Call for papers - Deep phenotyping

Guest Editors

Tudor Groza, PhD, Perth Children’s Hospital, Australia; Institute of Precision Medicine, Singapore; Curtin University, Australia
Patrizia Vizza, PhD, Magna Graecia University of Catanzaro, Italy

Submission Status: Open | Submission Deadline: 14 March 2025

BMC Medical Informatics and Decision Making is calling for submissions to our Collection on Deep phenotyping.

Deep phenotyping represents a comprehensive approach to understand disease phenotypes by integrating detailed data from electronic health records (EHRs), clinical notes, and high-throughput technologies. Recent advancements in artificial intelligence (AI) and machine learning have significantly enhanced our ability to analyze and interpret complex phenotypic data, making it possible to uncover previously hidden patterns and correlations. Techniques such as natural language processing (NLP), deep learning, and network analysis are increasingly being used to extract and structure phenotypic information from unstructured data sources. These developments are particularly exciting as they pave the way for more precise disease classification and personalized treatment strategies.

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Meet the Guest Editors

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Tudor Groza, PhD, Rare Care Centre, Perth Children’s Hospital, Australia; Sing Health Duke-NUS Institute of Precision Medicine, Singapore; School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Australia

Dr Tudor Groza is a seasoned researcher and professional in computational phenotyping for complex and rare diseases and standardization of phenotype nomenclature in genomics and clinical practice, with over fifteen years experience in knowledge representation, information extraction, natural language processing, and machine learning. His work spans various dimensions of the research-clinical care continuum, from devising algorithms to support clinical decision-making in the rare disorders field to standardizing clinical terminology and integration with national public and private health systems. He led and leads several of the global phenotype standardization initiatives as well as founded start-ups that pushed the boundaries of innovation in the field via clinical accreditation of whole genome sequencing or phenotype-driven patient stratification in primary care.

Patrizia Vizza, PhD, Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Italy

Dr Patrizia Vizza received a Master's Degree in Electronic Engineering from the University of Calabria and a PhD in Biomedical Engineering and Computer Science from the University of Catanzaro, focusing on algorithms and methods for biomedical data analysis. She is an assistant professor in bioengineering at the University of Catanzaro, working on the study and development of smart technologies in the health fields for the acquisition, modeling, and analysis of data. Her primary scientific topics encompass (i) AI algorithms for information extraction and data/bioimages analysis; (ii) integration and correlation of clinical data with environmental factors to extract information of medical-clinical and epidemiological interest (e.g., decision support for the extraction of quantitative information); (iii) design of devices for the acquisition and processing of biomedical signals; and (iv) analysis of signals and data in health informatics and bioinformatics.

About the Collection

BMC Medical Informatics and Decision Making is calling for submissions to our Collection on Deep phenotyping.

Deep phenotyping represents a comprehensive approach to understand disease phenotypes by integrating detailed data from electronic health records (EHRs), clinical notes, and high-throughput technologies. Recent advancements in artificial intelligence (AI) and machine learning have significantly enhanced our ability to analyze and interpret complex phenotypic data, making it possible to uncover previously hidden patterns and correlations. Techniques such as natural language processing (NLP), deep learning, and network analysis are increasingly being used to extract and structure phenotypic information from unstructured data sources. These developments are particularly exciting as they pave the way for more precise disease classification and personalized treatment strategies.

This collection welcomes studies that focus on developing robust classification systems using multidimensional analysis to identify and understand disease subtypes. Additionally, we invite submissions that facilitate large-scale research and the identification of complex disease patterns by integrating genetic, environmental, and lifestyle factors through multidimensional analysis. The integration of these advanced technologies and methodologies holds great promise for transforming clinical decision-making and patient care, making deep phenotyping an increasingly important area of research.

There are currently no articles in this collection.

Submission Guidelines

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This Collection welcomes submission of original Research Articles. Should you wish to submit a different article type, please read our submission guidelines to confirm that type is accepted by the journal. Articles for this Collection should be submitted via our submission system, Snapp. During the submission process you will be asked whether you are submitting to a Collection, please select ''Deep phenotyping'' from the dropdown menu.

Articles will undergo the journal’s standard peer-review process and are subject to all of the journal’s standard policies. Articles will be added to the Collection as they are published.

The Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Editors have competing interests is handled by another Editorial Board Member who has no competing interests.