scholarly journals A novel phylogenetic analysis and machine learning predict pathogenicity of human mtDNA variants

2020 ◽  
Author(s):  
Bala Anı Akpınar ◽  
Paul O. Carlson ◽  
Ville O. Paavilainen ◽  
Cory D. Dunn
Keyword(s):  
Machine Learning ◽  
Genetic Interactions ◽  
Substantial Fraction ◽  
Differential Distribution ◽  
Site Specific ◽  
New Approach ◽  
Mtdna Variation ◽  
Formidable Challenge ◽  
Mtdna Variants ◽  
Specific Conservation

ABSTRACTLinking mitochondrial DNA (mtDNA) variation to clinical outcomes remains a formidable challenge. Diagnosis of mitochondrial disease is hampered by the multicopy nature and potential heteroplasmy of the mitochondrial genome, differential distribution of mutant mtDNAs among various tissues, genetic interactions among alleles, and environmental effects. Here, we describe a new approach to the assessment of which mtDNA variants may be pathogenic. Our method takes advantage of site-specific conservation and variant acceptability metrics that minimize previous classification limitations. Using our novel features, we deploy machine learning to predict the pathogenicity of thousands of human mtDNA variants. Our work demonstrates that a substantial fraction of mtDNA changes not yet characterized as harmful are, in fact, likely to be deleterious. Our findings will be of direct relevance to those at risk of mitochondria-associated metabolic disease.

A New Approach to Water Quality Objectives: Development of a Tissue-Based Site-Specific Objective for a Bioaccumulative Pollutant

2009 ◽  
Vol 2009 (6) ◽  
pp. 984-1006
Author(s):  
Karen Cowan ◽  
Earl Byron ◽  
Samuel Luoma ◽  
Theresa Presser ◽  
Gary Santolo ◽  
...  
Keyword(s):  
Water Quality ◽  
Site Specific ◽  
New Approach ◽  
Specific Objective ◽  
Water Quality Objectives

Research of actual information on well casing using machine learning and neural networks

2021 ◽  
pp. 108-119
Author(s):  
D. V. Shalyapin ◽  
D. L. Bakirov ◽  
M. M. Fattahov ◽  
A. D. Shalyapina ◽  
V. G. Kuznetsov
Keyword(s):  
Machine Learning ◽  
Probabilistic Models ◽  
Western Siberia ◽  
Network Modeling ◽  
Horizontal Wells ◽  
Categorical Variables ◽  
Neural Network Modeling ◽  
New Approach ◽  
Attachment Process

In domestic and world practice, despite the measures applied and developed to improve the quality of well casing, there is a problem of leaky structures in almost 50 % of completed wells. The study of actual data using classical methods of statistical analysis (regression and variance analyses) doesn't allow us to model the process with sufficient accuracy that requires the development of a new approach to the study of the attachment process. It is proposed to use the methods of machine learning and neural network modeling to identify the most important parameters and their synergistic impact on the target variables that affect the quality of well casing. The formulas necessary for translating the numerical values of the results of acoustic and gamma-gamma cementometry into categorical variables to improve the quality of probabilistic models are determined. A database consisting of 93 parameters for 934 wells of fields located in Western Siberia has been formed. The analysis of fastening of production columns of horizontal wells of four stratigraphic arches is carried out, the most weighty variables and regularities of their influence on target indicators are established. Recommendations are formulated to improve the quality of well casing by correcting the effects of acoustic and gamma-gamma logging on the results.

scholarly journals Process Intensification 4.0: A New Approach for Attaining New, Sustainable and Circular Processes Enabled by Machine Learning

2021 ◽  
pp. 108671
Author(s):  
Enrique A. López-Guajardo ◽  
Fernando Delgado-Licona ◽  
Alejandro J. Álvarez ◽  
Krishna D.P. Nigam ◽  
Alejandro Montesinos-Castellanos ◽  
...  
Keyword(s):  
Machine Learning ◽  
Process Intensification ◽  
New Approach

scholarly journals Introducing the Viewpoint in the Resource Description using Machine Learning

2021 ◽  
Author(s):  
Ouahiba Djama
Keyword(s):  
Machine Learning ◽  
Experimental Study ◽  
Search Engines ◽  
Machine Learning Technique ◽  
New Approach ◽  
Learning Technique ◽  
Resource Description

