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Applied Sciences
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The Development of Novel Integrative Bioinformatics Based Machine Learning Techniques...
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The Development of Novel Integrative Bioinformatics Based Machine Learning Techniques and Multi-Omics Data Integration

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 May 2023) | Viewed by 6469

Special Issue Editor

Prof. Dr. Malik Yousef

E-Mail Website
Guest Editor
Department of Information Systems, Zefat Academic College, Zefat 13206, Israel
Interests: bioinformatics; text mining; biological domain knowledge-based feature selection on gene expression data; microRNA; one-class classification
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the last two decades, there have been massive advancements in the generation of data in the field of bioinformatics, such as in high throughput technologies, which have resulted in the exponential growth of public repositories of gene expression datasets for various phenotypes. Integrative bioinformatics is a discipline of bioinformatics that focuses on problems of data integration. With the advent of sequencing technology, biology has become increasingly dependent on data generated at these levels, which together is called as “multi-omics” data. One of the main goals of this special issue is to explore the novel methods that integrate different types of information in order to improve the identification of the biomolecular signatures of diseases and the discovery of new potential targets for treatment. These integrative approaches are expected to aid in the prediction, diagnosis, and treatment of diseases, as well as to enlighten us with regard to disease state dynamics, mechanisms of their onset and progression. The integration of various types of biological information will necessitate the development of novel techniques for integration and data analysis. Thus, the application of machine learning (ML) is becoming an important element in meeting the challenge of analysis and integration of different data types. ML is also becoming increasingly important for the development of such techniques.

With this Special Issue, we envision providing a forum for the application of machine learning to a wide variety of different data sets to demonstrate its utility in addressing these growing computational challenges. We invite your contributions, either in the form of original research articles, reviews, or shorter perspective articles on all aspects related to the theme of “Integrative Bioinformatics Based Machine Learning Techniques ”. Articles with sound methodology and scientific practice are particularly welcomed. Relevant topics include, but are not limited to, the following:

  • Machine learning;
  • Feature selection;
  • Biomedical text classification;
  • Integrative analysis of biomedical data;
  • Machine learning in bioinformatics integrated with biological domain knowledge;
  • Integrative analysis of multi-omics;
  • Deep learning approaches;
  • Genomics;
  • Proteomics;
  • System biology;
  • Gene expression analysis;
  • Machine learning for biomedical data analysis;
  • Computational modeling and data integration;
  • Biomedical text mining and ontologies;
  • Next-generation sequencing data analysis;
  • Drug discovery;
  • Single cell sequencing data analysis;
  • Microbiome and metagenomics;
  • Machine learning and deep learning in image analysis.

Prof. Dr. Malik Yousef
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

  • integrative bioinformatics
  • multi omics data integration
  • machine learning for integrative biological knowledge
  • computational biology for integrative bioinformatics
  • integrative analysis of biomedical big data

Published Papers (3 papers)

