Biomarkers and Therapeutic Targets Based on Bioinformatical Studies

Topic Information

Dear Colleagues,

Bioinformatics is a rapidly evolving field that has revolutionized our understanding of biological systems and their functions. The ability to analyze large volumes of data and thereby extract meaningful information has led to the identification of biomarkers that can be used for the diagnosis and treatment of diseases. This has greatly improved patient outcomes and paved the way for personalized medicine. The integration of cutting-edge technologies into bioinformatics has greatly facilitated the identification of biomarkers and new drugs, the development of diagnostic models, the design of targeted drugs, and the personalized treatment of diseases. In the development of drug targets, bioinformatics can also be used to discover new targets, predict drug actions, evaluate drug efficacy and safety, etc. Among these tasks, drug target prediction based on bioinformatics is an incredibly important research area. By analyzing protein sequences, structures, and functions through molecular docking and molecular simulation, the interactions between drugs and proteins can be predicted, and molecules with potential drug activity can be screened. In addition, bioinformatics can also be used for the simulation and design of drug molecules, thereby optimizing the properties and effects of drugs. In summary, bioinformatics plays an important role in the development of drug targets, providing new ideas and methods for drug development.

The implementation of cutting-edge technologies such as machine learning, RNA-seq, epigenomics, and metabolomics has greatly improved our understanding of biological systems and their functions. These technologies have been used to identify biomarkers for diagnosis and treatment, develop prognostic models that can predict the outcomes of disease progression, and identify patients at high risk of developing complications. The integration of these technologies with clinical data has led to the development of personalized medicine, which has greatly improved patient outcomes and has the potential to revolutionize healthcare.

We welcome original research, reviews, and other articles relevant to the relevant Topics. Topics of interest include but are not limited to the following:

The identification of biomarkers using advanced sequencing technologies;
the development of prognostic and diagnostic models using machine learning and deep learning algorithms;
the design of targeted drugs using omics data;
the validation of biomarkers using clinical samples;
the application of advanced technologies in personalized medicine;
the integration of different omics data for a comprehensive understanding of diseases.

Dr. Qingjia Chi
Prof. Dr. Xiangcheng Chen
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Bioengineering bioengineering	4.6	4.2	2014	17.7 Days	CHF 2700	Submit
Biology biology	4.2	4.0	2012	18.7 Days	CHF 2700	Submit
Biomimetics biomimetics	4.5	4.5	2016	17.2 Days	CHF 2200	Submit
Life life	3.2	2.7	2011	17.5 Days	CHF 2600	Submit
Molecules molecules	4.6	6.7	1996	14.6 Days	CHF 2700	Submit

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Published Papers (4 papers)

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All Bioengineering Biology Biomimetics Life Molecules

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15 pages, 3602 KiB  

Open AccessArticle

PRMT5 Mediated HIF1α Signaling and Ras-Related Nuclear Protein as Promising Biomarker in Hepatocellular Carcinoma

by Wafaa Abumustafa, Darko Castven, Fatemeh Saheb Sharif-Askari, Batoul Abi Zamer, Mawieh Hamad, Jens-Uwe Marquardt and Jibran Sualeh Muhammad

Biology 2024, 13(4), 216; https://doi.org/10.3390/biology13040216 - 27 Mar 2024

Viewed by 722

Abstract

Protein arginine N-methyltransferase 5 (PRMT5) has been identified as a potential therapeutic target for various cancer types. However, its role in regulating the hepatocellular carcinoma (HCC) transcriptome remains poorly understood. In this study, publicly available databases were employed to investigate PRMT5 expression, its [...] Read more.

Protein arginine N-methyltransferase 5 (PRMT5) has been identified as a potential therapeutic target for various cancer types. However, its role in regulating the hepatocellular carcinoma (HCC) transcriptome remains poorly understood. In this study, publicly available databases were employed to investigate PRMT5 expression, its correlation with overall survival, targeted pathways, and genes of interest in HCC. Additionally, we utilized in-house generated NGS data to explore PRMT5 expression in dysplastic nodules compared to hepatocellular carcinoma. Our findings revealed that PRMT5 is significantly overexpressed in HCC compared to normal liver, and elevated expression correlates with poor overall survival. To gain insights into the mechanism driving PRMT5 overexpression in HCC, we analyzed promoter CpG islands and methylation status in HCC compared to normal tissues. Pathway analysis of PRMT5 knockdown in the HCC cells revealed a connection between PRMT5 expression and genes related to the HIF1α pathway. Additionally, by filtering PRMT5-correlated genes within the HIF1α pathway and selecting up/downregulated genes in HCC patients, we identified Ras-related nuclear protein (RAN) as a target associated with overall survival. For the first time, we report that PRMT5 is implicated in the regulation of HIF1A and RAN genes, suggesting the potential prognostic utility of PRMT5 in HCC. Full article

(This article belongs to the Topic Biomarkers and Therapeutic Targets Based on Bioinformatical Studies)

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27 pages, 2559 KiB  

Open AccessArticle

Enhancement of Classifier Performance with Adam and RanAdam Hyper-Parameter Tuning for Lung Cancer Detection from Microarray Data—In Pursuit of Precision

by Karthika M S, Harikumar Rajaguru and Ajin R. Nair

Bioengineering 2024, 11(4), 314; https://doi.org/10.3390/bioengineering11040314 - 26 Mar 2024

Viewed by 521

Abstract

Microarray gene expression analysis is a powerful technique used in cancer classification and research to identify and understand gene expression patterns that can differentiate between different cancer types, subtypes, and stages. However, microarray databases are highly redundant, inherently nonlinear, and noisy. Therefore, extracting [...] Read more.

