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.

scholarly journals Machine Learning and Deep Learning Techniques, Features and Obstacles in the Cataract Diagnosis

2020 ◽  
Vol 9 (3) ◽  
pp. 87-93
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Literature Review ◽  
Congenital Cataract ◽  
Low Cost ◽  
Current Situation ◽  
Learning Techniques ◽  
Research Questions ◽  
New Research

Cataract is a degenerative condition that, according to estimations, will rise globally. Even though there are various proposals about its diagnosis, there are remaining problems to be solved. This paper aims to identify the current situation of the recent investigations on cataract diagnosis using a framework to conduct the literature review with the intention of answering the following research questions: RQ1) Which are the existing methods for cataract diagnosis? RQ2) Which are the features considered for the diagnosis of cataracts? RQ3) Which is the existing classification when diagnosing cataracts? RQ4) And Which obstacles arise when diagnosing cataracts? Additionally, a cross-analysis of the results was made. The results showed that new research is required in: (1) the classification of “congenital cataract” and, (2) portable solutions, which are necessary to make cataract diagnoses easily and at a low cost.

scholarly journals Multi Labeled Imbalanced Data Classification Based on Advanced Min-Max Machine Learning

2019 ◽  
Vol 9 (1) ◽  
pp. 1776-1778
Keyword(s):  
Machine Learning ◽  
Uncertain Data ◽  
Imbalanced Data ◽  
Data Classification ◽  
Data Sets ◽  
Imbalanced Data Classification ◽  
Different Types ◽  
Measured System ◽  
Traditional Approaches

Some true applications, for example, content arrangement and sub-cell confinement of protein successions, include multi-mark grouping with imbalanced information. Different types of traditional approaches are introduced to describe the relation of hubristic and undertaking formations, classification of different attributes with imbalanced for different uncertain data sets. Here this addresses the issues by utilizing the min-max particular system. The min-max measured system can break down a multi-mark issue into a progression of little two-class sub-issues, which would then be able to be consolidated by two straightforward standards. Additionally present a few decay procedures to improve the presentation of min-max particular systems. Trial results on sub-cellular restriction demonstrate that our strategy has preferable speculation execution over customary SVMs in settling the multi-name and imbalanced information issues. In addition, it is additionally a lot quicker than customary SVMs

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 TEsorter: lineage-level classification of transposable elements using conserved protein domains

2019 ◽  
Author(s):  
Ren-Gang Zhang ◽  
Zhao-Xuan Wang ◽  
Shujun Ou ◽  
Guang-Yuan Li
Keyword(s):  
Transposable Elements ◽  
Phylogenetic Relationships ◽  
Protein Domains ◽  
Ltr Retrotransposons ◽  
Link Type ◽  
Eukaryotic Genomes

AbstractSummaryTransposable elements (TEs) constitute an import part in eukaryotic genomes, but their classification, especially in the lineage or clade level, is still challenging. For this purpose, we propose TEsorter, which is based on conserved protein domains of TEs. It is easy-to-use, fast with multiprocessing, sensitive and precise to classify TEs especially LTR retrotransposons (LTR-RTs). Its results can also directly reflect phylogenetic relationships and diversities of the classified LTR-RTs.AvailabilityThe code in Python is freely available at https://github.com/zhangrengang/TEsorter.

Computational approaches for identification and classification of transposable elements in eukaryotic genomes

2012 ◽  
Vol 34 (8) ◽  
pp. 1009-1019
Author(s):  
Hong-En XU ◽  
Hua-Hao ZHANG ◽  
Min-Jin HAN ◽  
Yi-Hong SHEN ◽  
Xian-Zhi HUANG ◽  
...  
Keyword(s):  
Transposable Elements ◽  
Computational Approaches ◽  
Eukaryotic Genomes

scholarly journals TERL: classification of transposable elements by convolutional neural networks

2020 ◽  
Author(s):  
Murilo Horacio Pereira da Cruz ◽  
Douglas Silva Domingues ◽  
Priscila Tiemi Maeda Saito ◽  
Alexandre Rossi Paschoal ◽  
Pedro Henrique Bugatti
Keyword(s):  
Neural Networks ◽  
Transposable Elements ◽  
Convolutional Neural Networks ◽  
Dimensional Space ◽  
Hierarchical Level ◽  
Deep Convolutional Neural Networks ◽  
One Dimensional ◽  
Space Data ◽  
Eukaryotic Genomes

Abstract Transposable elements (TEs) are the most represented sequences occurring in eukaryotic genomes. Few methods provide the classification of these sequences into deeper levels, such as superfamily level, which could provide useful and detailed information about these sequences. Most methods that classify TE sequences use handcrafted features such as k-mers and homology-based search, which could be inefficient for classifying non-homologous sequences. Here we propose an approach, called transposable elements pepresentation learner (TERL), that preprocesses and transforms one-dimensional sequences into two-dimensional space data (i.e., image-like data of the sequences) and apply it to deep convolutional neural networks. This classification method tries to learn the best representation of the input data to classify it correctly. We have conducted six experiments to test the performance of TERL against other methods. Our approach obtained macro mean accuracies and F1-score of 96.4% and 85.8% for superfamilies and 95.7% and 91.5% for the order sequences from RepBase, respectively. We have also obtained macro mean accuracies and F1-score of 95.0% and 70.6% for sequences from seven databases into superfamily level and 89.3% and 73.9% for the order level, respectively. We surpassed accuracy, recall and specificity obtained by other methods on the experiment with the classification of order level sequences from seven databases and surpassed by far the time elapsed of any other method for all experiments. Therefore, TERL can learn how to predict any hierarchical level of the TEs classification system and is about 20 times and three orders of magnitude faster than TEclass and PASTEC, respectively https://github.com/muriloHoracio/TERL. Contact:[email protected]

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