Special Issue on Statistical and Machine Learning Modeling in Computational Epigenetics
11 October 2017

Epigenetics has recently emerged as one of the hottest elds 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-speci c 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 eld are computational tools that, by analyzing complex genomic information, play an essential role in discovering evidences to de ne new assessable hypotheses. In particular, the literature at a glance shows the e ectiveness of a combination of statistical and machine learning techniques in several epigenetic analyses. is special issue aims to host original papers and reviews on recent research advances and the state-of- the-art methods in the elds of statistical and machine learning methodologies and algorithm design for the study of epigenetic mechanisms. Especially welcome are also so ware 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 modi cations in DNA sequences
Identi cation of abnormal DNA methylation within CpG islands in di erent 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/.

Papers are published upon acceptance, regardless of the Special Issue publication date.


Lead Guest Editor

Antonino Staiano, Università di Napoli Parthenope, Napoli, Italy antonino.staiano@uniparthenope.it

Guest Editors

Angelo Ciaramella, Università di Napoli Parthenope, Napoli, Italy angelo.ciaramella@uniparthenope.it

Antonio Eleuteri, University of Liverpool, Liverpool, UK antonio.eleuteri@liverpool.ac.uk

Lehoang Son, Vietnam National University, Hanoi, Vietnam sonlh@vnu.edu.vn

Umberto F. Petrillo, Università di Roma “La Sapienza”, Rome, Italy umberto.ferraro@uniroma1.it

Submission Deadline

Friday, 2 March 2018

Publication Date

July 2018