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Axioms
Special Issues
Bio-Informatics and Data Set Analysis
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Bio-Informatics and Data Set Analysis

A special issue of Axioms (ISSN 2075-1680).

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 2395

Special Issue Editors

Dr. Jingbo Xia

E-Mail Website
Guest Editor
Hubei Key Laboratory of Agricultural Bioinformatics, Department of Big Data Science, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
Interests: BioNLP; data mining; bioinformatics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Tianyong Hao

E-Mail Website
Guest Editor
School of Computer Science, South China Normal University, Guangzhou 510631, China
Interests: medical natural language processing; medical text mining
Prof. Dr. Qingcai Chen

E-Mail Website
Guest Editor
School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China
Interests: clinical NLP; deep learning
Prof. Dr. Hongfei Lin

E-Mail Website
Guest Editor
School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
Interests: natural language processing; sentimental analysis; medical text mining; social media processing

Special Issue Information

Dear Colleagues,

Healthcare and bioinformatics focus on various health and biological data, aiming to curate information and knowledge for the sake of human being health. In the era of big data, a significant amount of data in the healthcare and bioinformatics field have been accumulated in a heterogeneous manner. For example, the healthcare field generates tons of clinical trail and EMR data, while bioinformatics brings in rich sequencing data. In addition, the literature and other text resources provide dozens of millions of text data. To tackle science discovery in healthcare and bioinformatics, information retrieval, data fusion, and data integration are effective means to explore and derive knowledge from heterogeneous data.

This Special Issue of Axioms, entitled “Bioinformatics and Data Set Analysis”, aims to focus on the latest research progress in data science for the sake of knowledge discovery in healthcare and bioinformatics, as well as advanced techniques in information retrieval, knowledge representation and reasoning, heterogeneous data fusion, and so on.

Moreover, this Special Issus is also collaborating with the China Conference on Health Information Processing (CHIP), the annual symposium of the Chinese Information Processing Society of China (CIPS) Technical Committee of Medical, Health and Biological Information Processing. Submissions not from CHIP are also welcomed.

Dr. Jingbo Xia
Prof. Dr. Tianyong Hao
Prof. Dr. Qingcai Chen
Prof. Dr. Hongfei Lin
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. Axioms 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 2400 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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0 pages, 3901 KiB  
Article
RETRACTED: Diabetic Retinopathy Progression Prediction Using a Deep Learning Model
by Hanan A. Hosni Mahmoud
Axioms 2022, 11(11), 614; https://doi.org/10.3390/axioms11110614 - 04 Nov 2022
Cited by 4 | Viewed by 1319 | Retraction
Abstract
Diabetes is an illness that happens with a high level of glucose in the body, and can harm the retina, causing permanent loss vision or diabetic retinopathy. The fundus oculi method comprises detecting the eyes to perform a pathology test. In this research, [...] Read more.
Diabetes is an illness that happens with a high level of glucose in the body, and can harm the retina, causing permanent loss vision or diabetic retinopathy. The fundus oculi method comprises detecting the eyes to perform a pathology test. In this research, we implement a method to predict the progress of diabetic retinopathy. There is a research gap that exists for the detection of diabetic retinopathy progression employing deep learning models. Therefore, in this research, we introduce a recurrent CNN (R-CNN) model to detect upcoming visual field inspections to predict diabetic retinopathy progression. A benchmark dataset of 7000 eyes from healthy and diabetic retinopathy progress cases over the years are utilized in this research. Approximately 80% of ocular cases from the dataset is utilized for the training stage, 10% of cases are used for validation, and 10% are used for testing. Six successive visual field tests are used as input and the seventh test is compared with the output of the R-CNN. The precision of the R-CNN is compared with the regression model and the Hidden Markov (HMM) method. The average prediction precision of the R-CNN is considerably greater than both regression and HMM. In the pointwise classification, R-CNN depicts the least classification mean square error among the compared models in most of the tests. Also, R-CNN is found to be the minimum model affected by the deterioration of reliability and diabetic retinopathy severity. Correctly predicting a progressive visual field test with the R-CNN model can aid physicians in making decisions concerning diabetic retinopathy. Full article
(This article belongs to the Special Issue Bio-Informatics and Data Set Analysis)
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