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Recent Progress and Challenges of Artificial Intelligence in Bioinformatics and New Medicine

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 July 2025 | Viewed by 16604

Special Issue Editors

School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China
Interests: bioinformatics; machine learning; network modeling; deep learning; big data mining

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Guest Editor
School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China
Interests: machine learning; network modeling; deep learning

E-Mail Website
Guest Editor
School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China
Interests: bioinformatics; data mining; artificial intelligence; knowledge engineering

Special Issue Information

Dear Colleagues,

At present, big data in biology, medical science, and public health are expanding rapidly, accelerating the development of new medicine. Discoveries in novel life sciences and translational medicine are constantly being promoted by the accumulation of big data. A number of disruptive new technologies, new methods, and new tools in the field of life and health have been formulated, and these are rapidly becoming a scientific and technological strategic focus leading the future of the life sciences, medical and health industries, and economic and social development. The cross-border integration and cross-innovation of the life sciences, medicine, and information science are the engines that push new medicine forward. In particular, advances in artificial intelligence (AI) have been applied in bioinformatics and new medicine, leading to tremendously successful novel discoveries. However, this process is accompanied by a lot of challenges, such as complex data modalities, the integration of multi-omics, highly dimensional features with a small sample size, etc. Considering these aspects, specific artificial intelligence methods are needed to handle the challenges in the field of bioinformatics and new medicine.

The aim of this Research Topic is to showcase advanced artificial intelligence methods and the traditional bioinformatics, data mining and statistical approaches helpful to discover novel knowledge in life science and new medicine. Topics of interest include, but are not limited to:

  • Review articles about the recent progress and challenges related to AI methods in bioinformatics and medical science.
  • Integration of single-cell omics.
  • Discovering novel knowledge in cancers and other complex diseases.
  • Exploration of risk factors of complex diseases.
  • Databases and webservers in bioinformatics.
  • Tumor heterogeneity and microenvironment.
  • Deep learning methods in bioinformatics and medical science.
  • Network modeling and analysis.

Dr. Tao Wang
Dr. Jiajie Peng
Dr. Yongtian Wang
Guest Editors

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 2400 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

  • artificial intelligence
  • bioinformatics
  • deep learning
  • new medicine
  • cancer
  • single cell omics
  • network

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Published Papers (8 papers)