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Research

17 pages, 1327 KiB  
Article
AMP-GSM: Prediction of Antimicrobial Peptides via a Grouping–Scoring–Modeling Approach
by Ümmü Gülsüm Söylemez, Malik Yousef and Burcu Bakir-Gungor
Appl. Sci. 2023, 13(8), 5106; https://0-doi-org.brum.beds.ac.uk/10.3390/app13085106 - 19 Apr 2023
Cited by 2 | Viewed by 1603
Abstract
Due to the increasing resistance of bacteria to antibiotics, scientists began seeking new solutions against this problem. One of the most promising solutions in this field are antimicrobial peptides (AMP). To identify antimicrobial peptides, and to aid the design and production of novel [...] Read more.
Due to the increasing resistance of bacteria to antibiotics, scientists began seeking new solutions against this problem. One of the most promising solutions in this field are antimicrobial peptides (AMP). To identify antimicrobial peptides, and to aid the design and production of novel antimicrobial peptides, there is a growing interest in the development of computational prediction approaches, in parallel with the studies performing wet-lab experiments. The computational approaches aim to understand what controls antimicrobial activity from the perspective of machine learning, and to uncover the biological properties that define antimicrobial activity. Throughout this study, we aim to develop a novel prediction approach that can identify peptides with high antimicrobial activity against selected target bacteria. Along this line, we propose a novel method called AMP-GSM (antimicrobial peptide-grouping–scoring–modeling). AMP-GSM includes three main components: grouping, scoring, and modeling. The grouping component creates sub-datasets via placing the physicochemical, linguistic, sequence, and structure-based features into different groups. The scoring component gives a score for each group according to their ability to distinguish whether it is an antimicrobial peptide or not. As the final part of our method, the model built using the top-ranked groups is evaluated (modeling component). The method was tested for three AMP prediction datasets, and the prediction performance of AMP-GSM was comparatively evaluated with several feature selection methods and several classifiers. When we used 10 features (which are members of the physicochemical group), we obtained the highest area under curve (AUC) value for both the Gram-negative (99%) and Gram-positive (98%) datasets. AMP-GSM investigates the most significant feature groups that improve AMP prediction. A number of physico-chemical features from the AMP-GSM’s final selection demonstrate how important these variables are in terms of defining peptide characteristics and how they should be taken into account when creating models to predict peptide activity. Full article
(This article belongs to the Special Issue The Development of Novel Integrative Bioinformatics Based Machine Learning Techniques and Multi-Omics Data Integration)
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18 pages, 1999 KiB  
Article
A Knowledge Graph Embedding Approach for Polypharmacy Side Effects Prediction
by Jinwoo Kim and Miyoung Shin
Appl. Sci. 2023, 13(5), 2842; https://0-doi-org.brum.beds.ac.uk/10.3390/app13052842 - 22 Feb 2023
Cited by 1 | Viewed by 1572
Abstract
Predicting the side effects caused by drug combinations may facilitate the prescription of multiple medications in a clinical setting. So far, several prediction models of multidrug side effects based on knowledge graphs have been developed, showing good performance under constrained test conditions. However, [...] Read more.
Predicting the side effects caused by drug combinations may facilitate the prescription of multiple medications in a clinical setting. So far, several prediction models of multidrug side effects based on knowledge graphs have been developed, showing good performance under constrained test conditions. However, these models usually focus on relationships between neighboring nodes of constituent drugs rather than whole nodes, and do not fully exploit the information about the occurrence of single drug side effects. The lack of learning the information on such relationships and single drug data may hinder improvement of performance. Moreover, compared with all possible drug combinations, the highly limited range of drug combinations used for model training prevents achieving high generalizability. To handle these problems, we propose a unified embedding-based prediction model using knowledge graph constructed with data of drug–protein and protein–protein interactions. Herein, single or multiple drugs or proteins are mapped into the same embedding space, allowing us to (1) jointly utilize side effect occurrence data associated with single drugs and multidrug combinations to train prediction models and (2) quantify connectivity strengths between drugs and other entities such as proteins. Due to these characteristics, it becomes also possible to utilize the quantified relationships between distant nodes, as well as neighboring nodes, of all possible multidrug combinations to regularize the models. Compared with existing methods, our model showed improved performance, especially in predicting the side effects of new combinations containing novel drugs that have no clinical information on polypharmacy effects. Furthermore, our unified embedding vectors have been shown to provide interpretability, albeit to a limited extent, for proteins highly associated with multidrug side effect. Full article
(This article belongs to the Special Issue The Development of Novel Integrative Bioinformatics Based Machine Learning Techniques and Multi-Omics Data Integration)
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20 pages, 4798 KiB  
Article
Evaluation and Optimization of Biomedical Image-Based Deep Convolutional Neural Network Model for COVID-19 Status Classification
by Soumadip Ghosh, Suharta Banerjee, Supantha Das, Arnab Hazra, Saurav Mallik, Zhongming Zhao and Ayan Mukherji
Appl. Sci. 2022, 12(21), 10787; https://0-doi-org.brum.beds.ac.uk/10.3390/app122110787 - 25 Oct 2022
Cited by 2 | Viewed by 1239
Abstract
Accurate detection of an individual’s coronavirus disease 2019 (COVID-19) status has become critical as the COVID-19 pandemic has led to over 615 million cases and over 6.454 million deaths since its outbreak in 2019. Our proposed research work aims to present a deep [...] Read more.
Accurate detection of an individual’s coronavirus disease 2019 (COVID-19) status has become critical as the COVID-19 pandemic has led to over 615 million cases and over 6.454 million deaths since its outbreak in 2019. Our proposed research work aims to present a deep convolutional neural network-based framework for the detection of COVID-19 status from chest X-ray and CT scan imaging data acquired from three benchmark imagery datasets. VGG-19, ResNet-50 and Inception-V3 models are employed in this research study to perform image classification. A variety of evaluation metrics including kappa statistic, Root-Mean-Square Error (RMSE), accuracy, True Positive Rate (TPR), False Positive Rate (FPR), Recall, precision, and F-measure are used to ensure adequate performance of the proposed framework. Our findings indicate that the Inception-V3 model has the best performance in terms of COVID-19 status detection. Full article
(This article belongs to the Special Issue The Development of Novel Integrative Bioinformatics Based Machine Learning Techniques and Multi-Omics Data Integration)
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