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Special Issue "Big Data in Biology, Life Sciences and Healthcare"
Deadline for manuscript submissions: 10 November 2019.
Massive quantities of data are being generated in biology, the life sciences and healthcare industries and institutions, which hold the promise of advancing our understandings of various biological systems and diseases, developing new biocatalysts and drugs, as well as delivering more effective patient care and reducing costs, etc. In this Special Issue, we seek research and case studies that demonstrate the application of big data modeling and analysis to support scientific research, drug development, clinical decision making, personalized medicine, and other critical tasks. Example topics include (but are not limited to) the following topics relating big data to biology, the life sciences or healthcare:
- Novel systems engineering approaches, including modeling and numerical analysis algorithms
- New statistical tools and algorithms
- Machine learning and artificial intelligence
- Integration of systems engineering approaches with machine learning
- Novel visualization approaches
- Computer or model-aided diagnostics
- Model-based drug development
- Evidence-based medicine
- Modeling and analysis of data from a multitude of sources
- Application of wearable devices
- Public health surveillance
Prof. Peter He
Prof. Jin Wang
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. Processes 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 1200 CHF (Swiss Francs). Please note that for papers submitted after 31 December 2019 an APC of 1400 CHF applies. 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.
- big data
- omics data
- electronic health record
- statistical analysis
- machine learning
- artificial intelligence
- systems biology
- drug development
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Construction process and application of Chinese medical Health knowledge Map based on deep learning
Abstract: Hospital electronic medical records as the core clinical data, it records the patient's disease, diagnosis and treatment information. Mining such data can assist doctors in clinical research and clinical diagnosis and treatment. In this paper, we first proposed one construction process of clinical specialty database based on Chinese knowledge map, and put forward the solution to each task in this process. Secondly, we carry out two aspects clinical medical applications, include clinical curative effect analysis and disease prediction based on knowledge map, and gave the experimental results.
Title: Detection of drivers’ anxiety invoked by driving situations using multimodal bio-signals
Author: Dr. Sung-Phil
Abstract: It is important to monitor drivers’ negative emotions during driving to prevent accidents. Despite drivers’ anxiety being critical for safe driving, there lack systematic approaches to detect anxiety in driving situations. This study employed multimodal bio-signals, including photoplethysmography (PPG), electrodermal activity (EDA), pupil and electroencephalography (EEG), to infer anxiety at various driving situations. Thirty-one drivers with at least one year of driving experience watched a set of thirty black box videos including anxiety-invoking events and another set of thirty videos without them, while their bio-signals were measured. Then, they self-reported anxiety-invoked point in each video, from which features of each bio-signal were extracted. Support vector machine (SVM) classified single bio-signals to detect anxiety. Furthermore, in the order of PPG, EDA, pupil, and EEG (easiest to hardest accessibility), SVM classified accumulated multimodal signals. Classification using EEG showed the highest accuracy of 83.97%, followed by 62% and 63% using EDA and pupil size respectively. PPG yielded classification accuracy similar to the chance level (56%). Classifications using multiple bio-signals all reached less than 60% due to the impact of PPG. This study exhibited the feasibility of utilizing multi-modal bio-signals to detect anxiety invoked by driving situations, demonstrating benefits of EEG over other signals.