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Research

Jump to: Review

16 pages, 454 KiB  
Article
Stability of Feature Selection in Multi-Omics Data Analysis
by Tomasz Łukaszuk, Jerzy Krawczuk, Kamil Żyła and Jacek Kęsik
Appl. Sci. 2024, 14(23), 11103; https://doi.org/10.3390/app142311103 - 28 Nov 2024
Viewed by 535
Abstract
In the rapidly evolving field of multi-omics data analysis, understanding the stability of feature selection is critical for reliable biomarker discovery and clinical applications. This study investigates the stability of feature-selection methods across various cancer types by utilizing 15 datasets from The Cancer [...] Read more.
In the rapidly evolving field of multi-omics data analysis, understanding the stability of feature selection is critical for reliable biomarker discovery and clinical applications. This study investigates the stability of feature-selection methods across various cancer types by utilizing 15 datasets from The Cancer Genome Atlas (TCGA). We employed classifiers with embedded feature selection, including Support Vector Machines (SVM), Logistic Regression (LR), and Lasso regression, each incorporating L1 regularization. Through a comprehensive evaluation using five-fold cross-validation, we measured feature-selection stability and assessed the accuracy of predictions regarding TP53 mutations, a known indicator of poor clinical outcomes in cancer patients. All three classifiers demonstrated optimal feature-selection stability, measured by the Nogueira metric, with higher regularization (fewer selected features), while lower regularization generally resulted in decreased stability across all omics layers. Our findings indicate differences in feature stability across the various omics layers; mirna consistently exhibited the highest stability across classifiers, while the mutation and rna layers were generally less stable, particularly with lower regularization. This work highlights the importance of careful feature selection and validation in high-dimensional datasets to enhance the robustness and reliability of multi-omics analyses. Full article
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12 pages, 2056 KiB  
Article
The Quantification of Myocardial Fibrosis on Human Histopathology Images by a Semi-Automatic Algorithm
by Diana Gonciar, Alexandru-George Berciu, Alex Ede Danku, Noemi Lorenzovici, Eva-Henrietta Dulf, Teodora Mocan, Sorina-Melinda Nicula and Lucia Agoston-Coldea
Appl. Sci. 2024, 14(17), 7696; https://doi.org/10.3390/app14177696 - 31 Aug 2024
Viewed by 1239
Abstract
(1) Background: Considering the increasing workload of pathologists, computer-assisted methods have the potential to come to their aid. Considering the prognostic role of myocardial fibrosis, its precise quantification is essential. Currently, the evaluation is performed semi-quantitatively by the pathologist, a method exposed to [...] Read more.
(1) Background: Considering the increasing workload of pathologists, computer-assisted methods have the potential to come to their aid. Considering the prognostic role of myocardial fibrosis, its precise quantification is essential. Currently, the evaluation is performed semi-quantitatively by the pathologist, a method exposed to the issues of subjectivity. The present research proposes validating a semi-automatic algorithm that aims to quantify myocardial fibrosis on microscopic images. (2) Methods: Forty digital images were selected from the slide collection of The Iowa Virtual Slidebox, from which the collagen volume fraction (CVF) was calculated using two semi-automatic methods: CIELAB-MATLAB® and CIELAB-Python. These involve the use of color difference analysis, using Delta E, in a rectangular region for CIELAB-Python and a region with a random geometric shape, determined by the user’s cursor movement, for CIELAB-MATLAB®. The comparison was made between the stereological evaluation and ImageJ. (3) Results: A total of 36 images were included in the study (n = 36), demonstrating a high, statistically significant correlation between stereology and ImageJ on the one hand, and the proposed methods on the other (p < 0.001). The mean CVF determined by the two methods shows a mean bias of 1.5% compared with stereology and 0.9% compared with ImageJ. Conclusions: The combined algorithm has a superior performance compared to the proposed methods, considered individually. Despite the relatively small mean bias, the limits of agreement are quite wide, reflecting the variability of the images included in the study. Full article
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16 pages, 4498 KiB  
Article
Towards Metric-Driven Difference Detection between Receptive and Nonreceptive Endometrial Samples Using Automatic Histology Image Analysis
by Vidas Raudonis, Ruta Bartasiene, Ave Minajeva, Merli Saare, Egle Drejeriene, Agne Kozlovskaja-Gumbriene and Andres Salumets
Appl. Sci. 2024, 14(13), 5715; https://doi.org/10.3390/app14135715 - 29 Jun 2024
Viewed by 1183
Abstract
This paper presents a technique that can potentially help to determine the receptivity stage of the endometrium from histology images by automatically measuring the stromal nuclear changes. The presented technique is composed of an image segmentation model and the statistical evolution of segmented [...] Read more.
This paper presents a technique that can potentially help to determine the receptivity stage of the endometrium from histology images by automatically measuring the stromal nuclear changes. The presented technique is composed of an image segmentation model and the statistical evolution of segmented areas in hematoxylin and eosin (HE)-stained histology images. Three different endometrium receptivity stages, namely pre-receptive, post-receptive, and receptive, were compared. An ensemble-based AI model was proposed for histology image segmentation, which is based on individual UNet++, UNet, and ResNet34-UNet segmentation models. The performance of the ensemble-based technique was assessed using the Dice score and intersection over unit (IoU) values. In comparison to alternative segmentation architectures that were applied singly, the current ensemble-based method obtained higher Dice score (0.95) and IoU (0.90) values. The statistical comparison highlighted a noticeable difference in the number of nuclei and the size of the stroma tissue. The proposed technique demonstrated the positive potential for practical implementation for automatic endometrial tissue analysis. Full article
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23 pages, 21874 KiB  
Article
Speech Emotion Recognition Using Deep Learning Transfer Models and Explainable Techniques
by Tae-Wan Kim and Keun-Chang Kwak
Appl. Sci. 2024, 14(4), 1553; https://doi.org/10.3390/app14041553 - 15 Feb 2024
Cited by 4 | Viewed by 3437
Abstract
This study aims to establish a greater reliability compared to conventional speech emotion recognition (SER) studies. This is achieved through preprocessing techniques that reduce uncertainty elements, models that combine the structural features of each model, and the application of various explanatory techniques. The [...] Read more.
