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Special Issue "Machine Learning in Bioinformatics: Latest Advances and Prospects"

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

Deadline for manuscript submissions: 20 August 2023 | Viewed by 3210

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Special Issue Editor

Dr. Je-Keun Rhee

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Department of Bioinformatics & Life Science, Soongsil University, Seoul 06978, Korea
Interests: bioinformatics; machine learning; genome informatics; cancer genomics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine learning can offer new perspectives in many fields of science and engineering. In particular, with the development of high-throughput technologies, the amount of biological data has been exponentially increased. Using large and complex datasets, machine learning techniques have been employed to solve a variety of biological problems. For example, machine learning has had significant impacts in many areas of bioinformatics, such as the analysis of genomic sequences, knowledge of gene regulation, prediction of molecular interactions, protein structure prediction, systematic modeling in cell systems, drug discovery, text mining, biomedical image analysis, and so on.

This Special Issue aims to cover recent advancements in machine learning techniques and applications that have been applied to bioinformatics. It will feature original research papers with technically sound and creative machine learning methods for a variety of biological challenges. Moreover, it invites review articles that present current challenges and outlooks in the field of biomedical sciences and highlight the importance of machine learning methods.

Dr. Je-Keun Rhee
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2300 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

machine learning
deep neural networks
feature selection
next-generation sequencing
gene expression
biomarker discovery
multi-omics data integration
reconstruction of biological networks
prediction of clinical outcomes
clinical decision support systems
prediction of drug response
drug discovery
protein-structure prediction
text mining
biomedical image analysis
healthcare

Published Papers (4 papers)

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Open AccessArticle

Feature Analysis of Predictors Affecting the Nidus Obliteration of Linear Accelerator-Based Radiosurgery for Arteriovenous Malformations Using Explainable Predictive Modeling

Kwang Hyeon Kim

and

Moon-Jun Sohn

Appl. Sci. 2023, 13(7), 4267; https://doi.org/10.3390/app13074267 - 28 Mar 2023

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Abstract

This study aimed to evaluate prognostic factors associated with nidus obliteration following stereotactic radiosurgery (SRS) for cerebral arteriovenous malformations. From January 2001 to January 2018, 119 patients who underwent SRS with AVM were studied to analyze major prognostic factors (age, prescription dose (Gy), volume (mm³), nidus size (cm), and Spetzler–Martin (SM) grade) for nidus obliteration. A random forest and tree explainer was used to construct a predictive model of nidus obliteration. The prognostic factors affecting nidus obliteration from most to least important were age, nidus size, volume, total prescription dose, and SM grade, using a predictive model. In a specific case for nidus size (1.5 cm), total dose (23 Gy), and SM grade (2), the result showed a high obliteration score of 0.75 with the actual obliteration period of 6 months spent; the mean AUC was 0.90 in K-fold cross validation. The predictive model identified the main contributing factors associated with a prognostic of nidus obliteration from linear accelerator-based SRS for cerebral AVM. It was confirmed that the results, including the prognostic factors, are potentially useful for outcome prediction for patient and treatment. Full article

(This article belongs to the Special Issue Machine Learning in Bioinformatics: Latest Advances and Prospects)

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Open AccessArticle

An Ensemble Feature Selection Approach for Analysis and Modeling of Transcriptome Data in Alzheimer’s Disease

