scholarly journals gammaBOriS: Identification and Taxonomic Classification of Origins of Replication in Gammaproteobacteria using Motif-based Machine Learning

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Theodor Sperlea ◽  
Lea Muth ◽  
Roman Martin ◽  
Christoph Weigel ◽  
Torsten Waldminghaus ◽  
...  
Keyword(s):  
Machine Learning ◽  
Taxonomic Classification ◽  
Origins Of Replication

scholarly journals Optimizing taxonomic classification of marker gene amplicon sequences

2018 ◽  
Author(s):  
Nicholas A Bokulich ◽  
Benjamin D Kaehler ◽  
Jai Ram Rideout ◽  
Matthew Dillon ◽  
Evan Bolyen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sequence Data ◽  
Marker Gene ◽  
Parameter Tuning ◽  
Operating Conditions ◽  
Evaluation Framework ◽  
Taxonomic Classification ◽  
Consensus Methods ◽  
Learning Classifier

Background: Taxonomic classification of marker-gene sequences is an important step in microbiome analysis. Results: We present q2-feature-classifier ( https://github.com/qiime2/q2-feature-classifier ), a QIIME 2 plugin containing several novel machine-learning and alignment-based taxonomy classifiers that meet or exceed the accuracy of existing methods for marker-gene amplicon sequence classification. We evaluated and optimized several commonly used taxonomic classification methods (RDP, BLAST, UCLUST) and several new methods (a scikit-learn naive Bayes machine-learning classifier, and alignment-based taxonomy consensus methods of VSEARCH, BLAST+, and SortMeRNA) for classification of marker-gene amplicon sequence data. Conclusions: Our results illustrate the importance of parameter tuning for optimizing classifier performance, and we make recommendations regarding parameter choices for a range of standard operating conditions. q2-feature-classifier and our evaluation framework, tax-credit, are both free, open-source, BSD-licensed packages available on GitHub.

scholarly journals Optimizing taxonomic classification of marker gene sequences

2017 ◽  
Author(s):  
Nicholas A Bokulich ◽  
Benjamin D Kaehler ◽  
Jai Ram Rideout ◽  
Matthew Dillon ◽  
Evan Bolyen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Marker Gene ◽  
Parameter Tuning ◽  
Operating Conditions ◽  
Evaluation Framework ◽  
Taxonomic Classification ◽  
Gene Sequences ◽  
Learning Classifier ◽  
Classifier Performance

Background. Taxonomic classification of marker-gene sequences is an important step in microbiome analysis. Results. We present q2-feature-classifier (https://github.com/qiime2/q2-feature-classifier), a QIIME 2 plugin containing several novel machine-learning and alignment-based taxonomy classifiers that meet or exceed classification accuracy of existing methods. We evaluated and optimized several commonly used taxonomic classification methods (RDP, BLAST, BLAST+, UCLUST) and several new methods (a scikit-learn naive Bayes machine-learning classifier, and VSEARCH and SortMeRNA alignment-based methods). Conclusions. Our results illustrate the importance of parameter tuning for optimizing classifier performance, and we make explicit recommendations regarding parameter choices for a range of standard operating conditions. q2-feature-classifier and our evaluation framework, tax-credit, are both free, open-source, BSD-licensed packages available on GitHub.

scholarly journals Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study

2020 ◽  
Author(s):  
Gurjit S. Randhawa ◽  
Maximillian P.M. Soltysiak ◽  
Hadi El Roz ◽  
Camila P.E. de Souza ◽  
Kathleen A. Hill ◽  
...  
Keyword(s):  
Machine Learning ◽  
Death Rate ◽  
Genomic Sequence ◽  
Sequence Data ◽  
Rank Correlation ◽  
Taxonomic Classification ◽  
Supervised Machine Learning ◽  
Biological Knowledge ◽  
Alignment Free

