Special Issue "Biocomputing and Synthetic Biology in Cells"

A special issue of Cells (ISSN 2073-4409).

Deadline for manuscript submissions: 28 September 2020.

Special Issue Editor

Guest Editor
Prof. Dr. Quan Zou

Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
E-Mail
Interests: bioinformatics; molecular computing; sequence alignment; systems biology

Special Issue Information

Dear Colleagues,

Biocomputing and synthetic biology have been two of the most exciting emerging fields in recent years. Biocomputing focuses on developing novel computational models beyond the Turing machine, such as DNA computing and membrane computing. It aims at to create a super machine in cells without any silicon. Synthetic biology, a more detailed extension of biocomputing, involves the design of circuits, simulations, and cell analysis. It is interdisciplinary, involving the chemical industry, biotechnology, computer science and mathematics.

For this Special Issue, we invite the submission of papers on new emerging topics or computational techniques in biocomputing and synthetic biology, particularly those involving interdisciplinary research.

Prof. Quan Zou
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 papers will be 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 monthly 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 1800 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

  • biocomputing
  • DNA computing
  • membrane computing
  • bioinformatics
  • neural computing
  • computational systems biology
  • synthetic biology
  • bio-inspired computing
  • DNA storage

Published Papers (2 papers)

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Research

Open AccessArticle
Ensemble of Deep Recurrent Neural Networks for Identifying Enhancers via Dinucleotide Physicochemical Properties
Received: 27 May 2019 / Revised: 19 July 2019 / Accepted: 21 July 2019 / Published: 23 July 2019
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Abstract
Enhancers are short deoxyribonucleic acid fragments that assume an important part in the genetic process of gene expression. Due to their possibly distant location relative to the gene that is acted upon, the identification of enhancers is difficult. There are many published works [...] Read more.
Enhancers are short deoxyribonucleic acid fragments that assume an important part in the genetic process of gene expression. Due to their possibly distant location relative to the gene that is acted upon, the identification of enhancers is difficult. There are many published works focused on identifying enhancers based on their sequence information, however, the resulting performance still requires improvements. Using deep learning methods, this study proposes a model ensemble of classifiers for predicting enhancers based on deep recurrent neural networks. The input features of deep ensemble networks were generated from six types of dinucleotide physicochemical properties, which had outperformed the other features. In summary, our model which used this ensemble approach could identify enhancers with achieved sensitivity of 75.5%, specificity of 76%, accuracy of 75.5%, and MCC of 0.51. For classifying enhancers into strong or weak sequences, our model reached sensitivity of 83.15%, specificity of 45.61%, accuracy of 68.49%, and MCC of 0.312. Compared to the benchmark result, our results had higher performance in term of most measurement metrics. The results showed that deep model ensembles hold the potential for improving on the best results achieved to date using shallow machine learning methods. Full article
(This article belongs to the Special Issue Biocomputing and Synthetic Biology in Cells)
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Open AccessArticle
Convolutional Neural Network and Bidirectional Long Short-Term Memory-Based Method for Predicting Drug–Disease Associations
Received: 9 June 2019 / Revised: 8 July 2019 / Accepted: 9 July 2019 / Published: 11 July 2019
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Abstract
Identifying novel indications for approved drugs can accelerate drug development and reduce research costs. Most previous studies used shallow models for prioritizing the potential drug-related diseases and failed to deeply integrate the paths between drugs and diseases which may contain additional association information. [...] Read more.
Identifying novel indications for approved drugs can accelerate drug development and reduce research costs. Most previous studies used shallow models for prioritizing the potential drug-related diseases and failed to deeply integrate the paths between drugs and diseases which may contain additional association information. A deep-learning-based method for predicting drug–disease associations by integrating useful information is needed. We proposed a novel method based on a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM)—CBPred—for predicting drug-related diseases. Our method deeply integrates similarities and associations between drugs and diseases, and paths among drug-disease pairs. The CNN-based framework focuses on learning the original representation of a drug-disease pair from their similarities and associations. As the drug-disease association possibility also depends on the multiple paths between them, the BiLSTM-based framework mainly learns the path representation of the drug-disease pair. In addition, considering that different paths have discriminate contributions to the association prediction, an attention mechanism at path level is constructed. Our method, CBPred, showed better performance and retrieved more real associations in the front of the results, which is more important for biologists. Case studies further confirmed that CBPred can discover potential drug-disease associations. Full article
(This article belongs to the Special Issue Biocomputing and Synthetic Biology in Cells)
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