Petros Paplomatas

Marios G. Krokidis

Panagiotis Vlamos

and

Aristidis G. Vrahatis

Appl. Sci. 2023, 13(4), 2353; https://doi.org/10.3390/app13042353 - 11 Feb 2023

Cited by 1 | Viewed by 859

Abstract

Data-driven analysis and characterization of molecular phenotypes comprises an efficient way to decipher complex disease mechanisms. Using emerging next generation sequencing technologies, important disease-relevant outcomes are extracted, offering the potential for precision diagnosis and therapeutics in progressive disorders. Single-cell RNA sequencing (scRNA-seq) allows the inherent heterogeneity between individual cellular environments to be exploited and provides one of the most promising platforms for quantifying cell-to-cell gene expression variability. However, the high-dimensional nature of scRNA-seq data poses a significant challenge for downstream analysis, particularly in identifying genes that are dominant across cell populations. Feature selection is a crucial step in scRNA-seq data analysis, reducing the dimensionality of data and facilitating the identification of genes most relevant to the biological question. Herein, we present a need for an ensemble feature selection methodology for scRNA-seq data, specifically in the context of Alzheimer’s disease (AD). We combined various feature selection strategies to obtain the most dominant differentially expressed genes (DEGs) in an AD scRNA-seq dataset, providing a promising approach to identify potential transcriptome biomarkers through scRNA-seq data analysis, which can be applied to other diseases. We anticipate that feature selection techniques, such as our ensemble methodology, will dominate analysis options for transcriptome data, especially as datasets increase in volume and complexity, leading to more accurate classification and the generation of differentially significant features. Full article

(This article belongs to the Special Issue Machine Learning in Bioinformatics: Latest Advances and Prospects)

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Open AccessArticle

Pseudomonas sp., Strain L5B5: A Genomic and Transcriptomic Insight into an Airborne Mine Bacterium

Jose Luis Gonzalez-Pimentel

Irene Dominguez-Moñino

Valme Jurado

Ana Teresa Caldeira

and

Cesareo Saiz-Jimenez

Appl. Sci. 2022, 12(21), 10854; https://doi.org/10.3390/app122110854 - 26 Oct 2022

Viewed by 678

Abstract

Mines, like other subterranean environments, have ecological conditions which allow the thriving of microorganisms. Prokaryotes and fungi are common inhabitants of mines, developing a metabolism suitable for growing in such inhospitable environments. The mine of Lousal, Portugal, is an interesting site for the study of microorganisms present in their galleries. Aerobiological studies resulted in the isolation of a Pseudomonas sp., strain L5B5, closely related to the opportunistic fish pathogen P. piscis MC042T, and to the soil bacteria P. protegens CHA0T, P. protegens Cab57, and P. protegens Pf-5. Strain L5B5 was able to inhibit the growth of the pathogenic bacteria Bacillus cereus, Staphylococcus aureus, and Acinetobacter baumanii, as well as the cave fungi Aspergillus versicolor, Penicillium chrysogenum, Cladosporium cladosporioides, Fusarium solani, and Ochroconis lascauxensis. In silico analyses based on de novo genome hybrid assembly and RNA-Seq, performing seven conditions based on culture and phases of growth resulted in the prediction and detection of genetic mechanisms involved in secondary metabolites, with the presence of a possible new gene cluster transcribed under the tested conditions, as well as feasible virulence factors and antimicrobial resistance mechanisms. Full article

(This article belongs to the Special Issue Machine Learning in Bioinformatics: Latest Advances and Prospects)

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Open AccessSystematic Review

PREFMoDeL: A Systematic Review and Proposed Taxonomy of Biomolecular Features for Deep Learning

Jacob L. North

and

Victor L. Hsu

Appl. Sci. 2023, 13(7), 4356; https://doi.org/10.3390/app13074356 - 29 Mar 2023

Viewed by 736

Abstract

Of fundamental importance in biochemical and biomedical research is understanding a molecule’s biological properties—its structure, its function(s), and its activity(ies). To this end, computational methods in Artificial Intelligence, in particular Deep Learning (DL), have been applied to further biomolecular understanding—from analysis and prediction of protein–protein and protein–ligand interactions to drug discovery and design. While choosing the most appropriate DL architecture is vitally important to accurately model the task at hand, equally important is choosing the features used as input to represent molecular properties in these DL models. Through hypothesis testing, bioinformaticians have created thousands of engineered features for biomolecules such as proteins and their ligands. Herein we present an organizational taxonomy for biomolecular features extracted from 808 articles from across the scientific literature. This objective view of biomolecular features can reduce various forms of experimental and/or investigator bias and additionally facilitate feature selection in biomolecular analysis and design tasks. The resulting dataset contains 1360 nondeduplicated features, and a sample of these features were classified by their properties, clustered, and used to suggest new features. The complete feature dataset (the Public Repository of Engineered Features for Molecular Deep Learning, PREFMoDeL) is released for collaborative sourcing on the web. Full article

(This article belongs to the Special Issue Machine Learning in Bioinformatics: Latest Advances and Prospects)

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Special Issue "Machine Learning in Bioinformatics: Latest Advances and Prospects"

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