scholarly journals Peer Review #2 of "A systematic review of the application of machine learning in the detection and classification of transposable elements (v0.1)"

2019 ◽  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Transposable Elements ◽  
Peer Review

scholarly journals Retrotransposons in Plant Genomes: Structure, Identification, and Classification through Bioinformatics and Machine Learning

2019 ◽  
Vol 20 (15) ◽  
pp. 3837 ◽  
Author(s):  
Simon Orozco-Arias ◽  
Gustavo Isaza ◽  
Romain Guyot
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Deep Learning ◽  
Transposable Elements ◽  
Structural Diversity ◽  
Structure Identification ◽  
Gene Expression And Regulation ◽  
Plant Genomes ◽  
Bioinformatics Software

Transposable elements (TEs) are genomic units able to move within the genome of virtually all organisms. Due to their natural repetitive numbers and their high structural diversity, the identification and classification of TEs remain a challenge in sequenced genomes. Although TEs were initially regarded as “junk DNA”, it has been demonstrated that they play key roles in chromosome structures, gene expression, and regulation, as well as adaptation and evolution. A highly reliable annotation of these elements is, therefore, crucial to better understand genome functions and their evolution. To date, much bioinformatics software has been developed to address TE detection and classification processes, but many problematic aspects remain, such as the reliability, precision, and speed of the analyses. Machine learning and deep learning are algorithms that can make automatic predictions and decisions in a wide variety of scientific applications. They have been tested in bioinformatics and, more specifically for TEs, classification with encouraging results. In this review, we will discuss important aspects of TEs, such as their structure, importance in the evolution and architecture of the host, and their current classifications and nomenclatures. We will also address current methods and their limitations in identifying and classifying TEs.

scholarly journals A systematic review of the application of machine learning in the detection and classification of transposable elements

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8311 ◽  
Author(s):  
Simon Orozco-Arias ◽  
Gustavo Isaza ◽  
Romain Guyot ◽  
Reinel Tabares-Soto
Keyword(s):  
Machine Learning ◽  
Transposable Elements ◽  
Nuclear Dna ◽  
Deep Impact ◽  
Different Types ◽  
Review Protocol ◽  
Research Questions ◽  
Hidden Patterns ◽  
Eukaryotic Genomes

Background Transposable elements (TEs) constitute the most common repeated sequences in eukaryotic genomes. Recent studies demonstrated their deep impact on species diversity, adaptation to the environment and diseases. Although there are many conventional bioinformatics algorithms for detecting and classifying TEs, none have achieved reliable results on different types of TEs. Machine learning (ML) techniques can automatically extract hidden patterns and novel information from labeled or non-labeled data and have been applied to solving several scientific problems. Methodology We followed the Systematic Literature Review (SLR) process, applying the six stages of the review protocol from it, but added a previous stage, which aims to detect the need for a review. Then search equations were formulated and executed in several literature databases. Relevant publications were scanned and used to extract evidence to answer research questions. Results Several ML approaches have already been tested on other bioinformatics problems with promising results, yet there are few algorithms and architectures available in literature focused specifically on TEs, despite representing the majority of the nuclear DNA of many organisms. Only 35 articles were found and categorized as relevant in TE or related fields. Conclusions ML is a powerful tool that can be used to address many problems. Although ML techniques have been used widely in other biological tasks, their utilization in TE analyses is still limited. Following the SLR, it was possible to notice that the use of ML for TE analyses (detection and classification) is an open problem, and this new field of research is growing in interest.

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.

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