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The Development and Application of Computational Models for Identifying Disease Markers

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A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 10253

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Prof. Dr. Hao Lin

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Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
Interests: protein informatics; peptide sequence analysis; machine learning application in biological macromolecular data; biomarker; protein post-translational modification site; systems biology; clinical data analysis; disease risk prediction; analysis and identification of DNA regulatory element
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Dear Colleagues,

The identification and prediction of disease and genetic markers could provide important clues for disease diagnosis, drug target discovery, and genetic character analysis. However, using the traditional method to identify and analyze these markers becomes more and more difficult because of its expensive experimental materials, long experimental period and low computational efficiency. The advent of big data and advanced technology provides us with an opportunity to mine data and samples for the discovery of various disease and genetic markers. Some dry and wet experimental methods have been developed to deal with various medical samples and biological data. The results obtained from wet experimental methods should at least be demonstrated in an in vitro experiment. This Special Issue will focus on various aspects of the development and application of computational methods and techniques in biological and medical data for discovering disease markers. The subtopics include, but are not limited to:

The identification of disease markers from DNA regulatory elements;
The identification of gene and RNA markers in disease using machine learning methods;
The recognition of differential gene expression in disease;
The discovery of drug target using computational method;
Pathogenicity island identification using a machine learning method;
Disease diagnosis based on DNA, RNA and protein data using data mining;
Epigenetics markers discovery for disease using systems biology;
Molecular network marker for disease diagnosis and therapy;
Mining disease markers from 3D genomes using computational model;
Design new computational model for disease marker discovery.

Prof. Dr. Hao Lin
Guest Editor

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Keywords

identification
DNA regulatory elements
gene and RNA markers
machine learning methods
differential gene expression
computational method
pathogenicity island identification
data mining
epigenetics markers
systems biology
molecular network marker
computational model

Published Papers (5 papers)

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Research

13 pages, 4784 KiB

Open AccessCommunication

Identification of Potential Biomarkers and Small Molecule Drugs for Bisphosphonate-Related Osteonecrosis of the Jaw (BRONJ): An Integrated Bioinformatics Study Using Big Data

by Kumarendran Balachandran, Roszalina Ramli, Saiful Anuar Karsani and Mariati Abdul Rahman

Int. J. Mol. Sci. 2023, 24(10), 8635; https://doi.org/10.3390/ijms24108635 - 11 May 2023

Viewed by 1626

Abstract

This study aimed to identify potential molecular mechanisms and therapeutic targets for bisphosphonate-related osteonecrosis of the jaw (BRONJ), a rare but serious side effect of bisphosphonate therapy. This study analyzed a microarray dataset (GSE7116) of multiple myeloma patients with BRONJ (n = 11) and controls (n = 10), and performed gene ontology, a pathway enrichment analysis, and a protein–protein interaction network analysis. A total of 1481 differentially expressed genes were identified, including 381 upregulated and 1100 downregulated genes, with enriched functions and pathways related to apoptosis, RNA splicing, signaling pathways, and lipid metabolism. Seven hub genes (FN1, TNF, JUN, STAT3, ACTB, GAPDH, and PTPRC) were also identified using the cytoHubba plugin in Cytoscape. This study further screened small-molecule drugs using CMap and verified the results using molecular docking methods. This study identified 3-(5-(4-(Cyclopentyloxy)-2-hydroxybenzoyl)-2-((3-hydroxybenzo[d]isoxazol-6-yl) methoxy) phenyl) propanoic acid as a potential drug treatment and prognostic marker for BRONJ. The findings of this study provide reliable molecular insight for biomarker validation and potential drug development for the screening, diagnosis, and treatment of BRONJ. Further research is needed to validate these findings and develop an effective biomarker for BRONJ. Full article

(This article belongs to the Special Issue The Development and Application of Computational Models for Identifying Disease Markers)

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17 pages, 3225 KiB

Open AccessArticle

Assessing the Differential Methylation Analysis Quality for Microarray and NGS Platforms

by Anna Budkina, Yulia A. Medvedeva and Alexey Stupnikov

Int. J. Mol. Sci. 2023, 24(10), 8591; https://doi.org/10.3390/ijms24108591 - 11 May 2023

Viewed by 1695

Abstract

Differential methylation (DM) is actively recruited in different types of fundamental and translational studies. Currently, microarray- and NGS-based approaches for methylation analysis are the most widely used with multiple statistical models designed to extract differential methylation signatures. The benchmarking of DM models is challenging due to the absence of gold standard data. In this study, we analyze an extensive number of publicly available NGS and microarray datasets with divergent and widely utilized statistical models and apply the recently suggested and validated rank-statistic-based approach Hobotnica to evaluate the quality of their results. Overall, microarray-based methods demonstrate more robust and convergent results, while NGS-based models are highly dissimilar. Tests on the simulated NGS data tend to overestimate the quality of the DM methods and therefore are recommended for use with caution. Evaluation of the top 10 DMC and top 100 DMC in addition to the not-subset signature also shows more stable results for microarray data. Summing up, given the observed heterogeneity in NGS methylation data, the evaluation of newly generated methylation signatures is a crucial step in DM analysis. The Hobotnica metric is coordinated with previously developed quality metrics and provides a robust, sensitive, and informative estimation of methods’ performance and DM signatures’ quality in the absence of gold standard data solving a long-existing problem in DM analysis. Full article

(This article belongs to the Special Issue The Development and Application of Computational Models for Identifying Disease Markers)

