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Symmetry
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Symmetry and Asymmetry in Computational Biology and Bioinformatics
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Symmetry and Asymmetry in Computational Biology and Bioinformatics

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: closed (30 June 2019) | Viewed by 14896

Special Issue Editor

Dr. Atif Ali Khan

E-Mail Website
Guest Editor
Department of Medicine, Section of Computational Biomedicine and Biomedical Data Science, University of Chicago, Chicago, IL, USA
Interests: computational biology; clinical data science; biomedical and health informatics; applied artificial intelligence; data mining; epidemiology

Special Issue Information

Dear Colleagues,

The volume of biomedical data (both structured and unstructured) has risen exponentially in the last decade. Despite the rapid increase in the collection and analysis of data, the biomedical and healthcare research communities are only beginning to capitalize on the transformative opportunities that these data provide. As large and complex data sets are becoming increasingly available to the research community, more advanced, sophisticated, and automated analytical techniques are required to investigate such gigantic datasets. There is a growing need to design the cutting-edge methods for storing, processing, and interpreting these complex datasets to infer novel symmetric and asymmetric patterns. In this context, modern machine learning and data mining methods can be used to extract valuable knowledge from a variety of large and heterogeneous textual and tabulated data sources, enhancing the biomedical research and improving the healthcare delivery.

This special issue is particularly interested in the research work that involves novel methods for medical data acquisition, structuring, analyses, knowledge extraction, and innovative application of modern computational techniques that has great potential for biomedical research. Such innovative interdisciplinary applications will allow identification and extraction of relevant symmetric and asymmetric patterns, facilitate more rapid discovery of meaningful information, and will open new avenues of knowledge. Researchers are invited to submit unpublished original work describing the recent advances on all aspects of bioinformatics and computational biology, and including but not limited to the following topics:

  • Machine learning and data mining in bioinformatics and computational biology
  • Next-generation sequencing data analysis and applications
  • Clinical and translational bioinformatics
  • Biomarker and drug discovery
  • Gene set enrichment analysis
  • Analysis and visualization of complex biomedical data
  • Analysis of electronic health records and health informatics
  • Medical diagnostic and decision support system
  • Biomedical text mining and natural language processing
  • Biological network analysis and systems biology
  • Gene-disease relationship mining
  • Review or Meta-data analysis related to biology and medicine

All submissions will be peer reviewed by the domain experts and will be evaluated based on their relevance to this special issue, novel contribution, significance of work, and the overall quality.

Dr. Atif Ali Khan
Guest Editor

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. Symmetry 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 (4 papers)

