Prediction of Recombination Spots Using Novel Hybrid Feature Extraction Method via Deep Learning Approach

Khan, Fatima; Khan, Muhammad Shahid; Iqbal, Nadeem; Khan, Salman; Khan, Dost Muhammad; Khan, Abbas; Wei, Dong‐Qing

doi:10.3389/fgene.2020.539227

Cited by 22 publications

(19 citation statements)

References 107 publications

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“…It should be highlighted that the hotspots and coldspots used in this study are not defined as in previous models in ORF-based way ( Zhou et al, 2006 ; Jiang et al, 2007 ; Liu et al, 2012 , 2017 ; Chen et al, 2013 ; Li et al, 2014 ; Qiu and Xiao, 2014 ; Jani et al, 2018 ; Zhang and Kong, 2019 ; Khan et al, 2020 ), but are based on the high-resolution Spo11-oligo seq data. In this way we train our models on “true” hotspots, rather than on hot/cold ORFs that are not necessarily equivalent to “true” hotspots.…”

Section: Methodsmentioning

confidence: 99%

“…Computational identification of recombination hotspots may help people get quick information about recombination and relieve the time-consuming experimental determination of hotspots with high cost. As reviewed in Yang et al, 2020 , there are some existing models for hotspot identification at present ( Zhou et al, 2006 ; Jiang et al, 2007 ; Liu et al, 2012 , 2017 ; Chen et al, 2013 ; Li et al, 2014 ; Qiu and Xiao, 2014 ; Jani et al, 2018 ; Zhang and Kong, 2019 ; Khan et al, 2020 ). Almost all of the models were DNA sequence dependent and epigenetic marks that have been increasingly freely available were not considered.…”

Section: Introductionmentioning

confidence: 99%

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Epigenetic Marks and Variation of Sequence-Based Information Along Genomic Regions Are Predictive of Recombination Hot/Cold Spots in Saccharomyces cerevisiae

et al. 2021

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Characterization and identification of recombination hotspots provide important insights into the mechanism of recombination and genome evolution. In contrast with existing sequence-based models for predicting recombination hotspots which were defined in a ORF-based manner, here, we first defined recombination hot/cold spots based on public high-resolution Spo11-oligo-seq data, then characterized them in terms of DNA sequence and epigenetic marks, and finally presented classifiers to identify hotspots. We found that, in addition to some previously discovered DNA-based features like GC-skew, recombination hotspots in yeast can also be characterized by some remarkable features associated with DNA physical properties and shape. More importantly, by using DNA-based features and several epigenetic marks, we built several classifiers to discriminate hotspots from coldspots, and found that SVM classifier performs the best with an accuracy of ∼92%, which is also the highest among the models in comparison. Feature importance analysis combined with prediction results show that epigenetic marks and variation of sequence-based features along the hotspots contribute dominantly to hotspot identification. By using incremental feature selection method, an optimal feature subset that consists of much less features was obtained without sacrificing prediction accuracy.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Epigenetic Marks and Variation of Sequence-Based Information Along Genomic Regions Are Predictive of Recombination Hot/Cold Spots in Saccharomyces cerevisiae

et al. 2021

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“…e key DL architectures for image classification are the convolutional neural networks (CNNs) [30][31][32][33][34][35]. We note that the use of CNN for recognition of fruit has increased dramatically over the last three years (2018 to 2021) and has generated excellent results through either new models or pretrained transfer-learning networks.…”

Section: Overview Of the Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Recent Advancements in Fruit Detection and Classification Using Deep Learning Techniques

