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Algorithms
Special Issues
Selected papers from the 10th International Workshop on Biological Knowledge...
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Selected papers from the 10th International Workshop on Biological Knowledge Discovery from Big Data (BIOKDD'19)

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (30 November 2019) | Viewed by 344

Special Issue Editors

Dr. Davide Verzotto

E-Mail Website
Guest Editor
1. Institute for Informatics and Telematics, CNR, Pisa, Italy
2. Department of Information Engineering, University of Pisa, Pisa, Italy
Interests: algorithms & data structures; bioinformatics & computational biology; machine learning; scalable data mining
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Mourad Elloumi

E-Mail Website
Guest Editor
1. The Laboratory of Technologies of Information and Communication, and Electrical Engineering (LaTICE), National Higher School of Engineers of Tunis (ENSIT), University of Tunis, Tunisia
2. Faculty of Economic Sciences and Management of Tunis (FSEGT), University of Tunis-El Manar, Tunisia
Interests: algorithmics; bioinformatics; knowledge discovery and data mining
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, there has been a rapid development of biological technologies producing more and more biological data, i.e. data related to biological macromolecules (DNA, RNA, and proteins).  The rise of next-generation sequencing technologies (NGS), also known as high-throughput sequencing technologies, has contributed actively to the deluge of these data.  In general, these data are big, heterogeneous, complex, and distributed worldwide in databases.  Analyzing biological big data is a challenging task, not only because of its complexity and its multiple and numerous correlated factors, but also because of the continuous evolution of our understanding of the biological mechanisms.  Classical approaches of biological data analysis are no longer efficient and produce only a very limited amount of information, compared to the numerous and complex biological mechanisms under study.  From here comes the necessity to adopt new computer tools and develop new in silico high performance approaches to support us in the analysis of biological big data and, hence, to help us in the understanding of the correlations that exist between, on the one hand, structures and functional patterns in biological macromolecules and, on the other hand, genetic and biochemical mechanisms.  Biological Knowledge Discovery from Big Data (BIOKDD) is a response to these new trends. 
 
Researchers are encouraged to submit original research contributions in all major areas, which include, but are not limited to:-
 
Data Preprocessing: biological big data storage, representation and management (e.g. data warehouses, databases, sequences, trees, graphs, biological networks and pathways), biological big data cleaning (e.g. errors removal, redundant data removal, completion of missing data), Feature Extraction (e.g. motifs, subgraphs), feature selection (e.g. filter approaches, wrapper approaches, hybrid approaches, embedded approaches).
 
Data Mining: biological big data regression (e.g. regression of biological sequence), biological big data clustering/biclustering (e.g. microarray data biclustering, clustering/biclustering of biological sequences), biological big data classification (e.g. classification of biological sequence), association rules learning from biological big data, text mining and application to biological sequences, web mining and application to biological big data, parallel, cloud and grid computing for biological and scalable big data mining.
 
Data Postprocessing: biological nuggets of knowledge filtering, biological nuggets of knowledge representation and visualization, biological nuggets of knowledge evaluation (e.g. calculation of the classification error rate, evaluation of the association rules via numerical indicators or measurements of interest), biological nuggets of knowledge integration.
 
The topics of interest to this Special Issue cover the scope of the International Workshop on Biological Knowledge Discovery from Big Data (BIOKDD 2019) (http://www.dexa.org/biokdd2019).
 
Extended versions of selected papers presented at BIOKDD 2019 are invited, but this call for papers is open to anyone who wishes to contribute by submitting a relevant research manuscript.

Dr. Davide Verzotto
Prof. Dr. Mourad Elloumi
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. Algorithms 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

There is no accepted submissions to this special issue at this moment.
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