ATGC transcriptomics: a web-based application to integrate, explore and analyze de novo transcriptomic data

González, Sergio; Clavijo, Bernardo; Rivarola, Máximo Lisandro; Moreno, Patricio; Fernández, Paula; Dopazo, Joaquín; Paniego, Norma

doi:10.1186/s12859-017-1494-2

Cited by 8 publications

(8 citation statements)

References 30 publications

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“…With the rapid development of neural networks and sensor technologies based on the Internet of Things, we can more intelligently detect athletes' training status and physical conditions. Therefore, this paper proposes a wearable device smart football player health prediction algorithm [ 10 , 11 ] based on the recurrent neural network (RNN). Firstly, wearable sensors are used to obtain the health information data [ 12 – 14 ] of football players and to obtain the big data of the athletes' health [ 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Wearable Device-Based Smart Football Athlete Health Prediction Algorithm Based on Recurrent Neural Networks

Feng

Wang

2021

Journal of Healthcare Engineering

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For football players who participate in sports, the word “health” is extremely important. Athletes cannot create their own value in competitive competitions without a strong foundation. Scholars have paid a lot of attention to athlete health this year, and many analysis methods have been proposed, but there have been few studies using neural networks. As a result, this article proposes a novel wearable device-based smart football player health prediction algorithm based on recurrent neural networks. To begin, this article employs wearable sensors to collect health data from football players. The time step data are then fed into a recurrent neural network to extract deep features, followed by the health prediction results. The collected football player health dataset is used in this paper to conduct experiments. The simulation results prove the reliability and superiority of the proposed algorithm. Furthermore, the algorithm presented in this paper can serve as a foundation for the football team’s and coaches’ scientific training plans.

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Section: Introductionmentioning

confidence: 99%

Wearable Device-Based Smart Football Athlete Health Prediction Algorithm Based on Recurrent Neural Networks

Feng

Wang

2021

Journal of Healthcare Engineering

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“…In order to identify putative orthologs of these senescence-associated WRKYs in petunia, the predicted protein sequences of the AtWRKY genes were used to perform BLAST searches using a public transcriptomic leaf database of Petunia x hybrida cv. 'Mitchell Diploid' (Villarino et al 2014) loaded into a web tool developed by Gonzalez et al (2017). Of the 28 AtWRKY proteins, 20 cDNA sequences were obtained in P. hybrida (PhWRKYs) by using tBLASTn (Table 1 and Supplementary Table S3).…”

Section: Resultsmentioning

confidence: 99%

“…Twenty-eight WRKY genes of Arabidopsis were selected from published studies on the leaf transcriptome (Buchanan-Wollaston et al 2005;Wagstaff et al 2009;Breeze et al 2011), public repositories including Leaf Senescence DataBase (Liu et al 2011) and Arabidopsis eFP Browser (Winter et al 2007). A leaf transcriptome dataset of P. hybrida (Villarino et al 2014) was used to create a repository to run BLAST (Petunia Transcriptome Repository ATGC v1.0) (Gonzalez et al 2017). Putative P. hybrida orthologs in this repository and the parental genomes of P. hybrida (P. axillaris and P. in ata) available at SOL Genomics Network (http:// solgenomics.net) (Bombarely et al 2016) were searched by using tBLASTn.…”

Section: Identification Of Wrky Transcription Factors In P Hybridamentioning

confidence: 99%

Identification of WRKY transcription factors associated with leaf and corolla senescence in Petunia hybrida

Astigueta¹,

Baigorria²,

García³

et al. 2021

Preprint

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Several families of transcription factors (TFs) control the progression of senescence. Many key TFs belonging to the WRKY family have been described to play crucial roles in the regulation of leaf senescence, mainly in Arabidopsis. However, little is known about senescence-associated WRKY members in floricultural species. Delay of senescence in leaves and petals of Petunia hybrida, a worldwide ornamental crop are highly appreciated traits. In this work, starting from 28 differentially expressed WRKY genes of Arabidopsis during the progression of leaf senescence, we identified the orthologous in P. hybrida and explored the expression profiles of 20 PhWRKY genes during the progression of natural (age-related) leaf and corolla senescence as well as in the corollas of flowers undergoing pollination-induced senescence. Simultaneous visualization showed consistent and similar expression profiles of PhWRKYs during natural leaf and corolla senescence, although weak expression changes were observed during pollination-induced senescence. Comparable expression trends between PhWRKYs and the corresponding genes of Arabidopsis were observed during leaf senescence, although more divergences were found in petals of pollinated petunia flowers. Integration of expression data with phylogenetics, conserved motif and cis-regulatory element analyses were used to establish a list of solid candidates that could regulate more than one senescence process. Our results suggest that several members of the WRKY family of TFs are tightly linked to the regulation of senescence in P. hybrida.

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“…To compare the different deep models with the proposed methodology, we use various performance measures such as precision, recall, and FScore along with accuracy, as accuracy alone cannot determine the effectiveness of a model [ 98 ]. During the experimentation, 75% of the dataset is for training the model, and 25% is for testing purposes.…”

Section: Methodsmentioning

confidence: 99%

Deep Ensemble Model for Classification of Novel Coronavirus in Chest X‐Ray Images

Ahmad

Farooq

Ghani

2021

Computational Intelligence and Neuroscience

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The novel coronavirus, SARS-CoV-2, can be deadly to people, causing COVID-19. The ease of its propagation, coupled with its high capacity for illness and death in infected individuals, makes it a hazard to the community. Chest X-rays are one of the most common but most difficult to interpret radiographic examination for early diagnosis of coronavirus-related infections. They carry a considerable amount of anatomical and physiological information, but it is sometimes difficult even for the expert radiologist to derive the related information they contain. Automatic classification using deep learning models can help in better assessing these infections swiftly. Deep CNN models, namely, MobileNet, ResNet50, and InceptionV3, were applied with different variations, including training the model from the start, fine-tuning along with adjusting learned weights of all layers, and fine-tuning with learned weights along with augmentation. Fine-tuning with augmentation produced the best results in pretrained models. Out of these, two best-performing models (MobileNet and InceptionV3) selected for ensemble learning produced accuracy and FScore of 95.18% and 90.34%, and 95.75% and 91.47%, respectively. The proposed hybrid ensemble model generated with the merger of these deep models produced a classification accuracy and FScore of 96.49% and 92.97%. For test dataset, which was separately kept, the model generated accuracy and FScore of 94.19% and 88.64%. Automatic classification using deep ensemble learning can help radiologists in the correct identification of coronavirus-related infections in chest X-rays. Consequently, this swift and computer-aided diagnosis can help in saving precious human lives and minimizing the social and economic impact on society.

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ATGC transcriptomics: a web-based application to integrate, explore and analyze de novo transcriptomic data

Cited by 8 publications

References 30 publications

Wearable Device-Based Smart Football Athlete Health Prediction Algorithm Based on Recurrent Neural Networks

Wearable Device-Based Smart Football Athlete Health Prediction Algorithm Based on Recurrent Neural Networks

Identification of WRKY transcription factors associated with leaf and corolla senescence in Petunia hybrida

Deep Ensemble Model for Classification of Novel Coronavirus in Chest X‐Ray Images

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