Emerging Trends in Computational Biology,
Bioinformatics, and Systems Biology

Publisher: Elsevier (MK imprints)

Paper Submission Deadline: November 1, 2014

Science Citation Indexing:
Subject to relevant Elsevier indexing products which includes,
Medline, Scopus, EMBASE, BIOSIS, Biological Abstracts and others.

You are invited to submit a paper/chapter; see below for submission
instructions. All accepted papers will be published in a research
book entitled “Emerging Trends in Computational Biology, Bioinformatics,
and Systems Biology”. This book will be published by Elsevier (in book
series, “Emerging Trends in Computer Science and Applied Computing”.)
The book will be subject to relevant Elsevier science citation indexing


Computational Biology is the science of using biological data to develop
algorithms and relations among various biological systems. It involves the
application of data-analytical and algorithms, mathematical modeling and
simulation techniques to the study of biological systems. The field is
multidisciplinary in that it includes topics that are traditionally
covered in computer science, mathematics, imaging science, statistics,
chemistry, biophysics, genetics, genomics, ecology, evolution, anatomy,
neuroscience, and visualization where computer science acts as the topical
bridge between all such diverse areas (for a formal definition of
Computational Biology, refer to the Computational_biology wiki page).
Many consider the area of Bioinformatics to be a sub-field of Computational
Biology which includes methods for storing, retrieving, organizing and
analyzing biological data. The area of Systems Biology is an emerging
methodology applied to biomedical and biological scientific research.
It is an area that overlaps with computational biology and bioinformatics.
In addition, the area of Big Data and Data Analytics has become an
important topic in Computational Biology. This edited book will cover the
emerging trends in all aspects of Computational Biology, Bioinformatics,
and Systems Biology. The topics of interest appear below (but we are not
limited to only these topics – therefore, the list below should be
considered to be only a partial list):

Computational biology involves the development and application of
data-analytical and theoretical methods, mathematical modeling and
computational simulation techniques to the study of biological and
behavioral systems. Topics of interest include (but not limited to):

- Computational Biology and Applications in Cancer Research
- Software Tools and Methods for Computational Biology
- Application of Computational Intelligence and Drug Design
- High-performance Computing and Applications in Biology
- Parallel Algorithms and Methods
- Scalable Systems and Algorithms
- Statistical Methods in Biology
- Cloud and Grid Computing for Biological Systems
- Simulation Methods

Bioinformatics is an interdisciplinary scientific field that is
concerned with methods for storing, retrieving, organizing and
analyzing biological data. The field is also concerned with software
tools to generate useful biological knowledge. Many of the methods
in bioinformatics have foundations in mathematics and statistics.
Topics of interest include (but not limited to):

- General Principles of DNA/RNA Structure and Stability
- Sequence Alignment Methods
- Software for Bioinformatics and Applications
- Algorithms and Models
- Phylogenetic Inference and Evolutionary Relationships
- Structure Prediction
- Genome Sequencing and Mapping
- Genome Reconstruction and Assembly
- Comparative Genomics
- Functional Genomics and Reverse Genetics
- Gene Expression Analysis, Clustering, Motif Discovery
- Genome Annotation: Gene Finding, Gene Indices
- Genetic Regulation and Genome Diversity and Structure
- The Human Genome Project
- Genome Databases
- Proteomics and Metabolomics
- Protein Structure and Modeling and Classification
- Microarray Analysis
- Medical Applications

Systems biology provides an ability to obtain, integrate and analyze
complex data sets from multiple experimental sources using
interdisciplinary tools. This is an evolving field and there is not
yet an agreed-upon definition for it. Topics of interest include
(but not limited to):

- Phenomics: Organismal variation in phenotype as it changes during
its life span
- Genomics: Organismal deoxyribonucleic acid (DNA) sequence, including
intra-organismal cell specific variation
- Epigenomics / Epigenetics: Organismal and corresponding cell specific
transcriptome regulating factors not empirically coded in the
genomic sequence
- Transcriptome: Organismal, tissue or whole cell gene expression
measurements by DNA microarrays or serial analysis of gene expression
- Interferomics: Organismal, tissue, or cell level transcript
factors (i.e. RNA interference)
- Translatomics / Proteomics: Organismal, tissue, or cell level
measurements of proteins and peptides via 2D gel electrophoresis,
spectrometry or multi-dimensional protein identification techniques
- Metabolomics: Organismal, tissue, or cell level measurements of all
small-molecules known as metabolites
- Glycomics: Organismal, tissue, or cell level measurements of
- Lipidomics: Organismal, tissue, or cell level measurements of lipids
- Probability Modeling and Statistical Inference in Periodic Cancer
- Cell cycle and checkpoint control
- Systems biology and vaccination research

