Computational Genomics for Disease Prediction

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biomedical Engineering and Biomaterials".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 5447

Special Issue Editor


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Guest Editor
1. Department of Information, Electronic and Bioengineering, Politecnico di Milano, Milan, Italy
2. Stanford University School of Medicine, Stanford, CA, USA
Interests: bioinformatics; computational biology; applied machine learning; data science; biomedicine
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Special Issue Information

Dear Colleagues,

Advances in computational genomics have revolutionized the field of disease prediction in recent years. By leveraging massive amounts of genomic data, researchers can identify genetic markers and patterns associated with an increased risk of developing various diseases. This Special Issue aims to showcase the latest research and advancements in computational genomics for disease prediction.

With the development of high-throughput sequencing technologies, researchers can now analyze the entire genome of an individual cost-effectively and efficiently. This has enabled the identification of rare and common genetic variants that play a role in the development of complex diseases such as cancer, cardiovascular disease, and neurodegenerative disorders. By integrating genomic data with clinical information and environmental factors, computational genomics can provide personalized risk assessment and enable targeted interventions for individuals at high risk of disease.

Topics of interest include but are not limited to the following:

  1. Genome-wide association studies;
  2. Machine learning algorithms for risk prediction;
  3. Functional annotation of genetic variants;
  4. Integration of multi-omics data for disease prediction.

We welcome submissions from bioinformatics, computational biology, and genetics researchers who are working towards improving disease prediction and prevention through computational genomics.

Dr. Pietro Pinoli
Guest Editor

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Keywords

  • computational genomics
  • disease prediction
  • genetic markers
  • high-throughput sequencing
  • machine learning
  • personalized medicine

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Published Papers (4 papers)