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Research

14 pages, 1272 KiB  
Article
A Novel Dissimilarity of Activity Biomarker and Functional Connectivity Analysis for the Epilepsy Diagnosis
by Abdul Basit, Saqib Ali Khan, Waqas Tariq Toor, Naeem Maroof, Muhammad Saadi and Atif Ali Khan
Symmetry 2019, 11(8), 979; https://doi.org/10.3390/sym11080979 - 02 Aug 2019
Cited by 3 | Viewed by 2421
Abstract
Epilepsy is a central nervous system disorder that results in asymmetries of brain regional activation and connectivity patterns. The detection of these abnormalities is oftentimes challenging and requires identification of robust bio-markers that are representative of disease activity. Functional Magnetic Resonance Imaging (fMRI) [...] Read more.
Epilepsy is a central nervous system disorder that results in asymmetries of brain regional activation and connectivity patterns. The detection of these abnormalities is oftentimes challenging and requires identification of robust bio-markers that are representative of disease activity. Functional Magnetic Resonance Imaging (fMRI) is one of the several methods that can be used to detect such bio-markers. fMRI has a high spatial resolution which makes it a suitable candidate for designing computational methods for computer-aided biomarker discovery. In this paper, we present a computational framework for analyzing fMRI data consisting of 100 epileptic and 80 healthy patients, with an overall goal to produce a novel bio-marker that is predictive of epilepsy. The proposed method is primarily based on Dissimilarity of Activity (DoA) analysis. We demonstrate that the bio-marker presented in this study can be used to capture asymmetries in activities by detecting any abnormalities in Blood Oxygenated Level Dependent (BOLD) signal. In order to represent all asymmetries (of connectivity and activation patterns), we used functional connectivity analysis (FCA) in conjunction with DoA to find underlying connectivity patterns of the regions. Subsequently, these biomarkers were used to train a Support Vector Machine (SVM) classifier that was able to distinguish between healthy and epileptic patients with 87.8% accuracy. These results demonstrate the applicability of computer-aided methods in complex disease diagnosis by simply utilizing the existing data. With the advent of all modern sensing and imaging techniques, the use of intelligent algorithms and advanced computational methods are increasingly becoming the future of computer-aided diagnosis. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in Computational Biology and Bioinformatics)
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23 pages, 7210 KiB  
Article
Multilevel and Multiscale Deep Neural Network for Retinal Blood Vessel Segmentation
by Pearl Mary Samuel and Thanikaiselvan Veeramalai
Symmetry 2019, 11(7), 946; https://doi.org/10.3390/sym11070946 - 22 Jul 2019
Cited by 40 | Viewed by 5488
Abstract
Retinal blood vessel segmentation influences a lot of blood vessel-related disorders such as diabetic retinopathy, hypertension, cardiovascular and cerebrovascular disorders, etc. It is found that vessel segmentation using a convolutional neural network (CNN) showed increased accuracy in feature extraction and vessel segmentation compared [...] Read more.
Retinal blood vessel segmentation influences a lot of blood vessel-related disorders such as diabetic retinopathy, hypertension, cardiovascular and cerebrovascular disorders, etc. It is found that vessel segmentation using a convolutional neural network (CNN) showed increased accuracy in feature extraction and vessel segmentation compared to the classical segmentation algorithms. CNN does not need any artificial handcrafted features to train the network. In the proposed deep neural network (DNN), a better pre-processing technique and multilevel/multiscale deep supervision (DS) layers are being incorporated for proper segmentation of retinal blood vessels. From the first four layers of the VGG-16 model, multilevel/multiscale deep supervision layers are formed by convolving vessel-specific Gaussian convolutions with two different scale initializations. These layers output the activation maps that are capable to learn vessel-specific features at multiple scales, levels, and depth. Furthermore, the receptive field of these maps is increased to obtain the symmetric feature maps that provide the refined blood vessel probability map. This map is completely free from the optic disc, boundaries, and non-vessel background. The segmented results are tested on Digital Retinal Images for Vessel Extraction (DRIVE), STructured Analysis of the Retina (STARE), High-Resolution Fundus (HRF), and real-world retinal datasets to evaluate its performance. This proposed model achieves better sensitivity values of 0.8282, 0.8979 and 0.8655 in DRIVE, STARE and HRF datasets with acceptable specificity and accuracy performance metrics. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in Computational Biology and Bioinformatics)
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14 pages, 4109 KiB  
Article
End-to-End Multimodal 16-Day Hatching Eggs Classification
by Lei Geng, Zhen Peng, Zhitao Xiao and Jiangtao Xi
Symmetry 2019, 11(6), 759; https://doi.org/10.3390/sym11060759 - 04 Jun 2019
Cited by 3 | Viewed by 2391
Abstract
Sixteen-day hatching eggs are divided into fertile eggs, waste eggs, and recovered eggs. Because different categories may have the same characteristics, they are difficult to classify. Few existing algorithms can successfully solve this problem. To this end, we propose an end-to-end deep learning [...] Read more.
Sixteen-day hatching eggs are divided into fertile eggs, waste eggs, and recovered eggs. Because different categories may have the same characteristics, they are difficult to classify. Few existing algorithms can successfully solve this problem. To this end, we propose an end-to-end deep learning network structure that uses multiple forms of signals. First, we collect the photoplethysmography (PPG) signal of the hatching eggs to obtain heartbeat information and photograph hatching eggs with a camera to obtain blood vessel pictures. Second, we use two different network structures to process the two kinds of signals: Temporal convolutional networks are used to process heartbeat information, and convolutional neural networks (CNNs) are used to process blood vessel pictures. Then, we combine the two feature maps and use the long short-term memory (LSTM) network to model the context and recognize the type of hatching eggs. The system is then trained with our dataset. The experimental results demonstrate that the proposed end-to-end multimodal deep learning network structure is significantly more accurate than using a single modal network. Additionally, the method successfully solves the 16-day hatching egg classification problem. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in Computational Biology and Bioinformatics)
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16 pages, 2919 KiB  
Article
A Coarse-to-Fine Fully Convolutional Neural Network for Fundus Vessel Segmentation
by Jianwei Lu, Yixuan Xu, Mingle Chen and Ye Luo
Symmetry 2018, 10(11), 607; https://doi.org/10.3390/sym10110607 - 07 Nov 2018
Cited by 31 | Viewed by 4145
Abstract
Fundus vessel analysis is a significant tool for evaluating the development of retinal diseases such as diabetic retinopathy and hypertension in clinical practice. Hence, automatic fundus vessel segmentation is essential and valuable for medical diagnosis in ophthalmopathy and will allow identification and extraction [...] Read more.
Fundus vessel analysis is a significant tool for evaluating the development of retinal diseases such as diabetic retinopathy and hypertension in clinical practice. Hence, automatic fundus vessel segmentation is essential and valuable for medical diagnosis in ophthalmopathy and will allow identification and extraction of relevant symmetric and asymmetric patterns. Further, due to the uniqueness of fundus vessel, it can be applied in the field of biometric identification. In this paper, we remold fundus vessel segmentation as a task of pixel-wise classification task, and propose a novel coarse-to-fine fully convolutional neural network (CF-FCN) to extract vessels from fundus images. Our CF-FCN is aimed at making full use of the original data information and making up for the coarse output of the neural network by harnessing the space relationship between pixels in fundus images. Accompanying with necessary pre-processing and post-processing operations, the efficacy and efficiency of our CF-FCN is corroborated through our experiments on DRIVE, STARE, HRF and CHASE DB1 datasets. It achieves sensitivity of 0.7941, specificity of 0.9870, accuracy of 0.9634 and Area Under Receiver Operating Characteristic Curve (AUC) of 0.9787 on DRIVE datasets, which surpasses the state-of-the-art approaches. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in Computational Biology and Bioinformatics)
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