Biocomputing and Synthetic Biology in Cells 2.0

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

Deadline for manuscript submissions: closed (15 November 2021) | Viewed by 4348

Special Issue Editors


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Guest Editor
Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
Interests: bioinformatics; parallel computing; deep learning; protein classification; genome assembly
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
Interests: proteomics; phosphoproteomics; bioinformatics; artificial intelligence biology; deep learning; autophagy; protein kinase
Special Issues, Collections and Topics in MDPI journals

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. Dr. Quan Zou
Prof. Dr. Yu Xue
Guest Editors

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

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

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Published Papers (1 paper)

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Research

14 pages, 13348 KiB  
Article
Machine Learning-Based Prediction of Drug-Drug Interactions for Histamine Antagonist Using Hybrid Chemical Features
by Luong Huu Dang, Nguyen Tan Dung, Ly Xuan Quang, Le Quang Hung, Ngoc Hoang Le, Nhi Thao Ngoc Le, Nguyen Thi Diem, Nguyen Thi Thuy Nga, Shih-Han Hung and Nguyen Quoc Khanh Le
Cells 2021, 10(11), 3092; https://doi.org/10.3390/cells10113092 - 09 Nov 2021
Cited by 14 | Viewed by 3454
Abstract
The requesting of detailed information on new drugs including drug-drug interactions or targets is often unavailable and resource-intensive in assessing adverse drug events. To shorten the common evaluation process of drug-drug interactions, we present a machine learning framework-HAINI to predict DDI types for [...] Read more.
The requesting of detailed information on new drugs including drug-drug interactions or targets is often unavailable and resource-intensive in assessing adverse drug events. To shorten the common evaluation process of drug-drug interactions, we present a machine learning framework-HAINI to predict DDI types for histamine antagonist drugs using simplified molecular-input line-entry systems (SMILES) combined with interaction features based on CYP450 group as inputs. The data used in our research consisted of approved drugs of histamine antagonists that are connected to 26,344 DDI pairs from the DrugBank database. Various classification algorithms such as Naive Bayes, Decision Tree, Random Forest, Logistic Regression, and XGBoost were used with 5-fold cross-validation to approach a large-scale DDIs prediction among histamine antagonist drugs. The prediction performance shows that our model outperformed previously published works on DDI prediction with the best precision of 0.788, a recall of 0.921, and an F1-score of 0.838 among 19 given DDIs types. An important finding of the study is that our prediction is based solely on the SMILES and CYP450 and thus can be applied at the early stage of drug development. Full article
(This article belongs to the Special Issue Biocomputing and Synthetic Biology in Cells 2.0)
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