Search engines allow providing the user with data and information according to their interests and specialty. Thus, it is necessary to exploit descriptions of the resources, which take into consideration viewpoints. Generally, the resource descriptions are available in RDF (e.g., DBPedia of Wikipedia content). However, these descriptions do not take into consideration viewpoints. In this paper, we propose a new approach, which allows converting a classic RDF resource description to a resource description that takes into consideration viewpoints. To detect viewpoints in the document, a machine learning technique will be exploited on an instanced ontology. This latter allows representing the viewpoint in a given domain. An experimental study shows that the conversion of the classic RDF resource description to a resource description that takes into consideration viewpoints, allows giving very relevant responses to the user’s requests.

scholarly journals Are we there yet? A machine learning architecture to predict organotropic metastases

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Michael Skaro ◽  
Marcus Hill ◽  
Yi Zhou ◽  
Shannon Quinn ◽  
Melissa B. Davis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cancer Metastasis ◽  
Driving Forces ◽  
The Cancer Genome Atlas ◽  
Tumor Type ◽  
Metastatic Progression ◽  
Statistical Machine Learning ◽  
Primary Tumors ◽  
Site Specific ◽  
Tissue Specific

Abstract Background & Aims Cancer metastasis into distant organs is an evolutionarily selective process. A better understanding of the driving forces endowing proliferative plasticity of tumor seeds in distant soils is required to develop and adapt better treatment systems for this lethal stage of the disease. To this end, we aimed to utilize transcript expression profiling features to predict the site-specific metastases of primary tumors and second, to identify the determinants of tissue specific progression. Methods We used statistical machine learning for transcript feature selection to optimize classification and built tree-based classifiers to predict tissue specific sites of metastatic progression. Results We developed a novel machine learning architecture that analyzes 33 types of RNA transcriptome profiles from The Cancer Genome Atlas (TCGA) database. Our classifier identifies the tumor type, derives synthetic instances of primary tumors metastasizing to distant organs and classifies the site-specific metastases in 16 types of cancers metastasizing to 12 locations. Conclusions We have demonstrated that site specific metastatic progression is predictable using transcriptomic profiling data from primary tumors and that the overrepresented biological processes in tumors metastasizing to congruent distant loci are highly overlapping. These results indicate site-specific progression was organotropic and core features of biological signaling pathways are identifiable that may describe proliferative plasticity in distant soils.

scholarly journals Environmental, pharmacological and genetic influences on the spread of drug-resistant malaria

2010 ◽  
Vol 278 (1712) ◽  
pp. 1705-1712 ◽  
Author(s):  
Tiago Antao ◽  
Ian M. Hastings
Keyword(s):  
Plasmodium Falciparum ◽  
Linkage Disequilibrium ◽  
Malaria Transmission ◽  
Clinical Outcomes ◽  
Artificial Selection ◽  
Genetic Interactions ◽  
Genetic Influences ◽  
Drug Resistant ◽  
New Approach ◽  
Rate Of Spread

Plasmodium falciparum malaria is subject to artificial selection from antimalarial drugs that select for drug-resistant parasites. We describe and apply a flexible new approach to investigate how epistasis, inbreeding, selection heterogeneity and multiple simultaneous drug deployments interact to influence the spread of drug-resistant malaria. This framework recognizes that different human ‘environments’ within which treatment may occur (such as semi- and non-immune humans taking full or partial drug courses) influence the genetic interactions between parasite loci involved in resistance. Our model provides an explanation for how the rate of spread varies according to different malaria transmission intensities, why resistance might stabilize at intermediate frequencies and also identifies several factors that influence the decline of resistance after a drug is removed. Results suggest that studies based on clinical outcomes might overestimate the spread of resistant parasites, especially in high-transmission areas. We show that when transmission decreases, prevalence might decrease without a corresponding change in frequency of resistance and that this relationship is heavily influenced by the extent of linkage disequilibrium between loci. This has important consequences on the interpretation of data from areas where control is being successful and suggests that reducing transmission might have less impact on the spread of resistance than previously expected.

scholarly journals Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment

2018 ◽  
Vol 5 (5) ◽  
pp. 939-945 ◽  
Author(s):  
Grace X. Gu ◽  
Chun-Teh Chen ◽  
Deon J. Richmond ◽  
Markus J. Buehler
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Additive Manufacturing ◽  
Convolutional Neural Networks ◽  
New Approach ◽  
Hierarchical Materials

A new approach to design hierarchical materials using convolutional neural networks is proposed and validated through additive manufacturing and testing.

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