Special Issue "Machine Learning for Biomedical Data Analysis"
Deadline for manuscript submissions: 15 July 2019
Nowadays, more and more data is being produced. This data comes from everywhere: sensors used to gather climate information, posts to social media sites, digital pictures and videos, purchase transaction records, and cell phone GPS signals, to name a few. This data is big data . This tendency also affects biology and medicine, where new techniques, e.g., next generation sequencing, allow us to produce more data than ever. Moreover, the data generated may be of a different nature: text, images, gene expression, signals, etc. In addition to this, typically such data present a high presence of noise. It follows that there is a clear need to analyze and extract useful information from such data. In this context, machine learning (ML) techniques provide the ability to analyse this data and extract relevant information from it, or even make predictions about it.
The overall aim of this Special Issue is to compile the latest research and development, up-to-date issues, and challenges in the field of ML and its applications in bioinformatics and medical applications.
Possible topics of interest include, but are not limited to:
- Medical imaging, signal processing and text analysis
- Data mining medical data and records
- Clinical expert systems
- Modelling and simulation of biomedical processes
- Drug description analysis
- Patient-centric care
- Medical prognosis based on machine learning approaches
- Interpreting genomic or metagenomic data
- Discovering regulatory or expression pathways
- Rational drug design and personalized medicine
- Modeling ecosystems or population dynamics
- Discovering genome–disease or genome–phenotype associations
- Biomedical text/data mining and visualization
- Network biology/medicine
- Omics data analysis and functional genomics for complex diseases
- Gene–gene interactions and gene–environment interactions for disease association analysis
- Protein structure prediction
- Assembling next generation sequence data
Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere.
Dr. Federico Divina
Dr. Francisco A. Gómez-Vela
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 1500 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.