Connectionists: Special Issue on Statistical and Machine Learning Modeling in Computational Epigenetics

[Antonino Staiano]

Call for papers Special Issue on Statistical and Machine Learning Modeling
in Computational Epigenetics at BioMed Research International

The call is available at https://www.hindawi.com/
journals/bmri/si/873738/cfp/

Special Issue on Statistical and Machine Learning Modeling in Computational
Epigenetics

Epigenetics has recently emerged as one of the hottest fields in life
sciences for studying heritable change in phenotype, gene function, or gene
expression that are not directly encoded in the DNA itself. Up-to-date
studies have shown that epigenetic modulations are fundamental in many
developmental processes, from tissue and organ formation to allele-specific
gene expression. When these normal epigenetic patterns modify, pattern of
gene expression can be deregulated, and it has been proven that such
mechanisms are central in several disorders and diseases, among which are
psychiatric disorders, obesity, and etiology of a number of diseases such
as cancer, schizophrenia, and Alzheimer, just to name a few. Today, thanks
also to several large human epigenome projects, scientists have a better
understanding of the basic principles of epigenetic mechanisms as well as
their relevance to health disorders and disease. At the heart of this
fascinating research field are computational tools that, by analyzing
complex genomic information, play an essential role in discovering
evidences to define new assessable hypotheses. In particular, the
literature at a glance shows the effectiveness of a combination of
statistical and machine learning techniques in several epigenetic analyses.
This special issue aims to host original papers and reviews on recent
research advances and the state-of-the-art methods in the fields of
statistical and machine learning methodologies and algorithm design for the
study of epigenetic mechanisms. Especially welcome are also software
systems with a special emphasis on tools developed with the help of big
data distributed processing framework like Hadoop and Spark to properly
manage the huge amount of data coming from epigenome-scale experiments.

Potential topics include but are not limited to the following:Machine
learning
Statistical learning theory
Fuzzy logic and systems
Neuro-fuzzy systems
Granular computing
Data mining
Probabilistic and statistical modelling
Algorithms designed for epigenomic big data
High-throughput data in the broad context of epigenomics
Analysis, modeling, and prediction of DNA methylation patterns
Analysis, modeling, and prediction of histone modifications in DNA sequences
Identification of abnormal DNA methylation within CpG islands in different
diseases
Analysis of epigenetic marks in stem cells
Analysis of miRNA changes in cancer and other diseases
Simultaneous analysis of methylome and transcriptome
Analysis of reciprocal regulation of noncoding RNA and methylation
Study of the epigenetic role in metabolomics
Analysis of microbiome role in epigenetic regulation of gene expression

Authors can submit their manuscripts through the Manuscript Tracking System
at https://mts.hindawi.com/submit/journals/bmri/computational.biology/acim/

Submission deadline:

Friday, 2 March 2018

Publication date:

July 2018
Antonino Staiano
Department of Science and Technology
University of Naples Parthenope, Naples, Italy
antonino.staiano at uniparthenope.it
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Antonino Staiano, PhD
Assistant Professor
Dept. Science and Technology
University of Naples Parthenope, Italy
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