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17 pages, 9897 KiB

Open AccessArticle

iADRGSE: A Graph-Embedding and Self-Attention Encoding for Identifying Adverse Drug Reaction in the Earlier Phase of Drug Development

by Xiang Cheng, Meiling Cheng, Liyi Yu and Xuan Xiao

Int. J. Mol. Sci. 2022, 23(24), 16216; https://doi.org/10.3390/ijms232416216 - 19 Dec 2022

Cited by 2 | Viewed by 2281

Abstract

Adverse drug reactions (ADRs) are a major issue to be addressed by the pharmaceutical industry. Early and accurate detection of potential ADRs contributes to enhancing drug safety and reducing financial expenses. The majority of the approaches that have been employed to identify ADRs are limited to determining whether a drug exhibits an ADR, rather than identifying the exact type of ADR. By introducing the “multi-level feature-fusion deep-learning model”, a new predictor, called iADRGSE, has been developed, which can be used to identify adverse drug reactions at the early stage of drug discovery. iADRGSE integrates a self-attentive module and a graph-network module that can extract one-dimensional sub-structure sequence information and two-dimensional chemical-structure graph information of drug molecules. As a demonstration, cross-validation and independent testing were performed with iADRGSE on a dataset of ADRs classified into 27 categories, based on SOC (system organ classification). In addition, experiments comparing iADRGSE with approaches such as NPF were conducted on the OMOP dataset, using the jackknife test method. Experiments show that iADRGSE was superior to existing state-of-the-art predictors. Full article

(This article belongs to the Special Issue The Development and Application of Computational Models for Identifying Disease Markers)

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14 pages, 2123 KiB

Open AccessArticle

DLm6Am: A Deep-Learning-Based Tool for Identifying N6,2′-O-Dimethyladenosine Sites in RNA Sequences

by Zhengtao Luo, Wei Su, Liliang Lou, Wangren Qiu, Xuan Xiao and Zhaochun Xu

Int. J. Mol. Sci. 2022, 23(19), 11026; https://doi.org/10.3390/ijms231911026 - 20 Sep 2022

Cited by 14 | Viewed by 1714

Abstract

N6,2′-O-dimethyladenosine (m⁶Am) is a post-transcriptional modification that may be associated with regulatory roles in the control of cellular functions. Therefore, it is crucial to accurately identify transcriptome-wide m⁶Am sites to understand underlying m⁶Am-dependent mRNA regulation mechanisms and biological functions. Here, we used three sequence-based feature-encoding schemes, including one-hot, nucleotide chemical property (NCP), and nucleotide density (ND), to represent RNA sequence samples. Additionally, we proposed an ensemble deep learning framework, named DLm6Am, to identify m⁶Am sites. DLm6Am consists of three similar base classifiers, each of which contains a multi-head attention module, an embedding module with two parallel deep learning sub-modules, a convolutional neural network (CNN) and a Bi-directional long short-term memory (BiLSTM), and a prediction module. To demonstrate the superior performance of our model’s architecture, we compared multiple model frameworks with our method by analyzing the training data and independent testing data. Additionally, we compared our model with the existing state-of-the-art computational methods, m6AmPred and MultiRM. The accuracy (ACC) for the DLm6Am model was improved by 6.45% and 8.42% compared to that of m6AmPred and MultiRM on independent testing data, respectively, while the area under receiver operating characteristic curve (AUROC) for the DLm6Am model was increased by 4.28% and 5.75%, respectively. All the results indicate that DLm6Am achieved the best prediction performance in terms of ACC, Matthews correlation coefficient (MCC), AUROC, and the area under precision and recall curves (AUPR). To further assess the generalization performance of our proposed model, we implemented chromosome-level leave-out cross-validation, and found that the obtained AUROC values were greater than 0.83, indicating that our proposed method is robust and can accurately predict m⁶Am sites. Full article

(This article belongs to the Special Issue The Development and Application of Computational Models for Identifying Disease Markers)

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14 pages, 2237 KiB

Open AccessArticle

A Statistical Analysis of the Sequence and Structure of Thermophilic and Non-Thermophilic Proteins

by Zahoor Ahmed, Hasan Zulfiqar, Lixia Tang and Hao Lin

Int. J. Mol. Sci. 2022, 23(17), 10116; https://doi.org/10.3390/ijms231710116 - 04 Sep 2022

Cited by 17 | Viewed by 2225

Abstract

Thermophilic proteins have various practical applications in theoretical research and in industry. In recent years, the demand for thermophilic proteins on an industrial scale has been increasing; therefore, the engineering of thermophilic proteins has become a hot direction in the field of protein engineering. However, the exact mechanism of thermostability of proteins is not yet known, for engineering thermophilic proteins knowing the basis of thermostability is necessary. In order to understand the basis of the thermostability in proteins, we have made a statistical analysis of the sequences, secondary structures, hydrogen bonds, salt bridges, DHA (Donor–Hydrogen–Accepter) angles, and bond lengths of ten pairs of thermophilic proteins and their non-thermophilic orthologous. Our findings suggest that polar amino acids contribute to thermostability in proteins by forming hydrogen bonds and salt bridges which provide resistance against protein denaturation. Short bond length and a wider DHA angle provide greater bond stability in thermophilic proteins. Moreover, the increased frequency of aromatic amino acids in thermophilic proteins contributes to thermal stability by forming more aromatic interactions. Additionally, the coil, helix, and loop in the secondary structure also contribute to thermostability. Full article

(This article belongs to the Special Issue The Development and Application of Computational Models for Identifying Disease Markers)

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