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Data
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Big Network Inference, Integration and Analysis for Precision Medicine (BigDataNetAnalysis)
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Big Network Inference, Integration and Analysis for Precision Medicine (BigDataNetAnalysis)

A special issue of Data (ISSN 2306-5729).

Deadline for manuscript submissions: closed (30 November 2019) | Viewed by 11795

Special Issue Editors

Dr. Pietro Hiram Guzzi

E-Mail Website
Guest Editor
Department of Surgical and Medical Sciences, University of Catanzaro, Viale Europa, 88100 Catanzaro, Italy
Interests: computational biology; data science; bioinformatics; COVID-19
Special Issues, Collections and Topics in MDPI journals
Dr. Laura Antonelli

E-Mail Website
Guest Editor
Institute for High-Performance Computing and Networking, National Research Council, 80131 Naples, Italy
Interests: image processing and analysis; scientific computing; parallel computing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Swarup Roy

E-Mail Website
Guest Editor
Department of Computer Application, Sikkim University, Gangtok Tadong 737102, India
Interests: data science; machine learning; data mining; computational biology
Dr. Pierangelo Veltri

E-Mail Website
Guest Editor
Bioinformatics and Computer Science Department of Surgical and Medical Science, University "Magna Græcia" of Catanzaro, Viale Europa (Località Germaneto), 88100 Catanzaro, Italy

Special Issue Information

Dear Colleagues,

Precision Medicine is currently a hot research field and is of high interest for the bioinformatics community. The field attracts the interests of computer scientists, biologists, and medical doctors interested in its applications to biology, precision medicine, and pharmacology.

The rationale underlying the research is that many biological processes in different fields, from molecular biology to neurological sciences, may be elucidated only by considering mutual interactions among different players. For instance, the regulation of messenger RNA (mRNA) levels is due to the synergistic and antagonist actions of transcription factors (TFs) and microRNAs (miRNAs).

Similarly, network-based approaches have recently been applied to modeling the human brain. A common aspect of these different scenarios is that available technological platforms enable the investigation of only a single aspect of these mechanisms, that is, the quantification of levels of mRNA or miRNA or the investigation of the activity of single brain regions.

Consequently, a comprehensive and holistic analysis is made possible only by the integration of these data sources. Currently, the interest of researchers in this area is growing, the number of projects is increasing, and the number of challenges and issues for computer scientists is considerable. Many approaches are based on the use of results coming from graph theory; thus, the need for a workshop bringing together computer scientists and biologists/doctors arises.

Recent approaches have integrated big data in heterogeneous networks. This workshop solicits submissions discussing general concepts related to improvements and challenges in this field. In addition, surveys or positions on data integrations, as well as novel approaches of analysis are welcome.

This Special Issue of Data is dedicated to selected and extended papers from the BigDataNetAnalysis Workshop, which is in conjunction with BIBM 2018 conference held in Madrid, Spain, 3–6 December 2018.

Dr. Pietro Hiram Guzzi
Dr. Laura Antonelli
Dr. Swarup Roy
Dr. Pierangelo Veltri
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. Data is an international peer-reviewed open access monthly 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 1600 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.

Published Papers (3 papers)

