Feature Papers in Computational Biology

A special issue of BioTech (ISSN 2673-6284). This special issue belongs to the section "Computational Biology".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 1678

Special Issue Editors


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Guest Editor
1. CSO and Co-Founder, Qnapsyn Biosciences, Inc., 2933 Lankford Dr., Lawrence, KS 66046, USA
2. Courtesy Professor, Department of Food, Nutrition, Dietetics & Health, Kansas State University, Manhattan, KS 66506, USA
Interests: neuroscience; microbiology; informatics; molecular modeling; structural biology; pharmacology; toxicology

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Guest Editor
Institute of Biotechnology, University of Helsinki, Biocentre 3, P.O. Box 65, Viikinkaari 1, 00014 Helsinki, Finland
Interests: genomics and evolution; biology of mobile elements; applications as markers for biodiversity and breeding; identification of mobile elements; bioinformatics (string searching and complexity analysis, search of repeats, DNA alignment and assembly, PCR primer/probe design)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to publish high-quality articles covering topics related to the latest research and developments in the wide-ranging realm of computational biology. Potential topics include, but are not limited to, the following:

  • Bioinformatics.
  • Machine learning and artificial intelligence.
  • Data analysis and information science.
  • Genomic research data analysis.
  • Mathematical modeling.
  • Computational simulation.
  • Systems biology.
  • Medical data.
  • Biomedical and health informatics.
  • Gene sequencing.
  • Cell populations, phenotypes, and lineage diversity.
  • Computational proteomics.

You are welcome to send short proposals for submissions of feature papers to our Editorial Office (biotech@mdpi.com). They will be evaluated by editors first, and the selected papers will be thoroughly as well as rigorously peer reviewed.

Dr. Gerald Lushington
Prof. Dr. Ruslan Kalendar
Guest Editors

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 submissions that pass pre-check are 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. BioTech is an international peer-reviewed open access quarterly 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 1600 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.

Published Papers (1 paper)

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Research

14 pages, 1409 KiB  
Article
Tracking of Systemic Lupus Erythematosus (SLE) Longitudinally Using Biosensor and Patient-Reported Data: A Report on the Fully Decentralized Mobile Study to Measure and Predict Lupus Disease Activity Using Digital Signals—The OASIS Study
by Eldon R. Jupe, Gerald H. Lushington, Mohan Purushothaman, Fabricio Pautasso, Georg Armstrong, Arif Sorathia, Jessica Crawley, Vijay R. Nadipelli, Bernard Rubin, Ryan Newhardt, Melissa E. Munroe and Brett Adelman
BioTech 2023, 12(4), 62; https://doi.org/10.3390/biotech12040062 - 09 Nov 2023
Cited by 1 | Viewed by 1122
Abstract
(1) Objective: Systemic lupus erythematosus (SLE) is a complex disease involving immune dysregulation, episodic flares, and poor quality of life (QOL). For a decentralized digital study of SLE patients, machine learning was used to assess patient-reported outcomes (PROs), QOL, and biometric data for [...] Read more.
(1) Objective: Systemic lupus erythematosus (SLE) is a complex disease involving immune dysregulation, episodic flares, and poor quality of life (QOL). For a decentralized digital study of SLE patients, machine learning was used to assess patient-reported outcomes (PROs), QOL, and biometric data for predicting possible disease flares. (2) Methods: Participants were recruited from the LupusCorner online community. Adults self-reporting an SLE diagnosis were consented and given a mobile application to record patient profile (PP), PRO, and QOL metrics, and enlisted participants received smartwatches for digital biometric monitoring. The resulting data were profiled using feature selection and classification algorithms. (3) Results: 550 participants completed digital surveys, 144 (26%) agreed to wear smartwatches, and medical records (MRs) were obtained for 68. Mining of PP, PRO, QOL, and biometric data yielded a 26-feature model for classifying participants according to MR-identified disease flare risk. ROC curves significantly distinguished true from false positives (ten-fold cross-validation: p < 0.00023; five-fold: p < 0.00022). A 25-feature Bayesian model enabled time-variant prediction of participant-reported possible flares (P(true) > 0.85, p < 0.001; P(nonflare) > 0.83, p < 0.0001). (4) Conclusions: Regular profiling of patient well-being and biometric activity may support proactive screening for circumstances warranting clinical assessment. Full article
(This article belongs to the Special Issue Feature Papers in Computational Biology)
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Planned Papers

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: Exploring Potential of Natural products as a FoxO1 inhibitor: An in-silico approach
Author: Raorane
Highlights: Docking, in-silico, ADMET screening, FoxO1, MM-GBSA, Molecular Dynamics

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