Data-Based Bioinformatics and Applications

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: 30 April 2024 | Viewed by 2428

Special Issue Editors


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Guest Editor
Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3216, Australia
Interests: data mining; machine learning; bioinformatics; computational biology; data sciences
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Computer Engineering, Faculty of Engineering, Fasa University, Fasa, Iran
Interests: data mining; machine learning; bioinformatics; computational biology; data sciences

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Guest Editor
School of Medicine, Deakin University, Waurn Ponds, VIC 3216, Australia
Interests: data sciences; statistics; epidemiology; preventive medicine

Special Issue Information

Dear Colleagues,

Mining and analyzing data-based bioinformatics makes it possible to screen key genes and pathways associated with some diseases. Data-based bioinformatics can be leveraged to create an atmosphere in which all stakeholders can seamlessly access data and process it to meet their needs. It can be used to explore protein networks in the diagnosis of diseases, such as the neuroinflammatory processes in Alzheimer’s disease. High-throughput data-based bioinformatics strategies are efficient and comprehensive research methods that have effectuated new perspectives and remarkable breakthroughs in resolving disease perplexities.

This Special Issue will publish papers on a wide range of data-based bioinformatics topics and applications. This Special Issue will also publish reviews for the users of databases and analytical tools of contemporary genetics, molecular and systems biology. Across this topic, papers must build on a deep understanding and utilization of medical domain knowledge and should consider pragmatic translation for clinical care or applications. Papers must focus on novel informatics methods and its comparison to the current approaches. This Special Issue will consider articles describing novel computational algorithms and software, models and tools, including statistical methods, machine learning and artificial intelligence, as well as systems biology. Topics of interest include (but are not limited to):

  • Clinical decision support;
  • Artificial intelligence and machine learning used for data-based bioinformatics;
  • Knowledge representation for healthcare;
  • Translational bioinformatics;
  • Ontology for bioinformatics applications;
  • Intelligent mining of large-scale bio-data;
  • Ensemble methods in bioinformatics;
  • Bioinformatics research and applications;
  • Data dimension reduction methods in high-dimensional databases;
  • Clinical research bioinformatics;
  • Transfer learning in bioinformatics;
  • Multitask learning in bioinformatics;
  • Graph neural networks and their current applications in bioinformatics;
  • Computational intelligence in data-based bioinformatics;
  • Pattern recognition techniques in bioinformatics;
  • Software and databases.

Dr. Roohallah Alizadehsani
Dr. Mohamad Roshanzamir
Dr. Mojtaba Lotfaliany
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. Big Data and Cognitive Computing 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 1800 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

  • data-based bioinformatics
  • gene set enrichment analysis
  • gene ontology
  • machine learning and data-based bioinformatics
  • pattern recognition in data-based bioinformatics
  • application of meta-heuristic algorithms in data-based bioinformatics
  • deep learning and data-based bioinformatics
  • data-based bioinformatics usage in disease detection
  • data mining algorithms for data-based bioinformatics
  • gene therapy and data-based bioinformatics
  • classification and clustering algorithms in data-based bioinformatics
  • applications of data-based bioinformatics

Published Papers (1 paper)

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Research

13 pages, 749 KiB  
Article
Massive Parallel Alignment of RNA-seq Reads in Serverless Computing
by Pietro Cinaglia, José Luis Vázquez-Poletti and Mario Cannataro
Big Data Cogn. Comput. 2023, 7(2), 98; https://doi.org/10.3390/bdcc7020098 - 15 May 2023
Cited by 3 | Viewed by 1733
Abstract
In recent years, the use of Cloud infrastructures for data processing has proven useful, with a computing potential that is not affected by the limitations of a local infrastructure. In this context, Serverless computing is the fastest-growing Cloud service model due to its [...] Read more.
In recent years, the use of Cloud infrastructures for data processing has proven useful, with a computing potential that is not affected by the limitations of a local infrastructure. In this context, Serverless computing is the fastest-growing Cloud service model due to its auto-scaling methodologies, reliability, and fault tolerance. We present a solution based on in-house Serverless infrastructure, which is able to perform large-scale RNA-seq data analysis focused on the mapping of sequencing reads to a reference genome. The main contribution was bringing the computation of genomic data into serverless computing, focusing on RNA-seq read-mapping to a reference genome, as this is the most time-consuming task for some pipelines. The proposed solution handles massive parallel instances to maximize the efficiency in terms of running time. We evaluated the performance of our solution by performing two main tests, both based on the mapping of RNA-seq reads to Human GRCh38. Our experiments demonstrated a reduction of 79.838%, 90.079%, and 96.382%, compared to the local environments with 16, 8, and 4 virtual cores, respectively. Furthermore, serverless limitations were investigated. Full article
(This article belongs to the Special Issue Data-Based Bioinformatics and Applications)
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