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Cells
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Advances of Deep Learning in Cell Biology
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Advances of Deep Learning in Cell Biology

A special issue of Cells (ISSN 2073-4409). This special issue belongs to the section "Cells of the Nervous System".

Deadline for manuscript submissions: closed (15 February 2023) | Viewed by 4606

Special Issue Editor

Dr. Shixiong Zhang

E-Mail Website
Guest Editor
School of Computer Science and Technology, Xidian University, Xi'an 710126, China
Interests: deep learning in cell biology

Special Issue Information

Dear Colleagues,

The data explosion driven by biotechnology advancements, such as high-throughput sequencing, is constantly challenging conventional methods used in exploring cell biology. The boom of deep learning has granted the computational power to resolve complex research questions in the specific field of structure/molecular biology, precision medicine, pharmacy, etc. Deep learning is expected to explore beyond our knowledge to interpret cell biology data. A variety of deep architectures has evolved into deep learning, such as autoencoders, convolutional neural networks, recurrent neural networks, long short-term memory, transfer learning, generative adversarial network, and graph neural networks. Its strong flexibility and high accuracy guarantee them sweeping superiority over other existing methods for bioinformatics analysis in cell biology.

Therefore, we have initiated such a Special Issue to provide a forum for advances in the development and application of deep learning-based tools in cell biology. Original studies, reviews, methods, and reports on tools, etc. are all welcome.

We welcome submissions related to but not limited to the following topics:

  • Deep Learning
  • Machine Learning
  • Cell Biology
  • Bioinformatics
  • Computational Biology
  • High-Throughput Sequencing
  • Data Integration
  • Genomic Medicine
  • Drug Discovery
  • Single-cell Omics Analysis
  • Precision Medicine
  • RNA/Protein Structure Prediction
  • Cancer Genomics
  • Graph Neural Network
  • Transfer Learning
  • Generative Adversarial Network
  • Autoencoder

Dr. Shixiong Zhang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cells is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • deep learning
  • machine learning
  • cell biology
  • bioinformatics
  • computational biology
  • high-throughput sequencing
  • data integration
  • genomic medicine
  • drug discovery

Published Papers (2 papers)

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Research

13 pages, 9124 KiB  
Article
iProm-Sigma54: A CNN Base Prediction Tool for σ54 Promoters
by Muhammad Shujaat, Hoonjoo Kim, Hilal Tayara and Kil To Chong
Cells 2023, 12(6), 829; https://doi.org/10.3390/cells12060829 - 7 Mar 2023
Cited by 2 | Viewed by 1937
Abstract
The sigma (σ) factor of RNA holoenzymes is essential for identifying and binding to promoter regions during gene transcription in prokaryotes. σ54 promoters carried out various ancillary methods and environmentally responsive procedures; therefore, it is crucial to accurately identify [...] Read more.
The sigma (σ) factor of RNA holoenzymes is essential for identifying and binding to promoter regions during gene transcription in prokaryotes. σ54 promoters carried out various ancillary methods and environmentally responsive procedures; therefore, it is crucial to accurately identify σ54 promoter sequences to comprehend the underlying process of gene regulation. Herein, we come up with a convolutional neural network (CNN) based prediction tool named “iProm-Sigma54” for the prediction of σ54 promoters. The CNN consists of two one-dimensional convolutional layers, which are followed by max pooling layers and dropout layers. A one-hot encoding scheme was used to extract the input matrix. To determine the prediction performance of iProm-Sigma54, we employed four assessment metrics and five-fold cross-validation; performance was measured using a benchmark and test dataset. According to the findings of this comparison, iProm-Sigma54 outperformed existing methodologies for identifying σ54 promoters. Additionally, a publicly accessible web server was constructed. Full article
(This article belongs to the Special Issue Advances of Deep Learning in Cell Biology)
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18 pages, 3660 KiB  
Article
SGAEMDA: Predicting miRNA-Disease Associations Based on Stacked Graph Autoencoder
by Shudong Wang, Boyang Lin, Yuanyuan Zhang, Sibo Qiao, Fuyu Wang, Wenhao Wu and Chuanru Ren
Cells 2022, 11(24), 3984; https://doi.org/10.3390/cells11243984 - 9 Dec 2022
Cited by 6 | Viewed by 1511
Abstract
MicroRNA (miRNA)-disease association (MDA) prediction is critical for disease prevention, diagnosis, and treatment. Traditional MDA wet experiments, on the other hand, are inefficient and costly.Therefore, we proposed a multi-layer collaborative unsupervised training base model called SGAEMDA (Stacked Graph Autoencoder-Based Prediction of Potential miRNA-Disease [...] Read more.
MicroRNA (miRNA)-disease association (MDA) prediction is critical for disease prevention, diagnosis, and treatment. Traditional MDA wet experiments, on the other hand, are inefficient and costly.Therefore, we proposed a multi-layer collaborative unsupervised training base model called SGAEMDA (Stacked Graph Autoencoder-Based Prediction of Potential miRNA-Disease Associations). First, from the original miRNA and disease data, we defined two types of initial features: similarity features and association features. Second, stacked graph autoencoder is then used to learn unsupervised low-dimensional representations of meaningful higher-order similarity features, and we concatenate the association features with the learned low-dimensional representations to obtain the final miRNA-disease pair features. Finally, we used a multilayer perceptron (MLP) to predict scores for unknown miRNA-disease associations. SGAEMDA achieved a mean area under the ROC curve of 0.9585 and 0.9516 in 5-fold and 10-fold cross-validation, which is significantly higher than the other baseline methods. Furthermore, case studies have shown that SGAEMDA can accurately predict candidate miRNAs for brain, breast, colon, and kidney neoplasms. Full article
(This article belongs to the Special Issue Advances of Deep Learning in Cell Biology)
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