Ukwuoma

Qin

Heyat

et al. 2022

Mathematical Problems in Engineering

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Recent advances in computer vision have allowed broad applications in every area of life, and agriculture is not left out. For the agri-food industry, the use of advanced technology is essential. Owing to deep learning’s capability to learn robust features from images, it has witnessed enormous application in several fields. Fruit detection and classification remains challenging due to the form, color, and texture of different fruit species. While studying the impact of computer vision on fruit detection and classification, we pointed out that till 2018 many conventional machine learning methods were utilized while a few methods exploited the application of deep learning methods for fruit detection and classification. This has prompted us to pursue an extensive study on surveying and implementing deep learning models for fruit detection and classification. In this article, we intensively discussed the datasets used by many scholars, the practical descriptors, the model’s implementation, and the challenges of using deep learning to detect and categorize fruits. Lastly, we summarized the results of different deep learning methods applied in previous studies for the purpose of fruit detection and classification. This review covers the study of recently published articles that utilized deep learning models for fruit identification and classification. Additionally, we also implemented from scratch a deep learning model for fruit classification using the popular dataset “Fruit 360” to make it easier for beginner researchers in the field of agriculture to understand the role of deep learning in the agriculture domain.

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“…Our study also investigated temporal trends in hotspot shifts using the top 19 keywords having the strongest citation bursts. These included prediction (2016)(2017)(2018)(2019)(2020)(2021), sequence (2017)(2018)(2019)(2020)(2021), mutation (2017)(2018)(2019)(2020)(2021), and cancer (2019)(2020)(2021) (Figure 8).…”

Section: Figurementioning

confidence: 99%

“…Huang et al identified a deep learning framework, which was evolutionbased, for unified variant and gene prioritization. The authors integrated constraints predicting missense variants and proteincoding genes associated with dominant disorders and estimated fitness effects for potential single-nucleotide variants, which outperformed current methods Huang, (2020). Zhang et al formulated a disease-specific variant classifier that assessed discriminate pathogenic variants from benign variants and prioritized disease-associated variants Zhang et al, (2021b).…”

Section: The Knowledge Base and Current Research Characteristicsmentioning

confidence: 99%

Knowledge structure and emerging trends in the application of deep learning in genetics research: A bibliometric analysis [2000–2021]

Zhang

Fan

2022

Front. Genet.

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Introduction: Deep learning technology has been widely used in genetic research because of its characteristics of computability, statistical analysis, and predictability. Herein, we aimed to summarize standardized knowledge and potentially innovative approaches for deep learning applications of genetics by evaluating publications to encourage more research.Methods: The Science Citation Index Expanded TM (SCIE) database was searched for deep learning applications for genomics-related publications. Original articles and reviews were considered. In this study, we derived a clustered network from 69,806 references that were cited by the 1,754 related manuscripts identified. We used CiteSpace and VOSviewer to identify countries, institutions, journals, co-cited references, keywords, subject evolution, path, current characteristics, and emerging topics.Results: We assessed the rapidly increasing publications concerned about deep learning applications of genomics approaches and identified 1,754 articles that published reports focusing on this subject. Among these, a total of 101 countries and 2,487 institutes contributed publications, The United States of America had the most publications (728/1754) and the highest h-index, and the US has been in close collaborations with China and Germany. The reference clusters of SCI articles were clustered into seven categories: deep learning, logic regression, variant prioritization, random forests, scRNA-seq (single-cell RNA-seq), genomic regulation, and recombination. The keywords representing the research frontiers by year were prediction (2016–2021), sequence (2017–2021), mutation (2017–2021), and cancer (2019–2021).Conclusion: Here, we summarized the current literature related to the status of deep learning for genetics applications and analyzed the current research characteristics and future trajectories in this field. This work aims to provide resources for possible further intensive exploration and encourages more researchers to overcome the research of deep learning applications in genetics.

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Prediction of Recombination Spots Using Novel Hybrid Feature Extraction Method via Deep Learning Approach

Cited by 22 publications

References 107 publications

Epigenetic Marks and Variation of Sequence-Based Information Along Genomic Regions Are Predictive of Recombination Hot/Cold Spots in Saccharomyces cerevisiae

Epigenetic Marks and Variation of Sequence-Based Information Along Genomic Regions Are Predictive of Recombination Hot/Cold Spots in Saccharomyces cerevisiae

Recent Advancements in Fruit Detection and Classification Using Deep Learning Techniques

Knowledge structure and emerging trends in the application of deep learning in genetics research: A bibliometric analysis [2000–2021]

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