Microarray gene expression analysis is a powerful technique used in cancer classification and research to identify and understand gene expression patterns that can differentiate between different cancer types, subtypes, and stages. However, microarray databases are highly redundant, inherently nonlinear, and noisy. Therefore, extracting meaningful information from such a huge database is a challenging one. The paper adopts the Fast Fourier Transform (FFT) and Mixture Model (MM) for dimensionality reduction and utilises the Dragonfly optimisation algorithm as the feature selection technique. The classifiers employed in this research are Nonlinear Regression, Naïve Bayes, Decision Tree, Random Forest and SVM (RBF). The classifiers’ performances are analysed with and without feature selection methods. Finally, Adaptive Moment Estimation (Adam) and Random Adaptive Moment Estimation (RanAdam) hyper-parameter tuning techniques are used as improvisation techniques for classifiers. The SVM (RBF) classifier with the Fast Fourier Transform Dimensionality Reduction method and Dragonfly feature selection achieved the highest accuracy of 98.343% with RanAdam hyper-parameter tuning compared to other classifiers. Full article

(This article belongs to the Topic Biomarkers and Therapeutic Targets Based on Bioinformatical Studies)

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27 pages, 1435 KiB  

Open AccessReview

Molecular Assessment of Methylglyoxal-Induced Toxicity and Therapeutic Approaches in Various Diseases: Exploring the Interplay with the Glyoxalase System

by Muhanad Alhujaily

Life 2024, 14(2), 263; https://doi.org/10.3390/life14020263 - 17 Feb 2024

Viewed by 1058

Abstract

This comprehensive exploration delves into the intricate interplay of methylglyoxal (MG) and glyoxalase 1 (GLO I) in various physiological and pathological contexts. The linchpin of the narrative revolves around the role of these small molecules in age-related issues, diabetes, obesity, cardiovascular diseases, and [...] Read more.

This comprehensive exploration delves into the intricate interplay of methylglyoxal (MG) and glyoxalase 1 (GLO I) in various physiological and pathological contexts. The linchpin of the narrative revolves around the role of these small molecules in age-related issues, diabetes, obesity, cardiovascular diseases, and neurodegenerative disorders. Methylglyoxal, a reactive dicarbonyl metabolite, takes center stage, becoming a principal player in the development of AGEs and contributing to cell and tissue dysfunction. The dual facets of GLO I—activation and inhibition—unfold as potential therapeutic avenues. Activators, spanning synthetic drugs like candesartan to natural compounds like polyphenols and isothiocyanates, aim to restore GLO I function. These molecular enhancers showcase promising outcomes in conditions such as diabetic retinopathy, kidney disease, and beyond. On the contrary, GLO I inhibitors emerge as crucial players in cancer treatment, offering new possibilities in diseases associated with inflammation and multidrug resistance. The symphony of small molecules, from GLO I activators to inhibitors, presents a nuanced understanding of MG regulation. From natural compounds to synthetic drugs, each element contributes to a molecular orchestra, promising novel interventions and personalized approaches in the pursuit of health and wellbeing. The abstract concludes with an emphasis on the necessity of rigorous clinical trials to validate these findings and acknowledges the importance of individual variability in the complex landscape of health. Full article

(This article belongs to the Topic Biomarkers and Therapeutic Targets Based on Bioinformatical Studies)

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40 pages, 7326 KiB  

Open AccessArticle

Enhancement of Classifier Performance Using Swarm Intelligence in Detection of Diabetes from Pancreatic Microarray Gene Data

by Dinesh Chellappan and Harikumar Rajaguru

Biomimetics 2023, 8(6), 503; https://doi.org/10.3390/biomimetics8060503 - 22 Oct 2023

Viewed by 1166

Abstract

In this study, we focused on using microarray gene data from pancreatic sources to detect diabetes mellitus. Dimensionality reduction (DR) techniques were used to reduce the dimensionally high microarray gene data. DR methods like the Bessel function, Discrete Cosine Transform (DCT), Least Squares [...] Read more.

In this study, we focused on using microarray gene data from pancreatic sources to detect diabetes mellitus. Dimensionality reduction (DR) techniques were used to reduce the dimensionally high microarray gene data. DR methods like the Bessel function, Discrete Cosine Transform (DCT), Least Squares Linear Regression (LSLR), and Artificial Algae Algorithm (AAA) are used. Subsequently, we applied meta-heuristic algorithms like the Dragonfly Optimization Algorithm (DOA) and Elephant Herding Optimization Algorithm (EHO) for feature selection. Classifiers such as Nonlinear Regression (NLR), Linear Regression (LR), Gaussian Mixture Model (GMM), Expectation Maximum (EM), Bayesian Linear Discriminant Classifier (BLDC), Logistic Regression (LoR), Softmax Discriminant Classifier (SDC), and Support Vector Machine (SVM) with three types of kernels, Linear, Polynomial, and Radial Basis Function (RBF), were utilized to detect diabetes. The classifier’s performance was analyzed based on parameters like accuracy, F1 score, MCC, error rate, FM metric, and Kappa. Without feature selection, the SVM (RBF) classifier achieved a high accuracy of 90% using the AAA DR methods. The SVM (RBF) classifier using the AAA DR method for EHO feature selection outperformed the other classifiers with an accuracy of 95.714%. This improvement in the accuracy of the classifier’s performance emphasizes the role of feature selection methods. Full article

(This article belongs to the Topic Biomarkers and Therapeutic Targets Based on Bioinformatical Studies)

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