Open Access Published by De Gruyter

Volume 18 Issue 2 - Special Section: Selected extended papers of the 14th International Conference on Practical Applications of Computational Biology and Bioinformatics (PACBB); Guest Editor: Florentino Fdez-Riverola

June 2021

Issue of Journal of Integrative Bioinformatics

Contents
Journal Overview

Publicly Available June 22, 2021

Frontmatter

Page range: i-ii

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Special Section: Selected extended papers of the 14th International Conference on Practical Applications of Computational Biology and Bioinformatics (PACBB), Guest Editor: Florentino Fdez-Riverola

Open Access May 20, 2021

Cross-evaluation of social mining for classification of depressed online personas

Alina Trifan, José Luis Oliveira

Page range: 101-110

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Abstract

With the continuous increase in the use of social networks, social mining is steadily becoming a powerful component of digital phenotyping. In this paper we explore social mining for the classification of self-diagnosed depressed users of Reddit as social network. We conduct a cross evaluation study based on two public datasets in order to understand the impact of transfer learning when the data source is virtually the same. We further complement these results with an experiment of transfer learning in post-partum depression classification, using a corpus we have collected for the matter. Our findings show that transfer learning in social mining might still be at an early stage in computational research and we thoroughly discuss its implications.

Open Access May 31, 2021

Structural relation matching: an algorithm to identify structural patterns into RNAs and their interactions

Michela Quadrini

Page range: 111-126

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Abstract

RNA molecules play crucial roles in various biological processes. Their three-dimensional configurations determine the functions and, in turn, influences the interaction with other molecules. RNAs and their interaction structures, the so-called RNA–RNA interactions, can be abstracted in terms of secondary structures, i.e., a list of the nucleotide bases paired by hydrogen bonding within its nucleotide sequence. Each secondary structure, in turn, can be abstracted into cores and shadows. Both are determined by collapsing nucleotides and arcs properly. We formalize all of these abstractions as arc diagrams, whose arcs determine loops. A secondary structure, represented by an arc diagram, is pseudoknot-free if its arc diagram does not present any crossing among arcs otherwise, it is said pseudoknotted. In this study, we face the problem of identifying a given structural pattern into secondary structures or the associated cores or shadow of both RNAs and RNA–RNA interactions, characterized by arbitrary pseudoknots. These abstractions are mapped into a matrix, whose elements represent the relations among loops. Therefore, we face the problem of taking advantage of matrices and submatrices. The algorithms, implemented in Python, work in polynomial time. We test our approach on a set of 16S ribosomal RNAs with inhibitors of Thermus thermophilus , and we quantify the structural effect of the inhibitors.

Open Access March 29, 2021

Cardiac well-being indexes: a decision support tool to monitor cardiovascular health

Ana Duarte, Orlando Belo

Page range: 127-138

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Abstract

Despite the increasing awareness about its severity and the importance of adopting preventive habits, cardiovascular disease remains the leading cause of death worldwide. Most people already recognize that a healthy lifestyle, which includes a balanced diet and the practice of physical activity, is essential to prevent this disease. However, since few simple mechanisms allow a self-assessment and a continuous monitoring of the level of cardiac well-being, people are not conscious enough about their own cardiovascular health status. In this context, this paper presents and describes a tool related to the creation of cardiac well-being indexes that allow a quick and intuitive monitoring and visualization of the users’ cardiovascular health level over time. For its implementation, data mining techniques were used to calculate the indexes, and a data warehouse was built to archive the data and to support the construction of dashboards for presenting the results.

Regular Contributions

Open Access December 29, 2020

A novel gene expression test method of minimizing breast cancer risk in reduced cost and time by improving SVM-RFE gene selection method combined with LASSO

Madhuri Gupta, Bharat Gupta

Page range: 139-153

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Abstract

Breast cancer is the leading diseases of death in women. It induces by a genetic mutation in breast cancer cells. Genetic testing has become popular to detect the mutation in genes but test cost is relatively expensive for several patients in developing countries like India. Genetic test takes between 2 and 4 weeks to decide the cancer. The time duration suffers the prognosis of genes because some patients have high rate of cancerous cell growth. In the research work, a cost and time efficient method is proposed to predict the gene expression level on the basis of clinical outcomes of the patient by using machine learning techniques. An improved SVM-RFE_MI gene selection technique is proposed to find the most significant genes related to breast cancer afterward explained variance statistical analysis is applied to extract the genes contain high variance. Least Absolute Shrinkage Selector Operator (LASSO) and Ridge regression techniques are used to predict the gene expression level. The proposed method predicts the expression of significant genes with reduced Root Mean Square Error and acceptable adjusted R-square value. As per the study, analysis of these selected genes is beneficial to diagnose the breast cancer at prior stage in reduced cost and time.