Big Data is the collection of data sets so large and complex that it
becomes difficult to process using conventional database management
systems or traditional data processing applications. With the advent of
high-throughput genomics, life scientists are starting to demand
massive data sets, encountering challenges with handling, processing
and moving information that were once the domain of other disciplines.
Topics of interest include (but not limited to):

- Algorithms for Big Data and applications in Biology: Data and
Information Fusion, Genetic Algorithms, Machine Learning, Scalable
Algorithms, Simulation and Modeling, Dimensionality Reduction
Multidimensional Big Data
- Biological Big Data Fundamentals: Novel Computational Methodologies,
Algorithms for Enhancing Data Quality, Graph Algorithms and Big Data,
Computational Intelligence
- Infrastructures for Big Data and Bioinformatics: Cloud and Grid Based
Infrastructures, High Performance Computing, Parallel & Distributed
Processing, Software and Tools for Big Data, Emerging Architectural
Frameworks for Big Data
- Big Data Management: Database and Web Applications, Distributed
Database Systems, Distributed File Systems, Data Fusion and
Data Management Methods
- Big Data Search & Mining Methods: Data Mining, Information Mining,
Scalable Search Architectures, Cleaning Big Data,, Visualization
for Search, Time Series Analysis, Graph Mining and Other Similar


Prospective authors are invited to submit their papers by
uploading them to:

Submissions must be received by no later than November 1, 2014 and
must be in either, MS doc or pdf format. Although we do not have a
specific page limit, we anticipate that a typical paper would be
about 10 pages long. At this point, all reasonable typesetting
formats are acceptable; however, authors of accepted papers will
later be asked to follow a particular typesetting format/instructions
to prepare their papers for publication/press.

Papers must not have been previously published or currently submitted
for publication elsewhere. However, we will consider and encourage
submissions that are the EXTENDED versions of already published
conference papers. The first page of the paper should include: title
of the paper, authors’ names, their affiliations, city and country.
The first page should also identify the name of the Contact Author and
a maximum of 5 topical keywords that would best represent the content
of the paper. For each author, his/her email address should also
be provided.  904525

Papers will be evaluated for originality, significance, clarity, impact,
and soundness. Each paper will be refereed by at least two experts in
the field. The referees’ evaluations will then be reviewed by the editors
who will recommend a decision.

This invitation is being sent to selected authors of CSCI and BIOCOMP
annual conferences
as well as to authors of other relevant conferences giving such authors
the opportunity to submit the extended versions of their papers for
publication consideration in this book series. This invitation is also
being sent to various listservs whose subscribers may include authors
who wish to submit papers to this book.


In recent years, the same co-editors composed the following books in the
subject matter:

The above books are now among the top 20% most downloaded books based on
the publisher’s record. We are hoping that this new book will be at least
as successful as the above books.


This book’s editorial board is composed of the program committees of:
BIOCOMP 2013 and 2014:
We will seek the help of additional experts as needs arise.

This book solicits papers from all researchers (ie, not only from


Prof. Quoc-Nam Tran, PhD
Professor and Chair,
Department of Computer Science, The University of South Dakota,
South Dakota, USA

Prof. Hamid R. Arabnia, PhD
Professor of Computer Science and Fellow of Int’l Society of
Biological Medicine (ISIBM);
Editor, Emerging Trends in Computer Science and Applied Computing
The University of Georgia, Department of Computer Science, Georgia, USA


Paper Submission (about 10 pages):         November 1, 2014
Notification of acceptance:                About 3 weeks after a paper
is submitted.
However, we intend to send all
notifications by November
21, 2014.
Submission of final paper for publication: December 8, 2014
Signed Copyright/Consent form:             December 8, 2014
Publication Date of the book:              January 2015 (or soon
Publisher:                                 Elsevier (MK imprints)
Science Citation Indexing:
Subject to relevant Elsevier indexing products which includes,
Medline, Scopus, EMBASE, BIOSIS, Biological Abstracts and others.
Title of book:
Emerging Trends in Computational Biology, Bioinformatics, and Systems


Inquiries should be sent to

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