variables with high feature values could be preferred with priority in any screening studies for enuresis. MLT may prevent clinical errors due to human cognitive biases and may help the physicians to be proactive in diagnosis and treatment of enuresis.

scholarly journals Reconstruction of long-distance bird migration routes using advanced machine learning techniques on geolocator data

2019 ◽  
Vol 16 (155) ◽  
pp. 20190031 ◽  
Author(s):  
Mattia Pancerasa ◽  
Matteo Sangiorgio ◽  
Roberto Ambrosini ◽  
Nicola Saino ◽  
David W. Winkler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Light Level ◽  
Machine Learning Techniques ◽  
Migration Routes ◽  
Long Distance ◽  
Light Levels ◽  
Environmental Light ◽  
Learning Techniques ◽  
Level Data ◽  
Manual Selection

Geolocators are a well-established technology to reconstruct migration routes of animals that are too small to carry satellite tags (e.g. passerine birds). These devices record environmental light-level data that enable the reconstruction of daily positions from the time of twilight. However, all current methods for analysing geolocator data require manual pre-processing of raw records to eliminate twilight events showing unnatural variation in light levels, a step that is time-consuming and must be accomplished by a trained expert. Here, we propose and implement advanced machine learning techniques to automate this procedure and we apply them to 108 migration tracks of barn swallows ( Hirundo rustica ). We show that routes reconstructed from the automated pre-processing are comparable to those obtained from manual selection accomplished by a human expert. This raises the possibility of fully automating light-level geolocator data analysis and possibly analysing the large amount of data already collected on several species.

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