[BBC] IEEE/ACM SPECIAL ISSUE ANNOUNCEMENT

[PietroHiram Guzzi]

(APOLOGIZE FOR MULTIPLE POSTING)

CALL FOR PAPERS
IEEE Transactions on IEEE/ACM Transactions on Computational Biology
and Bioinformatics
Special Issue/Section on Deep Learning and Graph Embeddings for Network Biology

GUEST EDITORS:
Pietro Hiram Guzzi
Associate Professor, University of Catanzaro, Department of Surgical
and Medical Sciences
hguzzi at unicz.it

Marinka Zitnik
Assistant Professor, Harvard University, Department of Biomedical Informatics
marinka at hms.harvard.edu

TOPIC SUMMARY:
Biological Networks are powerful resources for modelling, analysis,
and discovery in biological systems, ranging from molecular to
epidemiological levels. In recent years, network models and algorithms
have been used to represent and analyse the whole set of associations
and interactions among biologically relevant molecules inside cells,
(e.g., proteins, genes, transcription factors and more recently the
big class of non-coding genes), supporting the elucidation of the
molecular mechanisms as well as the development of precision medicine
for many relevant diseases (e.g., cancers or brain disorders).

Mathematical machinery that is central to this area of research is
graph theory and machine learning on graph-structured data. Recent
research efforts have introduced methods and tools that can model
biological phenomena and learn and reason about them through networks.
Such data and models are typically stored in various databases of
experimental data and repositories of biomedical knowledge. Network
data extracted from these databases are often mined for knowledge
about a biological system of interest (e.g., using network statistics
or community detection algorithms) and to compare two or more networks
(e.g., using network alignment algorithms). Current approaches may
present limitations in some applications since they can fail to
generalize from observed network structure to new biological
phenomena, are unable to include prior knowledge in the analysis, rely
on user-defined heuristics and painstaking manual feature engineering
to extract features from biological networks, or fail to support
researchers when limited biological data is available (e.g., small
datasets with low coverage).

Recent years have seen a surge in approaches, such as deep learning,
that have shown broad utility in uncovering new biology and
contributing to new discoveries in wet laboratory experiments. In
particular, in biological and biomedical area, deep learning have
evidenced an efficient way to deal with data generated from modern
high-throughput technologies.

In parallel, the field of network science has been influenced by the
development of methods that automatically learn to encode network
structure into low-dimensional embeddings, using data transformation
techniques based on matrix factorization, deep learning, nonlinear
dimensionality reduction, and complex non-linear models. The key idea
of these methods (or graph representation learning) is to
automatically learn a function able to map nodes in the graph (or
other graph structures) to points in a compact vector space, whose
geometry is optimized to reflect topology of the input graph. The
relevance and potential of graph representation learning are evidenced
by the rise of approaches that are beginning to effect on the way
network biology is performed today at the fundamental level.
Therefore, there is strong need to discuss and foster these advances
in a systematic way to give support both to researchers and
practitioners.

The goal of this special issue is to collect both surveys and papers
describing novel methods and applications in computational biology and
bioinformatics. Papers presenting applications in medicine and
healthcare are also welcome.

The topics of interest for this special issue include, but are not limited to:
* Deep learning and graph neural networks for network biology
* Learning meaningful representations for biomedical networks
* Learning node, edge, higher-order, and graph-level embeddings for
biological networks
* Next-generation graph embedding techniques for important problems,
including node classification, link prediction, graph classification,
network alignment and beyond
* Graph representation learning for visualizing and interpreting
interaction data
* Next-generation network science through network embeddings
* Relevant benchmark datasets, initial solutions for new challenges
and new directions in network biology
* Applications of network embeddings broadly in computational biology,
genomics, medicine, and health

IMPORTANT DATES:
Abstract submission:  Jan-Feb 2020
Open for submissions in ScholarOne Manuscripts: February 28 2020
Closed for submissions: July 30, 2020
Results of first round of reviews: September 15, 2020
Submission of revised manuscripts: October 15, 2020
Results of second round of reviews: November 15, 2020
Publication materials due: December 15, 2020

SUBMISSION GUIDELINES:
Prospective authors are invited to submit their manuscripts
electronically after the Òopen for submissionsÓ date, adhering to the
IEEE/ACM Transactions in Computational Biology and Bioinformatics
guidelines (http://www.computer.org/portal/web/tcbb-cs/author). Please
submit your papers through the online system
(https://mc.manuscriptcentral.com/tcbb-cs) and be sure to select the
special issue or special section name. Manuscripts should not be
published or currently submitted for publication elsewhere. Please
submit only full papers intended for review, not abstracts, to the
ScholarOne portal. If requested, abstracts should be sent by e-mail to
the Guest Editors directly.

GUEST EDITORS BIOGRAPHIES:

Pietro Hiram Guzzi

Pietro H. GuzziÊis an Associate Professor of Computer Science and
Bioinformatics  at the University ÔMagna Gr¾ciaÕ of Catanzaro, Italy,
since 2008. He received his PhD in Biomedical Engineering in 2008,
from Magna Gr¾cia University of Catanzaro. He received his Laurea
degree in Computer Engineering in 2004 from the University of
Calabria, Rende, Italy. His research interests comprise
bioinformatics, network analysis. In network analysis, in particular,
Pietro has worked on local alignment of biological networks providong
some tools for network alignment. Actually is working on novel
approaches of alignment that merge together both local and global
alignment and on the development of novel methods of analysis based on
the integration of heterogeneous networks thorugh embedding. Pietro is
an ACM member and serves the scientific community as reviewer for many
conferences. He is associate editor of IEEE/ACM TCBB, and of
SIGBioinformatics Record.

Marinka Zitnik

Marinka Zitnik is an Assistant Professor at Harvard University. Her
research investigates artificial intelligence and machine learning to
advance science, medicine, and health. Her methods have had a tangible
impact in biology, genomics, and drug discovery, and are used by major
biomedical institutions, including Baylor College of Medicine,
Karolinska Institute, Stanford Medical School, and Massachusetts
General Hospital. Before Harvard, she was a postdoctoral scholar in
Computer Science at Stanford University and a member of the Chan
Zuckerberg Biohub at Stanford. She received her Ph.D. in Computer
Science from University of Ljubljana while also researching at
Imperial College London, University of Toronto, Baylor College of
Medicine, and Stanford University. Her work received several best
paper, poster, and research awards from the International Society for
Computational Biology. She has recently been named a Rising Star in
EECS by MIT and also a Next Generation in Biomedicine by The Broad
Institute of Harvard and MIT, being the only young scientist who
received such recognition in both EECS and Biomedicine.