Special Issue "Trends in Precision Oncology from Data Science"

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Molecular Medicine".

Deadline for manuscript submissions: closed (31 January 2019).

Special Issue Editor

Dr. Enrico Capobianco
E-Mail Website
Guest Editor
Center for Computational Science, University of Miami, Coral Gables, FL, USA
Interests: complexity in biomedicine; big data in health; systems medicine; precision medicine; translational medicine; cancer networks; comorbidity; theranostics; digital biomarkers; computational bio-imaging

Special Issue Information

Dear Colleagues,

The new developments in Precision Oncology depend on a few commonly-accepted factors:

  1. Access to Big Data resources, such as large repositories of molecular profiles, genetic information, electronic health records;
  2. Annotated bio-banks with biological samples associated to clinical information;
  3. Drug banks designed to perform computational repositioning and enabling targeting by proteins, peptides, small molecules, monoclonal antibodies;
  4. Bio-imaging repositories

All such domains present complexities that need to be addressed by the next generation of computational methods and developments appearing from the emerging Data Science field.

This call is specifically addressing these needs, and invites contributions targeted to the design, implementation, application and utilizations of technologies and methodologies relevant to Precision Oncology. Prevention, diagnostic, therapeutic focus will be given equal consideration.

The submission deadline for abstract is 28 February 2018, while for full manuscript is 30 June 2018.

Dr. Enrico Capobianco
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Clinical Medicine is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Precision Medicine
  • Big Data
  • Machine Learning
  • Bioinformatics
  • Analytics
  • Digital Health

Published Papers (3 papers)

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Research

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Open AccessArticle
Next Generation Networks: Featuring the Potential Role of Emerging Applications in Translational Oncology
J. Clin. Med. 2019, 8(5), 664; https://doi.org/10.3390/jcm8050664 - 11 May 2019
Abstract
Nowadays, networks are pervasively used as examples of models suitable to mathematically represent and visualize the complexity of systems associated with many diseases, including cancer. In the cancer context, the concept of network entropy has guided many studies focused on comparing equilibrium to [...] Read more.
Nowadays, networks are pervasively used as examples of models suitable to mathematically represent and visualize the complexity of systems associated with many diseases, including cancer. In the cancer context, the concept of network entropy has guided many studies focused on comparing equilibrium to disequilibrium (i.e., perturbed) conditions. Since these conditions reflect both structural and dynamic properties of network interaction maps, the derived topological characterizations offer precious support to conduct cancer inference. Recent innovative directions have emerged in network medicine addressing especially experimental omics approaches integrated with a variety of other data, from molecular to clinical and also electronic records, bioimaging etc. This work considers a few theoretically relevant concepts likely to impact the future of applications in personalized/precision/translational oncology. The focus goes to specific properties of networks that are still not commonly utilized or studied in the oncological domain, and they are: controllability, synchronization and symmetry. The examples here provided take inspiration from the consideration of metastatic processes, especially their progression through stages and their hallmark characteristics. Casting these processes into computational frameworks and identifying network states with specific modular configurations may be extremely useful to interpret or even understand dysregulation patterns underlying cancer, and associated events (onset, progression) and disease phenotypes. Full article
(This article belongs to the Special Issue Trends in Precision Oncology from Data Science)
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Review

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Open AccessReview
The Challenges of Diagnostic Imaging in the Era of Big Data
J. Clin. Med. 2019, 8(3), 316; https://doi.org/10.3390/jcm8030316 - 06 Mar 2019
Cited by 2
Abstract
The diagnostic imaging field has undergone considerable growth both in terms of technological development and market expansion; with the following increasing production of a considerable amount of data that potentially fully poses diagnostic imaging in the Big data in the context of healthcare. [...] Read more.
The diagnostic imaging field has undergone considerable growth both in terms of technological development and market expansion; with the following increasing production of a considerable amount of data that potentially fully poses diagnostic imaging in the Big data in the context of healthcare. Nevertheless, the mere production of a large amount of data does not automatically permit the real exploitation of their intrinsic value. Therefore, it is necessary to develop digital platforms and applications that favor the correct and advantageous management of diagnostic images such as Big data. This work aims to frame the role of diagnostic imaging in this new scenario, emphasizing the open challenges in exploiting such intense data generation for decision making with Big data analytics. Full article
(This article belongs to the Special Issue Trends in Precision Oncology from Data Science)
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Open AccessReview
Moonlighting with WDR5: A Cellular Multitasker
J. Clin. Med. 2018, 7(2), 21; https://doi.org/10.3390/jcm7020021 - 30 Jan 2018
Cited by 8
Abstract
WDR5 is a highly conserved WD40 repeat-containing protein that is essential for proper regulation of multiple cellular processes. WDR5 is best characterized as a core scaffolding component of histone methyltransferase complexes, but emerging evidence demonstrates that it does much more, ranging from expanded [...] Read more.
WDR5 is a highly conserved WD40 repeat-containing protein that is essential for proper regulation of multiple cellular processes. WDR5 is best characterized as a core scaffolding component of histone methyltransferase complexes, but emerging evidence demonstrates that it does much more, ranging from expanded functions in the nucleus through to controlling the integrity of cell division. The purpose of this review is to describe the current molecular understandings of WDR5, discuss how it participates in diverse cellular processes, and highlight drug discovery efforts around WDR5 that may form the basis of new anti-cancer therapies. Full article
(This article belongs to the Special Issue Trends in Precision Oncology from Data Science)
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