Open Access December 29, 2020

A computational model for GPCR-ligand interaction prediction

Shiva Karimi, Maryam Ahmadi, Farjam Goudarzi, Reza Ferdousi

Page range: 155-165

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Abstract

G protein-coupled receptors (GPCRs) play an essential role in critical human activities, and they are considered targets for a wide range of drugs. Accordingly, based on these crucial roles, GPCRs are mainly considered and focused on pharmaceutical research. Hence, there are a lot of investigations on GPCRs. Experimental laboratory research is very costly in terms of time and expenses, and accordingly, there is a marked tendency to use computational methods as an alternative method. In this study, a prediction model based on machine learning (ML) approaches was developed to predict GPCRs and ligand interactions. Decision tree (DT), random forest (RF), multilayer perceptron (MLP), support vector machine (SVM), and Naive Bayes (NB) were the algorithms that were investigated in this study. After several optimization steps, receiver operating characteristic (ROC) for DT, RF, MLP, SVM, and NB algorithm were 95.2, 98.1, 96.3, 95.5, and 97.3, respectively. Accordingly final model was made base on the RF algorithm. The current computational study compared with others focused on specific and important types of proteins (GPCR) interaction and employed/examined different types of sequence-based features to obtain more accurate results. Drug science researchers could widely use the developed prediction model in this study. The developed predictor was applied over 16,132 GPCR-ligand pairs and about 6778 potential interactions predicted.

Open Access February 22, 2021

BioDWH2: an automated graph-based data warehouse and mapping tool

Marcel Friedrichs

Page range: 167-176

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Abstract

Data integration plays a vital role in scientific research. In biomedical research, the OMICS fields have shown the need for larger datasets, like proteomics, pharmacogenomics, and newer fields like foodomics. As research projects require multiple data sources, mapping between these sources becomes necessary. Utilized workflow systems and integration tools therefore need to process large amounts of heterogeneous data formats, check for data source updates, and find suitable mapping methods to cross-reference entities from different databases. This article presents BioDWH2, an open-source, graph-based data warehouse and mapping tool, capable of helping researchers with these issues. A workspace centered approach allows project-specific data source selections and Neo4j or GraphQL server tools enable quick access to the database for analysis. The BioDWH2 tools are available to the scientific community at https://github.com/BioDWH2.

About this journal

Objective
Journal of Integrative Bioinformatics (JIB) is an international open access journal publishing original peer-reviewed research articles in all aspects of integrative bioinformatics.

Molecular biology produces huge amounts of data in the post-genomic era. This includes data describing metabolic mechanisms and pathways, structural genomic organization, patterns of regulatory regions; proteomics, transcriptomics, and metabolomics. On the one hand, analysis of this data uses essentially the methods and concepts of computer science; on the other hand, the range of biological tasks solved by researchers determines the range and scope of the data. Currently, there are about 1,000 database systems and various analytical tools available via the Internet which are directed at solving various biological tasks.

The challenge we have is to integrate these list-parts and relationships from genomics and proteomics at novel levels of understanding. Integrative Bioinformatics is a new area of research using the tools of computer science and electronic infrastructure applied to Biotechnology. These tools will also represent the backbone of the concept of a virtual cell.

Topics

Software applications/tools and databases covering the following topics:

Molecular Databases, Information Systems and Data Warehouses
Integration of Data, Methods and Tools
Metabolic and Regulatory Network Modeling and Simulation
Signal Pathways and Cell Control
Network Analysis
Medical Informatics, Biomedicine and Biotechnology
Integrative Approaches for Drug Design
Integrative Data and Text Mining Approaches
Integrative, whole cell and molecular modeling
Visualization and animation

Review papers are also welcome with regard to JIB.tools.

Article formats
Research articles, Review papers, Workshop contributions (if peer-reviewed)

Article processing charges (APCs)
Each unsolicited article, which is accepted for publication in the Journal of Integrative Bioinformatics is subject to an Article Processing Charge of 1,000€.
The Open Access publication of invited articles for Special Issues is sponsored by the editors.

Inquiries concerning APCs should be addressed to the Editorial Office at De Gruyter (see contact details below).