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Applied Sciences
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New Applications of Computational Biology and Bioinformatics
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New Applications of Computational Biology and Bioinformatics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 5139

Special Issue Editors

Dr. Arianna Consiglio

E-Mail Website
Guest Editor
Institute for Biomedical Technologies, National Research Council, 70126 Bari, Italy
Interests: bioinformatics; biostatistics; computational intelligence; NGS data analysis
Dr. Flavio Licciulli

E-Mail Website
Guest Editor
Institute for Biomedical Technologies, National Research Council, 70126 Bari, Italy
Interests: bioinformatics; computational biology; ncRNAs functional analysis; biological databases and data-warehouses; big data management and integration; bioinformatics analysis pipelines and WEB interfaces

Special Issue Information

Dear Colleagues,

The unstoppable advancement of biomedical technologies, and, in particular, those related to the production of omics data, has led to a rapid advancement in all areas of biology and medicine and for all kingdoms of life.

At the same time, the need for new tools to support the management and analysis of the data produced also continues to grow and open up new research horizons.

In this context, bioinformatics provides tools and pipelines to facilitate the elaboration of biomedical data, and computational biology helps to improve our understanding and characterization of biological systems.

In this Special Issue, we invite submissions exploring highly innovative applications in the fields of bioinformatics and computational biology.

Potential topics include, but are not limited to, the following:

  • New algorithms and pipelines for biomedical data analyses;
  • Approaches for integrated biomedical data analyses (omics, bioimages, clinical and lifestyle data);
  • New databases and collections of biomedical resources;
  • Innovative applications of artificial intelligence to biomedical issues;
  • High-throughput omics data analyses;
  • Health-care applications.

Dr. Arianna Consiglio
Dr. Flavio Licciulli
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. Applied Sciences is an international peer-reviewed open access semimonthly 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.

Keywords

  • omics data analysis
  • biological data management and integration
  • novel artificial intelligence applications to biological data

Published Papers (3 papers)

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20 pages, 605 KiB  
Article
Experimenting with Extreme Learning Machine for Biomedical Image Classification
by Francesco Mercaldo, Luca Brunese, Fabio Martinelli, Antonella Santone and Mario Cesarelli
Appl. Sci. 2023, 13(14), 8558; https://doi.org/10.3390/app13148558 - 24 Jul 2023
Cited by 1 | Viewed by 1335
Abstract
Currently, deep learning networks, with particular regard to convolutional neural network models, are typically exploited for biomedical image classification. One of the disadvantages of deep learning is that is extremely expensive to train due to complex data models. Extreme learning machine has recently [...] Read more.
Currently, deep learning networks, with particular regard to convolutional neural network models, are typically exploited for biomedical image classification. One of the disadvantages of deep learning is that is extremely expensive to train due to complex data models. Extreme learning machine has recently emerged which, as shown in experimental studies, can produce an acceptable predictive performance in several classification tasks, and at a much lower training cost compared to deep learning networks that are trained by backpropagation. We propose a method devoted to exploring the possibility of considering extreme learning machines for biomedical classification tasks. Binary and multiclass classification in four case studies are considered to demonstrate the effectiveness of extreme learning machine, considering the biomedical images acquired with the dermatoscope and with the blood cell microscope, showing that the extreme learning machine can be successfully applied for biomedical image classification. Full article
(This article belongs to the Special Issue New Applications of Computational Biology and Bioinformatics)
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18 pages, 2145 KiB  
Article
Optimized Feature Learning for Anti-Inflammatory Peptide Prediction Using Parallel Distributed Computing
by Salman Khan, Muhammad Abbas Khan, Mukhtaj Khan, Nadeem Iqbal, Salman A. AlQahtani, Mabrook S. Al-Rakhami and Dost Muhammad Khan
Appl. Sci. 2023, 13(12), 7059; https://doi.org/10.3390/app13127059 - 12 Jun 2023
Cited by 7 | Viewed by 1269
Abstract
With recent advancements in computational biology, high throughput Next-Generation Sequencing (NGS) has become a de facto standard technology for gene expression studies, including DNAs, RNAs, and proteins; however, it generates several millions of sequences in a single run. Moreover, the raw sequencing datasets [...] Read more.
With recent advancements in computational biology, high throughput Next-Generation Sequencing (NGS) has become a de facto standard technology for gene expression studies, including DNAs, RNAs, and proteins; however, it generates several millions of sequences in a single run. Moreover, the raw sequencing datasets are increasing exponentially, doubling in size every 18 months, leading to a big data issue in computational biology. Moreover, inflammatory illnesses and boosting immune function have recently attracted a lot of attention, yet accurate recognition of Anti-Inflammatory Peptides (AIPs) through a biological process is time-consuming as therapeutic agents for inflammatory-related diseases. Similarly, precise classification of these AIPs is challenging for traditional technology and conventional machine learning algorithms. Parallel and distributed computing models and deep neural networks have become major computing platforms for big data analytics now required in computational biology. This study proposes an efficient high-throughput anti-inflammatory peptide predictor based on a parallel deep neural network model. The model performance is extensively evaluated regarding performance measurement parameters such as accuracy, efficiency, scalability, and speedup in sequential and distributed environments. The encoding sequence data were balanced using the SMOTETomek approach, resulting in a high-accuracy performance. The parallel deep neural network demonstrated high speed up and scalability compared to other traditional classification algorithms study’s outcome could promote a parallel-based model for predicting anti-Inflammatory Peptides. Full article
(This article belongs to the Special Issue New Applications of Computational Biology and Bioinformatics)
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12 pages, 3286 KiB  
Article
De Novo Transcriptome Analysis of the Lizard Fish (Saurida elongata): Novel Insights into Genes Related to Sex Differentiation
by Binbin Shan, Liangming Wang, Yan Liu, Changping Yang, Manting Liu, Dianrong Sun and Pujiang Huang
Appl. Sci. 2022, 12(22), 11319; https://doi.org/10.3390/app122211319 - 8 Nov 2022
Viewed by 1298
Abstract
Among vertebrates, teleost fishes exhibit the largest array of sex-determining systems, resulting in many reproductive strategies. Screening these fish for sex-related genes could enhance our understanding of sexual differentiation. The lizardfish, Saurida elongata (Temminck & Schlegel, 1846), is a commercially important marine fish [...] Read more.
Among vertebrates, teleost fishes exhibit the largest array of sex-determining systems, resulting in many reproductive strategies. Screening these fish for sex-related genes could enhance our understanding of sexual differentiation. The lizardfish, Saurida elongata (Temminck & Schlegel, 1846), is a commercially important marine fish in tropical and subtropical seas of the northwest Pacific. However, little genomic information on S. elongata is available. In this study, the transcriptomes of three female and three male S. elongata were sequenced. A total of 49.19 million raw read pairs were generated. After identification and assembly, a total of 59,902 nonredundant unigenes were obtained with an N50 length of 2070 bp. Then, 38,016 unigenes (63.47% of the total) were successfully annotated through multiple public databases. A comparison of the unigenes of different sexes of S. elongata revealed that 22,507 unigenes (10,419 up-regulated in a female and 12,088 up-regulated in a male) were differentially expressed between sexes. Then, numerous candidate sex-related genes were identified, including dmrt2, dmrt4, foxl2, zps and starts. Furthermore, 23,941 simple sequence repeats (SSRs) were detected in SSR-containing sequences. This informative transcriptome analysis provides valuable data to increase the genomic resources of S. elongata. Full article
(This article belongs to the Special Issue New Applications of Computational Biology and Bioinformatics)
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