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Entropy
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Bio-Neuro Informatics Models and Algorithms
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Bio-Neuro Informatics Models and Algorithms

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Complexity".

Deadline for manuscript submissions: closed (20 December 2023) | Viewed by 6389

Special Issue Editors

Prof. Dr. Saikat Gochhait

E-Mail Website
Guest Editor
Grade II, Symbiosis Institute of Digital and Telecom Management (Constituents of Symbiosis International—Deemed University), Pune 412115, India
Interests: computer science; healthcare; marketing; entrepreneurship
Prof. Dr. Victor B. Kazantsev

E-Mail Website
Guest Editor
1. Neuroscience and Cognitive Technology Laboratory, Innopolis University, 420500 Innopolis, Russia
2. Institute of Biology and Biomedicine, Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia
Interests: computational neuroscience; spiking neuron networks; neurointerfaces; neurocontrol; biomorphic robotics; nonlinear dynamics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue targets state-of-the-art, as well as emerging, areas pertaining to bio-neuro informatics, biotechnology, technology in healthcare, technological innovation, emerging technologies in ICT, engineering, and medical sciences.

The objective of this Special Issue is to endow opportunities for academicians, scientists, and research scholars, as well as professionals, decision makers, industrial practitioners, and students, to interact and exchange ideas, experiences, and expertise in the recent trending areas in the fields of bio-neuro informatics, healthcare, engineering, and medical sciences.

We encourage the authors addressing the latest problems, advances, and diversity within the bio-neuro informatics scientific fields, within the scope of the journal Entropy, to submit an original research paper to be considered for publication in this Special Issue. 

Prof. Dr. Saikat Gochhait
Prof. Dr. Victor B. Kazantsev
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. Entropy 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 2600 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.

Keywords

  • Bioinformatics and Data Mining of Biological Data
  • Biomedical Informatics
  • Technology in Healthcare
  • Technological Innovation

Published Papers (2 papers)

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Research

26 pages, 14953 KiB  
Article
Effective Approaches to Fetal Brain Segmentation in MRI and Gestational Age Estimation by Utilizing a Multiview Deep Inception Residual Network and Radiomics
by Moona Mazher, Abdul Qayyum, Domenec Puig and Mohamed Abdel-Nasser
Entropy 2022, 24(12), 1708; https://doi.org/10.3390/e24121708 - 23 Nov 2022
Cited by 2 | Viewed by 1783
Abstract
To completely comprehend neurodevelopment in healthy and congenitally abnormal fetuses, quantitative analysis of the human fetal brain is essential. This analysis requires the use of automatic multi-tissue fetal brain segmentation techniques. This paper proposes an end-to-end automatic yet effective method for a multi-tissue [...] Read more.
To completely comprehend neurodevelopment in healthy and congenitally abnormal fetuses, quantitative analysis of the human fetal brain is essential. This analysis requires the use of automatic multi-tissue fetal brain segmentation techniques. This paper proposes an end-to-end automatic yet effective method for a multi-tissue fetal brain segmentation model called IRMMNET. It includes a inception residual encoder block (EB) and a dense spatial attention (DSAM) block, which facilitate the extraction of multi-scale fetal-brain-tissue-relevant information from multi-view MRI images, enhance the feature reuse, and substantially reduce the number of parameters of the segmentation model. Additionally, we propose three methods for predicting gestational age (GA)—GA prediction by using a 3D autoencoder, GA prediction using radiomics features, and GA prediction using the IRMMNET segmentation model’s encoder. Our experiments were performed on a dataset of 80 pathological and non-pathological magnetic resonance fetal brain volume reconstructions across a range of gestational ages (20 to 33 weeks) that were manually segmented into seven different tissue categories. The results showed that the proposed fetal brain segmentation model achieved a Dice score of 0.791±0.18, outperforming the state-of-the-art methods. The radiomics-based GA prediction methods achieved the best results (RMSE: 1.42). We also demonstrated the generalization capabilities of the proposed methods for tasks such as head and neck tumor segmentation and the prediction of patients’ survival days. Full article
(This article belongs to the Special Issue Bio-Neuro Informatics Models and Algorithms)
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8 pages, 297 KiB  
Article
On the Inherent Instability of Biocognition: Toward New Probability Models and Statistical Tools
by Rodrick Wallace, Irina Leonova and Saikat Gochhait
Entropy 2022, 24(8), 1070; https://doi.org/10.3390/e24081070 - 03 Aug 2022
Cited by 1 | Viewed by 1271
Abstract
A central conundrum enshrouds biocognition: almost all such phenomena are inherently unstable and must be constantly controlled by external regulatory machinery to ensure proper function, in much the same sense that blood pressure and the ‘stream of consciousness’ require persistent delicate regulation for [...] Read more.
A central conundrum enshrouds biocognition: almost all such phenomena are inherently unstable and must be constantly controlled by external regulatory machinery to ensure proper function, in much the same sense that blood pressure and the ‘stream of consciousness’ require persistent delicate regulation for the survival of higher organisms. Here, we derive the Data Rate Theorem of control theory that characterizes such instability via the Rate Distortion Theorem of information theory for adiabatically stationary nonergodic systems. We then outline a novel approach to building new statistical tools for data analysis based on those theorems, focusing on groupoid symmetry-breaking phase transitions characterized by Fisher Zero analogs. Full article
(This article belongs to the Special Issue Bio-Neuro Informatics Models and Algorithms)
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