AbstractAs of February 20, 2020, the 2019 novel coronavirus (renamed to COVID-19) spread to 30 countries with 2130 deaths and more than 75500 confirmed cases. COVID-19 is being compared to the infamous SARS coronavirus, which resulted, between November 2002 and July 2003, in 8098 confirmed cases worldwide with a 9.6% death rate and 774 deaths. Though COVID-19 has a death rate of 2.8% as of 20 February, the 75752 confirmed cases in a few weeks (December 8, 2019 to February 20, 2020) are alarming, with cases likely being under-reported given the comparatively longer incubation period. Such outbreaks demand elucidation of taxonomic classification and origin of the virus genomic sequence, for strategic planning, containment, and treatment. This paper identifies an intrinsic COVID-19 genomic signature and uses it together with a machine learning-based alignment-free approach for an ultra-fast, scalable, and highly accurate classification of whole COVID-19 genomes. The proposed method combines supervised machine learning with digital signal processing for genome analyses, augmented by a decision tree approach to the machine learning component, and a Spearman’s rank correlation coefficient analysis for result validation. These tools are used to analyze a large dataset of over 5000 unique viral genomic sequences, totalling 61.8 million bp. Our results support a hypothesis of a bat origin and classify COVID-19 as Sarbecovirus, within Betacoronavirus. Our method achieves high levels of classification accuracy and discovers the most relevant relationships among over 5,000 viral genomes within a few minutes, ab initio, using raw DNA sequence data alone, and without any specialized biological knowledge, training, gene or genome annotations. This suggests that, for novel viral and pathogen genome sequences, this alignment-free whole-genome machine-learning approach can provide a reliable real-time option for taxonomic classification.

MACHINE LEARNING-BASED TAXONOMIC CLASSIFICATION OF CARBONATE SKELETAL GRAINS

2020 ◽  
Author(s):  
Jessica Mueller ◽  
◽  
Jonathan L. Payne ◽  
Ardiansyah Koeshidayatullah
Keyword(s):  
Machine Learning ◽  
Taxonomic Classification

scholarly journals Optimizing taxonomic classification of marker gene amplicon sequences

2018 ◽  
Author(s):  
Nicholas A Bokulich ◽  
Benjamin D Kaehler ◽  
Jai Ram Rideout ◽  
Matthew Dillon ◽  
Evan Bolyen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sequence Data ◽  
Marker Gene ◽  
Parameter Tuning ◽  
Operating Conditions ◽  
Evaluation Framework ◽  
Taxonomic Classification ◽  
Consensus Methods ◽  
Learning Classifier

Background: Taxonomic classification of marker-gene sequences is an important step in microbiome analysis. Results: We present q2-feature-classifier ( https://github.com/qiime2/q2-feature-classifier ), a QIIME 2 plugin containing several novel machine-learning and alignment-based taxonomy classifiers that meet or exceed the accuracy of existing methods for marker-gene amplicon sequence classification. We evaluated and optimized several commonly used taxonomic classification methods (RDP, BLAST, UCLUST) and several new methods (a scikit-learn naive Bayes machine-learning classifier, and alignment-based taxonomy consensus methods of VSEARCH, BLAST+, and SortMeRNA) for classification of marker-gene amplicon sequence data. Conclusions: Our results illustrate the importance of parameter tuning for optimizing classifier performance, and we make recommendations regarding parameter choices for a range of standard operating conditions. q2-feature-classifier and our evaluation framework, tax-credit, are both free, open-source, BSD-licensed packages available on GitHub.

scholarly journals A comparison of machine learning techniques for taxonomic classification of teeth from the Family Bovidae

2018 ◽  
Vol 45 (15) ◽  
pp. 2773-2787
Author(s):  
Gregory J. Matthews ◽  
Juliet K. Brophy ◽  
Maxwell Luetkemeier ◽  
Hongie Gu ◽  
George K. Thiruvathukal
Keyword(s):  
Machine Learning ◽  
Taxonomic Classification ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
The Family

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

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