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Submission Preparation Checklist

As part of the submission process, authors are required to check off their submission's compliance with all of the following items, and submissions may be returned to authors that do not adhere to these guidelines.
  • The submission has not been previously published, nor is it before another journal for consideration (or an explanation has been provided in Comments to the Editor).
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  • The text is single-spaced; uses a 12-point font; employs italics, rather than underlining (except with URL addresses); and all illustrations, figures, and tables are placed within the text at the appropriate points, rather than at the end.
  • The text adheres to the stylistic and bibliographic requirements outlined in the Author Guidelines.

Author Guidelines

Submission Process

Manuscripts for the Journal of Decision Analytics and Intelligent Computing should be submitted online at The submitting author, who is generally the corresponding author, is responsible for the manuscript during the submission and peer-review process. The submitting author must ensure that all eligible co-authors have been included in the author list and that they all have read and approved the submitted version of the manuscript.

Authors who are interested in submitting their papers to the JDAIC journal must send the request to the Editorial Office and attach a manuscript to the email. The request should be sent to email After receiving the response from the Editorial Office authors can submit a paper to the system.

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Editorial staff or editors shall not be involved in processing their own academic work. Submissions authored by editorial staff/editors will be assigned to at least two independent outside reviewers. Decisions will be made by other editorial board members who do not have conflicts of interest with the author. Journal staff are not involved in the processing of their own work submitted to JDAIC.

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Section default policy

Integration of Artificial Intelligence with Network Biology

Recent advancements in experimental technologies have expanded the availability and quantity of data in biology. There has been a steady growth in demand for technology that is intelligent and can react and perform in the situation more accurately. Advances in digital technology are redefining the way we deliver technology to people and make them comfortable with the use of tech functions. Given the importance of interactions in biological processes, such as the interactions between proteins or the bonds within a chemical compound, this data is often represented in the form of a biological network. The rise of this data has created a need for new computational tools to analyze networks. One major trend in the field is to use Ai algorithm which more specifically uses methods that work with networks. 

Biological databases that once comprised sequences and structures of compounds have now advanced into the storage of more complex and bulk data. Most Microarray Profiling studies are based upon a limited subset of the complete expression dataset. We realize that full potential can only be reached upon integration and unification of all available data. And Ai is playing a vital role in the integration and unification of datasets. The ability of AI to make informed decisions, learn and perceive the environment, and predict certain behavior, among its many other skills, makes this application of paramount importance in today's world. For decades, we have tried building computational models for teaching machines.

However, one major setback here is the amount of variation in data collected from different sources. As we know, we shall be able to achieve optimized results only when we would be able to integrate data from a variety of different sources and then devise an automated learning algorithm to analyze and infer prediction based on previous learning experiences.

Keywords: Artificial Intelligence, Machine Learning; Signal Processing; E-Health, Deep
Learning, Convolutional Neural Network, Biological networks, Bioengineering

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