New Developments and Applications in Bioinformatics and Computational Biology

Topic Information

Dear Colleagues,

Bioinformatics and Computational Biology is a field of increasing interest due to recent developments in AI and ICT technologies, as well as the production of massive data in the field of biology and medicine thanks to the development of omics techniques. Further development of novel methods and tools is needed to correctly manage, analyze, use and re-use, and interpret the amount of data being generated in the biological sciences. 

This Topic aims to present novel methods as well as applications of new and existing tools in the Bioinformatics and Computational Biology area. Developments and applications in genomics, transcriptomics, proteomics, and metabolomics are of particular interest, although any study in the Bioinformatics and Computational Biology area is suitable for this Topic.

Prof. Dr. Angelo Facchiano
Dr. Deborah Giordano
Dr. Bernardina Scafuri
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
BioMed biomed	-	-	2021	21.9 Days	CHF 1000
Computation computation	1.9	3.5	2013	18.6 Days	CHF 1800
Entropy entropy	2.1	4.9	1999	22.3 Days	CHF 2600
Informatics informatics	3.4	6.6	2014	38.1 Days	CHF 1800
Molecules molecules	4.2	7.4	1996	15.1 Days	CHF 2700

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Published Papers (2 papers)

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All BioMed Computation Entropy Informatics Molecules

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25 pages, 1047 KiB  

Open AccessArticle

MSProfileR: An Open-Source Software for Quality Control of Matrix-Assisted Laser Desorption Ionization–Time of Flight Spectra

by Refka Ben Hamouda, Bertrand Estellon, Khalil Himet, Aimen Cherif, Hugo Marthinet, Jean-Marie Loreau, Gaëtan Texier, Samuel Granjeaud and Lionel Almeras

Informatics 2024, 11(2), 39; https://doi.org/10.3390/informatics11020039 - 6 Jun 2024

Cited by 1 | Viewed by 2118

Abstract

In the early 2000s, matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS) emerged as a performant and relevant tool for identifying micro-organisms. Since then, it has become practically essential for identifying bacteria in microbiological diagnostic laboratories. In the last decade, it [...] Read more.

In the early 2000s, matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS) emerged as a performant and relevant tool for identifying micro-organisms. Since then, it has become practically essential for identifying bacteria in microbiological diagnostic laboratories. In the last decade, it was successfully applied for arthropod identification, allowing researchers to distinguish vectors from non-vectors of infectious diseases. However, identification failures are not rare, hampering its wide use. Failure is generally attributed either to the absence of respective counter species MS spectra in the database or to the insufficient quality of query MS spectra (i.e., lower intensity and diversity of MS peaks detected). To avoid matching errors due to non-compliant spectra, the development of a strategy for detecting and excluding outlier MS profiles became compulsory. To this end, we created MSProfileR, an R package leading to a bioinformatics tool through a simple installation, integrating a control quality system of MS spectra and an analysis pipeline including peak detection and MS spectra comparisons. MSProfileR can also add metadata concerning the sample that the spectra are derived from. MSProfileR has been developed in the R environment and offers a user-friendly web interface using the R Shiny framework. It is available on Microsoft Windows as a web browser application by simple navigation using the link of the package on Github v.3.10.0. MSProfileR is therefore accessible to non-computer specialists and is freely available to the scientific community. We evaluated MSProfileR using two datasets including exclusively MS spectra from arthropods. In addition to coherent sample classification, outlier MS spectra were detected in each dataset confirming the value of MSProfileR. Full article

(This article belongs to the Topic New Developments and Applications in Bioinformatics and Computational Biology)

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16 pages, 4384 KiB  

Open AccessArticle

PIS-Net: Efficient Medical Image Segmentation Network with Multivariate Downsampling for Point-of-Care

by Changrui Zhang and Jia Wang

Entropy 2024, 26(4), 284; https://doi.org/10.3390/e26040284 - 26 Mar 2024

Viewed by 1339

Abstract

Recently, with more portable diagnostic devices being moved to people anywhere, point-of-care (PoC) imaging has become more convenient and more popular than the traditional “bed imaging”. Instant image segmentation, as an important technology of computer vision, is receiving more and more attention in [...] Read more.

Recently, with more portable diagnostic devices being moved to people anywhere, point-of-care (PoC) imaging has become more convenient and more popular than the traditional “bed imaging”. Instant image segmentation, as an important technology of computer vision, is receiving more and more attention in PoC diagnosis. However, the image distortion caused by image preprocessing and the low resolution of medical images extracted by PoC devices are urgent problems that need to be solved. Moreover, more efficient feature representation is necessary in the design of instant image segmentation. In this paper, a new feature representation considering the relationships among local features with minimal parameters and a lower computational complexity is proposed. Since a feature window sliding along a diagonal can capture more pluralistic features, a Diagonal-Axial Multi-Layer Perceptron is designed to obtain the global correlation among local features for a more comprehensive feature representation. Additionally, a new multi-scale feature fusion is proposed to integrate nonlinear features with linear ones to obtain a more precise feature representation. Richer features are figured out. In order to improve the generalization of the models, a dynamic residual spatial pyramid pooling based on various receptive fields is constructed according to different sizes of images, which alleviates the influence of image distortion. The experimental results show that the proposed strategy has better performance on instant image segmentation. Notably, it yields an average improvement of 1.31% in Dice than existing strategies on the BUSI, ISIC2018 and MoNuSeg datasets. Full article

(This article belongs to the Topic New Developments and Applications in Bioinformatics and Computational Biology)

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