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Application of Evolutionary Computing for Bioinformatics
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Application of Evolutionary Computing for Bioinformatics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 6036

Special Issue Editor

Dr. Zhibin Lv

E-Mail Website
Guest Editor
Department of Medical Instruments and Information, College of Biomedical Engineering, Sichuan University, Chengdu 610065, China
Interests: bioelectronics; biological information processing; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Bioinformatics is an emerging multidisciplinary field that engages in the acquisition, processing, storage, distribution, and interpretation of biological information and integrates mathematical, computer science, and biological tools to understand biology in data. By contrast, evolutionary computing, also known as evolutionary algorithms, is a family of global optimization algorithms inspired by biological evolution, as well as the subfields of artificial intelligence and soft computing that study these algorithms. It is based on a series of algorithms based on population evolution including genetic algorithm (GA), evolutionary strategy (ES), genetic programming (GP), etc., with meta-heuristic or stochastic optimization features. In recent years, with the continuous accumulation of biological big data, the processing of biological information has become challenging. Evolutionary computing, especially the combination of evolutionary computing and emerging algorithms such as machine learning and deep learning, has been widely used in bioinformatics.

In this Special Issue, we invite submissions exploring cutting-edge research and recent advances in the fields of applying evolutionary computing algorithms for bioinformatics. Both theoretical and experimental studies are welcome, as well as comprehensive review and survey papers.  The bioinformatics tasks mentioned in the manuscripts may include but are not limited to 1) the alignment and comparison of DNA, RNA, and protein sequences; 2) epigenetics; 3) identification of gene regulatory networks; 4) structure prediction; 5) biological sequence identification and functional analysis; and 6) the relationship between biological sequences and diseases, etc.

Dr. Zhibin Lv
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. Applied Sciences 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 2400 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

  • evolutionary computing
  • bioinformatics
  • machine learning
  • biosequence analysis

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Published Papers (2 papers)

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16 pages, 1392 KiB  
Article
Identification of Thermophilic Proteins Based on Sequence-Based Bidirectional Representations from Transformer-Embedding Features
by Hongdi Pei, Jiayu Li, Shuhan Ma, Jici Jiang, Mingxin Li, Quan Zou and Zhibin Lv
Appl. Sci. 2023, 13(5), 2858; https://doi.org/10.3390/app13052858 - 23 Feb 2023
Cited by 18 | Viewed by 3425
Abstract
Thermophilic proteins have great potential to be utilized as biocatalysts in biotechnology. Machine learning algorithms are gaining increasing use in identifying such enzymes, reducing or even eliminating the need for experimental studies. While most previously used machine learning methods were based on manually [...] Read more.
Thermophilic proteins have great potential to be utilized as biocatalysts in biotechnology. Machine learning algorithms are gaining increasing use in identifying such enzymes, reducing or even eliminating the need for experimental studies. While most previously used machine learning methods were based on manually designed features, we developed BertThermo, a model using Bidirectional Encoder Representations from Transformers (BERT), as an automatic feature extraction tool. This method combines a variety of machine learning algorithms and feature engineering methods, while relying on single-feature encoding based on the protein sequence alone for model input. BertThermo achieved an accuracy of 96.97% and 97.51% in 5-fold cross-validation and in independent testing, respectively, identifying thermophilic proteins more reliably than any previously described predictive algorithm. Additionally, BertThermo was tested by a balanced dataset, an imbalanced dataset and a dataset with homology sequences, and the results show that BertThermo was with the best robustness as comparied with state-of-the-art methods. The source code of BertThermo is available. Full article
(This article belongs to the Special Issue Application of Evolutionary Computing for Bioinformatics)
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11 pages, 1078 KiB  
Article
Prediction of Cell-Penetrating Peptides Using a Novel HSIC-Based Multiview TSK Fuzzy System
by Peng Liu, Shulin Zhao, Quan Zou and Yijie Ding
Appl. Sci. 2022, 12(11), 5383; https://doi.org/10.3390/app12115383 - 26 May 2022
Cited by 6 | Viewed by 1808
Abstract
Cell-penetrating peptides (CPPs) are short peptides that can carry cargo into cells. CPPs are widely utilized due to their powerful loading capacity and transduction efficiency. Identifying CPPs is the basis for studying their functions and mechanisms; however, experimental methods to identify CPPs are [...] Read more.
Cell-penetrating peptides (CPPs) are short peptides that can carry cargo into cells. CPPs are widely utilized due to their powerful loading capacity and transduction efficiency. Identifying CPPs is the basis for studying their functions and mechanisms; however, experimental methods to identify CPPs are expensive and time-consuming. Recently, CPP predictors based on machine learning methods have become a research hotspot. Although considerable progress has been made, some challenges remain unresolved. First, most predictors employ a variety of feature descriptors to transform an original sequence into multiview data; however, extant methods ignore the relationships between different views, limiting further performance improvement. Second, most machine learning models are actually black boxes and cannot offer insightful advice. In this paper, a novel Hilbert–Schmidt independence criterion (HSIC)-based multiview TSK fuzzy system is proposed. Compared with other machine learning methods, TSK fuzzy systems have better interpretability, and the introduction of multiview mechanisms provides comprehensive insight into the intrinsic laws of the data. HSIC is utilized here to measure the independence and enhance the complementarity between different views. Notably, the proposed method attained prediction accuracy results of 92.2% and 96.2% for the training and independent test sets, respectively. The empirical results show that our promising approach features greater recognition performance than the state-of-the-art method. Full article
(This article belongs to the Special Issue Application of Evolutionary Computing for Bioinformatics)
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