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Applied Sciences
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Recent Applications of Artificial Intelligence for Bioinformatics
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Recent Applications of Artificial Intelligence 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: 10 May 2024 | Viewed by 3722

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,

The development of high-throughput sequencing technology has led to the explosive growth of bio-sequences data. How to manage and study these data to interpret biological and medical significance is full of challenges. Artificial intelligence (AI) technology provides a new data analysis technology for bioinformation data analysis and computational biology and provides a powerful solution for bioinformatics research. The main applications of artificial intelligence in bioinformatics research include: exploiting the biological significance contained in biological big data by using the existing artificial intelligence technology; and developing more efficient artificial intelligence tools to solve biological problems. AI has already produced examples of applications in bio-big-data analytics such as alphafold2. However, there are still plenty of biological problems that need AI intervention to obtain a more accurate interpretation. These urgent problems include but are not limited to: 1) analysis of different omics data, including full transcriptomics, genomics, proteomics, and epigenomics; 2) functional prediction of first-order sequence data; 3) analysis of multiple omics fusion data; 4) analysis of emerging single-cell data; and 5) some common problems faced by AI in biological big data analysis, such as data imbalance, interpretable models of deep learning, etc. This Special Issue will focus on the latest advances in the application of artificial intelligence to bioinformatics, not only in the field of methods and techniques, but also in the field of artificial intelligence solutions for biodata security.

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

  • artificial intelligence
  • deep learning
  • epigenomics
  • epitranscriptomics
  • functional genomics
  • genomics
  • machine learning
  • metabolomics
  • proteomics
  • transcriptomics

Published Papers (3 papers)

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Research

16 pages, 2808 KiB  
Article
An Approach for Cancer-Type Classification Using Feature Selection Techniques with Convolutional Neural Network
by Saleh N. Almuayqil, Murtada K. Elbashir, Mohamed Ezz, Mohanad Mohammed, Ayman Mohamed Mostafa, Meshrif Alruily and Eslam Hamouda
Appl. Sci. 2023, 13(19), 10919; https://doi.org/10.3390/app131910919 - 02 Oct 2023
Viewed by 1109
Abstract
Cancer diagnosis and treatment depend on accurate cancer-type prediction. A prediction model can infer significant cancer features (genes). Gene expression is among the most frequently used features in cancer detection. Deep Learning (DL) architectures, which demonstrate cutting-edge performance in many disciplines, are not [...] Read more.
Cancer diagnosis and treatment depend on accurate cancer-type prediction. A prediction model can infer significant cancer features (genes). Gene expression is among the most frequently used features in cancer detection. Deep Learning (DL) architectures, which demonstrate cutting-edge performance in many disciplines, are not appropriate for the gene expression data since it contains a few samples with thousands of features. This study presents an approach that applies three feature selection techniques (Lasso, Random Forest, and Chi-Square) on gene expression data obtained from Pan-Cancer Atlas through the TCGA Firehose Data using R statistical software version 4.2.2. We calculated the feature importance of each selection method. Then we calculated the mean of the feature importance to determine the threshold for selecting the most relevant features. We constructed five models with a simple convolutional neural networks (CNNs) architecture, which are trained using the selected features and then selected the winning model. The winning model achieved a precision of 94.11%, a recall of 94.26%, an F1-score of 94.14%, and an accuracy of 96.16% on a test set. Full article
(This article belongs to the Special Issue Recent Applications of Artificial Intelligence for Bioinformatics)
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13 pages, 2268 KiB  
Article
A Stacking Machine Learning Method for IL-10-Induced Peptide Sequence Recognition Based on Unified Deep Representation Learning
by Jiayu Li, Jici Jiang, Hongdi Pei and Zhibin Lv
Appl. Sci. 2023, 13(16), 9346; https://doi.org/10.3390/app13169346 - 17 Aug 2023
Viewed by 1132
Abstract
Interleukin-10 (IL-10) has anti-inflammatory properties and is a crucial cytokine in regulating immunity. The identification of IL-10 through wet laboratory experiments is costly and time-intensive. Therefore, a new IL-10-induced peptide recognition method, IL10-Stack, was introduced in this research, which was based on unified [...] Read more.
Interleukin-10 (IL-10) has anti-inflammatory properties and is a crucial cytokine in regulating immunity. The identification of IL-10 through wet laboratory experiments is costly and time-intensive. Therefore, a new IL-10-induced peptide recognition method, IL10-Stack, was introduced in this research, which was based on unified deep representation learning and a stacking algorithm. Two approaches were employed to extract features from peptide sequences: Amino Acid Index (AAindex) and sequence-based unified representation (UniRep). After feature fusion and optimized feature selection, we selected a 1900-dimensional UniRep feature vector and constructed the IL10-Stack model using stacking. IL10-Stack exhibited excellent performance in IL-10-induced peptide recognition (accuracy (ACC) = 0.910, Matthews correlation coefficient (MCC) = 0.820). Relative to the existing methods, IL-10Pred and ILeukin10Pred, the approach increased in ACC by 12.1% and 2.4%, respectively. The IL10-Stack method can identify IL-10-induced peptides, which aids in the development of immunosuppressive drugs. Full article
(This article belongs to the Special Issue Recent Applications of Artificial Intelligence for Bioinformatics)
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20 pages, 2350 KiB  
Article
A Framework for Identifying Essential Proteins with Hybridizing Deep Neural Network and Ordinary Least Squares
by Sai Zou, Yunbin Hu and Wenya Yang
Appl. Sci. 2023, 13(15), 8613; https://doi.org/10.3390/app13158613 - 26 Jul 2023
Viewed by 637
Abstract
Essential proteins are vital for maintaining life activities and play a crucial role in biological processes. Identifying essential proteins is of utmost importance as it helps in understanding the minimal requirements for cell life, discovering pathogenic genes and drug targets, diagnosing diseases, and [...] Read more.
Essential proteins are vital for maintaining life activities and play a crucial role in biological processes. Identifying essential proteins is of utmost importance as it helps in understanding the minimal requirements for cell life, discovering pathogenic genes and drug targets, diagnosing diseases, and comprehending the mechanism of biological evolution. The latest research suggests that integrating protein–protein interaction (PPI) networks and relevant biological sequence features can enhance the accuracy and robustness of essential protein identification. In this paper, a deep neural network (DNN) method was used to identify a yeast essential protein, which was named IYEPDNN. The method combines gene expression profiles, PPI networks, and orthology as input features to improve the accuracy of DNN while reducing computational complexity. To enhance the robustness of the yeast dataset, the common least squares method is used to supplement absenting data. The correctness and effectiveness of the IYEPDNN method are verified using the DIP and GAVIN databases. Our experimental results demonstrate that IYEPDNN achieves an accuracy of 84%, and it outperforms state-of-the-art methods (WDC, PeC, OGN, ETBUPPI, RWAMVL, etc.) in terms of the number of essential proteins identified. The findings of this study demonstrate that the correlation between features plays a crucial role in enhancing the accuracy of essential protein prediction. Additionally, selecting the appropriate training data can effectively address the issue of imbalanced training data in essential protein identification. Full article
(This article belongs to the Special Issue Recent Applications of Artificial Intelligence for Bioinformatics)
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