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Research

17 pages, 3124 KiB  
Article
DGNMDA: Dual Heterogeneous Graph Neural Network Encoder for miRNA-Disease Association Prediction
by Daying Lu, Qi Zhang, Chunhou Zheng, Jian Li and Zhe Yin
Bioengineering 2024, 11(11), 1132; https://doi.org/10.3390/bioengineering11111132 - 10 Nov 2024
Viewed by 949
Abstract
In recent years, numerous studies have highlighted the pivotal importance of miRNAs in personalized healthcare, showcasing broad application prospects. miRNAs hold significant potential in disease diagnosis, prognosis assessment, and therapeutic target discovery, making them an integral part of precision medicine. They are expected [...] Read more.
In recent years, numerous studies have highlighted the pivotal importance of miRNAs in personalized healthcare, showcasing broad application prospects. miRNAs hold significant potential in disease diagnosis, prognosis assessment, and therapeutic target discovery, making them an integral part of precision medicine. They are expected to enable precise disease subtyping and risk prediction, thereby advancing the development of precision medicine. GNNs, a class of deep learning architectures tailored for graph data analysis, have greatly facilitated the advancement of miRNA-disease association prediction algorithms. However, current methods often fall short in leveraging network node information, particularly in utilizing global information while neglecting the importance of local information. Effectively harnessing both local and global information remains a pressing challenge. To tackle this challenge, we propose an innovative model named DGNMDA. Initially, we constructed various miRNA and disease similarity networks based on authoritative databases. Subsequently, we creatively design a dual heterogeneous graph neural network encoder capable of efficiently learning feature information between adjacent nodes and similarity information across the entire graph. Additionally, we develop a specialized fine-grained multi-layer feature interaction gating mechanism to integrate outputs from the neural network encoders to identify novel associations connecting miRNAs with diseases. We evaluate our model using 5-fold cross-validation and real-world disease case studies, based on the HMDD V3.2 dataset. Our method demonstrates superior performance compared to existing approaches in various tasks, confirming the effectiveness and potential of DGNMDA as a robust method for predicting miRNA-disease associations. Full article
(This article belongs to the Special Issue Computational Genomics for Disease Prediction)
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22 pages, 9721 KiB  
Article
Identification of a Novel Signature Based on Ferritinophagy-Related Genes to Predict Prognosis in Lung Adenocarcinoma: Focus on AHNAK2
by Liangjiang Xia and Haitao Ma
Bioengineering 2024, 11(11), 1070; https://doi.org/10.3390/bioengineering11111070 - 26 Oct 2024
Viewed by 1033
Abstract
Background: Lung adenocarcinoma (LUAD) accounts for over 40% of all non-small cell lung cancer (NSCLC) cases and continues to be difficult to treat despite advancements in diagnostics and therapies. Ferritinophagy, a newly recognized autophagy process linked to ferroptosis, has been associated with LUAD [...] Read more.
Background: Lung adenocarcinoma (LUAD) accounts for over 40% of all non-small cell lung cancer (NSCLC) cases and continues to be difficult to treat despite advancements in diagnostics and therapies. Ferritinophagy, a newly recognized autophagy process linked to ferroptosis, has been associated with LUAD development. Recent studies have shown a dysregulation of genes related to ferritinophagy in LUAD, indicating its potential as a therapeutic target. Methods: We constructed a predictive model using seven genes associated with ferritinophagy. The model’s accuracy was evaluated across three independent gene expression datasets. We analyzed the biological functions, immune environment, mutations, and drug sensitivities in groups with high and low risk. Utilizing a single-cell sequencing (scRNA-seq) dataset, we confirmed the expression of the model genes and identified a subtype of epithelial cells expressing AHNAK2. We further investigated the impact of the ferritinophagy-related gene AHNAK2 on LUAD cell proliferation, invasion, migration, and ferroptosis in vitro. Results: Our prediction model, comprising seven genes (AHNAK2, ARNTL2, CD27, LTB, SLC15A1, SLC2A1, and SYT1), has shown potential in predicting the prognosis of individuals diagnosed with LUAD. Notably, AHNAK2 impedes ferroptosis, promoting LUAD progression in vitro. Conclusions: Our research suggests that ferritinophagy-associated genes are promising prognostic markers for LUAD and lay the groundwork for further exploration of ferritinophagy’s role in LUAD. Furthermore, we present AHNAK2 as a novel regulator of ferroptosis, which requires further investigation to understand its mechanism. Full article
(This article belongs to the Special Issue Computational Genomics for Disease Prediction)
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18 pages, 6385 KiB  
Article
Mobile Application and Machine Learning-Driven Scheme for Intelligent Diabetes Progression Analysis and Management Using Multiple Risk Factors
by Huaiyan Jiang, Han Wang, Ting Pan, Yuhang Liu, Peiguang Jing and Yu Liu
Bioengineering 2024, 11(11), 1053; https://doi.org/10.3390/bioengineering11111053 - 22 Oct 2024
Cited by 1 | Viewed by 1237
Abstract
Diabetes mellitus is a chronic disease that affects over 500 million people worldwide, necessitating personalized health management programs for effective long-term control. Among the various biomarkers, glycated hemoglobin (HbA1c) is a crucial indicator for monitoring long-term blood glucose levels and assessing diabetes progression. [...] Read more.
Diabetes mellitus is a chronic disease that affects over 500 million people worldwide, necessitating personalized health management programs for effective long-term control. Among the various biomarkers, glycated hemoglobin (HbA1c) is a crucial indicator for monitoring long-term blood glucose levels and assessing diabetes progression. This study introduces an innovative approach to diabetes management by integrating a mobile application and machine learning. We designed and implemented an intelligent application capable of collecting comprehensive data from diabetic patients, creating a novel diabetes dataset named DiabMini with 127 features of 88 instances, including medical information, personal information, and detailed nutrient intake and lifestyle. Leveraging the DiabMini, we focused the analysis on HbA1c dynamics due to their clinical significance in tracking diabetes progression. We developed a stacking model combining eXtreme Gradient Boosting (XGBoost), Support Vector Classifier (SVC), Extra Trees (ET), and K-Nearest Neighbors (KNN) to explore the impact of various influencing factors on HbA1c dynamics, which achieved a classification accuracy of 94.23%. Additionally, we applied SHapley Additive exPlanations (SHAP) to visualize the contributions of risk factors to HbA1c dynamics, thus clarifying the differential impacts of these factors on diabetes progression. In conclusion, this study demonstrates the potential of integrating mobile health applications with machine learning to enhance personalized diabetes management. Full article
(This article belongs to the Special Issue Computational Genomics for Disease Prediction)
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13 pages, 1959 KiB  
Article
A Deep Learning-Enhanced Compartmental Model and Its Application in Modeling Omicron in China
by Qi Deng and Guifang Wang
Bioengineering 2024, 11(9), 906; https://doi.org/10.3390/bioengineering11090906 - 10 Sep 2024
Viewed by 1186
Abstract
The mainstream compartmental models require stochastic parameterization to estimate the transmission parameters between compartments, whose calculation depend upon detailed statistics on epidemiological characteristics, which are expensive, economically and resource-wise, to collect. In addition, infectious diseases spread in three dimensions: temporal, spatial, and mobile, [...] Read more.
The mainstream compartmental models require stochastic parameterization to estimate the transmission parameters between compartments, whose calculation depend upon detailed statistics on epidemiological characteristics, which are expensive, economically and resource-wise, to collect. In addition, infectious diseases spread in three dimensions: temporal, spatial, and mobile, i.e., they affect a population through not only the time progression of infection, but also the geographic distribution and physical mobility of the population. However, the parameterization process for the mainstream compartmental models does not effectively capture the spatial and mobile dimensions. As an alternative, deep learning techniques are utilized in estimating these stochastic parameters with greatly reduced dependency on data particularity and with a built-in temporal–spatial–mobile process that models the geographic distribution and physical mobility of the population. In particular, we apply DNN (Deep Neural Network) and LSTM (Long-Short Term Memory) techniques to estimate the transmission parameters in a customized compartmental model, then feed the estimated transmission parameters to the compartmental model to predict the development of the Omicron epidemic in China over the 28 days for the period between 4 June and 1 July 2022. The average levels of predication accuracy of the model are 98% and 92% for the number of infections and deaths, respectively. We establish that deep learning techniques provide an alternative to the prevalent compartmental modes and demonstrate the efficacy and potential of applying deep learning methodologies in predicting the dynamics of infectious diseases. Full article
(This article belongs to the Special Issue Computational Genomics for Disease Prediction)
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