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Research

12 pages, 1368 KiB  
Article
Graph Theoretic and Pearson Correlation-Based Discovery of Network Biomarkers for Cancer
by Raihanul Bari Tanvir, Tasmia Aqila, Mona Maharjan, Abdullah Al Mamun and Ananda Mohan Mondal
Data 2019, 4(2), 81; https://doi.org/10.3390/data4020081 - 05 Jun 2019
Cited by 9 | Viewed by 4017
Abstract
Two graph theoretic concepts—clique and bipartite graphs—are explored to identify the network biomarkers for cancer at the gene network level. The rationale is that a group of genes work together by forming a cluster or a clique-like structures to initiate a cancer. After [...] Read more.
Two graph theoretic concepts—clique and bipartite graphs—are explored to identify the network biomarkers for cancer at the gene network level. The rationale is that a group of genes work together by forming a cluster or a clique-like structures to initiate a cancer. After initiation, the disease signal goes to the next group of genes related to the second stage of a cancer, which can be represented as a bipartite graph. In other words, bipartite graphs represent the cross-talk among the genes between two disease stages. To prove this hypothesis, gene expression values for three cancers— breast invasive carcinoma (BRCA), colorectal adenocarcinoma (COAD) and glioblastoma multiforme (GBM)—are used for analysis. First, a co-expression gene network is generated with highly correlated gene pairs with a Pearson correlation coefficient ≥ 0.9. Second, clique structures of all sizes are isolated from the co-expression network. Then combining these cliques, three different biomarker modules are developed—maximal clique-like modules, 2-clique-1-bipartite modules, and 3-clique-2-bipartite modules. The list of biomarker genes discovered from these network modules are validated as the essential genes for causing a cancer in terms of network properties and survival analysis. This list of biomarker genes will help biologists to design wet lab experiments for further elucidating the complex mechanism of cancer. Full article
(This article belongs to the Special Issue Big Network Inference, Integration and Analysis for Precision Medicine (BigDataNetAnalysis))
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13 pages, 925 KiB  
Article
Ensemble Based Classification of Sentiments Using Forest Optimization Algorithm
by Mehreen Naz, Kashif Zafar and Ayesha Khan
Data 2019, 4(2), 76; https://doi.org/10.3390/data4020076 - 23 May 2019
Cited by 11 | Viewed by 3575
Abstract
Feature subset selection is a process to choose a set of relevant features from a high dimensionality dataset to improve the performance of classifiers. The meaningful words extracted from data forms a set of features for sentiment analysis. Many evolutionary algorithms, like the [...] Read more.
Feature subset selection is a process to choose a set of relevant features from a high dimensionality dataset to improve the performance of classifiers. The meaningful words extracted from data forms a set of features for sentiment analysis. Many evolutionary algorithms, like the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), have been applied to feature subset selection problem and computational performance can still be improved. This research presents a solution to feature subset selection problem for classification of sentiments using ensemble-based classifiers. It consists of a hybrid technique of minimum redundancy and maximum relevance (mRMR) and Forest Optimization Algorithm (FOA)-based feature selection. Ensemble-based classification is implemented to optimize the results of individual classifiers. The Forest Optimization Algorithm as a feature selection technique has been applied to various classification datasets from the UCI machine learning repository. The classifiers used for ensemble methods for UCI repository datasets are the k-Nearest Neighbor (k-NN) and Naïve Bayes (NB). For the classification of sentiments, 15–20% improvement has been recorded. The dataset used for classification of sentiments is Blitzer’s dataset consisting of reviews of electronic products. The results are further improved by ensemble of k-NN, NB, and Support Vector Machine (SVM) with an accuracy of 95% for the classification of sentiment tasks. Full article
(This article belongs to the Special Issue Big Network Inference, Integration and Analysis for Precision Medicine (BigDataNetAnalysis))
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19 pages, 6610 KiB  
Article
Isolation, Characterization, and Agent-Based Modeling of Mesenchymal Stem Cells in a Bio-construct for Myocardial Regeneration Scaffold Design
by Diana Victoria Ramírez López, María Isabel Melo Escobar, Carlos A. Peña-Reyes, Álvaro J. Rojas Arciniegas and Paola Andrea Neuta Arciniegas
Data 2019, 4(2), 71; https://doi.org/10.3390/data4020071 - 19 May 2019
Cited by 3 | Viewed by 3367
Abstract
Regenerative medicine involves methods to control and modify normal tissue repair processes. Polymer and cell constructs are under research to create tissue that replaces the affected area in cardiac tissue after myocardial infarction (MI). The aim of the present study is to evaluate [...] Read more.
Regenerative medicine involves methods to control and modify normal tissue repair processes. Polymer and cell constructs are under research to create tissue that replaces the affected area in cardiac tissue after myocardial infarction (MI). The aim of the present study is to evaluate the behavior of differentiated and undifferentiated mesenchymal stem cells (MSCs) in vitro and in silico and to compare the results that both offer when it comes to the design process of biodevices for the treatment of infarcted myocardium in biomodels. To assess in vitro behavior, MSCs are isolated from rat bone marrow and seeded undifferentiated and differentiated in multiple scaffolds of a gelled biomaterial. Subsequently, cell behavior is evaluated by trypan blue and fluorescence microscopy, which showed that the cells presented high viability and low cell migration in the biomaterial. An agent-based model intended to reproduce as closely as possible the behavior of individual MSCs by simulating cellular-level processes was developed, where the in vitro results are used to identify parameters in the agent-based model that is developed, and which simulates cellular-level processes: Apoptosis, differentiation, proliferation, and migration. Thanks to the results obtained, suggestions for good results in the design and fabrication of the proposed scaffolds and how an agent-based model can be helpful for testing hypothesis are presented in the discussion. It is concluded that assessment of cell behavior through the observation of viability, proliferation, migration, inflammation reduction, and spatial composition in vitro and in silico, represents an appropriate strategy for scaffold engineering. Full article
(This article belongs to the Special Issue Big Network Inference, Integration and Analysis for Precision Medicine (BigDataNetAnalysis))
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