This study aims to establish a greater reliability compared to conventional speech emotion recognition (SER) studies. This is achieved through preprocessing techniques that reduce uncertainty elements, models that combine the structural features of each model, and the application of various explanatory techniques. The ability to interpret can be made more accurate by reducing uncertain learning data, applying data in different environments, and applying techniques that explain the reasoning behind the results. We designed a generalized model using three different datasets, and each speech was converted into a spectrogram image through STFT preprocessing. The spectrogram was divided into the time domain with overlapping to match the input size of the model. Each divided section is expressed as a Gaussian distribution, and the quality of the data is investigated by the correlation coefficient between distributions. As a result, the scale of the data is reduced, and uncertainty is minimized. VGGish and YAMNet are the most representative pretrained deep learning networks frequently used in conjunction with speech processing. In dealing with speech signal processing, it is frequently advantageous to use these pretrained models synergistically rather than exclusively, resulting in the construction of ensemble deep networks. And finally, various explainable models (Grad CAM, LIME, occlusion sensitivity) are used in analyzing classified results. The model exhibits adaptability to voices in various environments, yielding a classification accuracy of 87%, surpassing that of individual models. Additionally, output results are confirmed by an explainable model to extract essential emotional areas, converted into audio files for auditory analysis using Grad CAM in the time domain. Through this study, we enhance the uncertainty of activation areas that are generated by Grad CAM. We achieve this by applying the interpretable ability from previous studies, along with effective preprocessing and fusion models. We can analyze it from a more diverse perspective through other explainable techniques. Full article
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15 pages, 3441 KiB  
Article
Individualized Stress Mobile Sensing Using Self-Supervised Pre-Training
by Tanvir Islam and Peter Washington
Appl. Sci. 2023, 13(21), 12035; https://doi.org/10.3390/app132112035 - 4 Nov 2023
Cited by 7 | Viewed by 2134
Abstract
Stress is widely recognized as a major contributor to a variety of health issues. Stress prediction using biosignal data recorded by wearables is a key area of study in mobile sensing research because real-time stress prediction can enable digital interventions to immediately react [...] Read more.
Stress is widely recognized as a major contributor to a variety of health issues. Stress prediction using biosignal data recorded by wearables is a key area of study in mobile sensing research because real-time stress prediction can enable digital interventions to immediately react at the onset of stress, helping to avoid many psychological and physiological symptoms such as heart rhythm irregularities. Electrodermal activity (EDA) is often used to measure stress. However, major challenges with the prediction of stress using machine learning include the subjectivity and sparseness of the labels, a large feature space, relatively few labels, and a complex nonlinear and subjective relationship between the features and outcomes. To tackle these issues, we examined the use of model personalization: training a separate stress prediction model for each user. To allow the neural network to learn the temporal dynamics of each individual’s baseline biosignal patterns, thus enabling personalization with very few labels, we pre-trained a one-dimensional convolutional neural network (1D CNN) using self-supervised learning (SSL). We evaluated our method using the Wearable Stress and Affect Detection(WESAD) dataset. We fine-tuned the pre-trained networks to the stress-prediction task and compared against equivalent models without any self-supervised pre-training. We discovered that embeddings learned using our pre-training method outperformed the supervised baselines with significantly fewer labeled data points: the models trained with SSL required less than 30% of the labels to reach equivalent performance without personalized SSL. This personalized learning method can enable precision health systems that are tailored to each subject and require few annotations by the end user, thus allowing for the mobile sensing of increasingly complex, heterogeneous, and subjective outcomes such as stress. Full article
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20 pages, 3983 KiB  
Article
Unlocking Efficiency in Fine-Grained Compositional Image Synthesis: A Single-Generator Approach
by Zongtao Wang and Zhiming Liu
Appl. Sci. 2023, 13(13), 7587; https://doi.org/10.3390/app13137587 - 27 Jun 2023
Viewed by 1178
Abstract
The use of Generative Adversarial Networks (GANs) has led to significant advancements in the field of compositional image synthesis. In particular, recent progress has focused on achieving synthesis at the semantic part level. However, to enhance performance at this level, existing approaches in [...] Read more.
The use of Generative Adversarial Networks (GANs) has led to significant advancements in the field of compositional image synthesis. In particular, recent progress has focused on achieving synthesis at the semantic part level. However, to enhance performance at this level, existing approaches in the literature tend to prioritize performance over efficiency, utilizing separate local generators for each semantic part. This approach leads to a linear increase in the number of local generators, posing a fundamental challenge for large-scale compositional image synthesis at the semantic part level. In this paper, we introduce a novel model called Single-Generator Semantic-Style GAN (SSSGAN) to improve efficiency in this context. SSSGAN utilizes a single generator to synthesize all semantic parts, thereby reducing the required number of local generators to a constant value. Our experiments demonstrate that SSSGAN achieves superior efficiency while maintaining a minimal impact on performance. Full article
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16 pages, 5443 KiB  
Article
A Novel Prognostic Model for Gastric Cancer with EP_Dis-Based Co-Expression Network Analysis
by Yalan Xu, Hongyan Zhang, Dan Cao, Zilan Ning, Liu Zhu and Xueyan Liu
Appl. Sci. 2023, 13(12), 7108; https://doi.org/10.3390/app13127108 - 14 Jun 2023
Viewed by 1398
Abstract
Ferroptosis is a regulated form of cell death that involves iron-dependent lipid peroxidation. Ferroptosis-related genes (FRGs) play an essential role in the tumorigenesis of gastric cancer (GC), which is one of the most common and lethal cancers worldwide. Understanding the prognostic significance of [...] Read more.
Ferroptosis is a regulated form of cell death that involves iron-dependent lipid peroxidation. Ferroptosis-related genes (FRGs) play an essential role in the tumorigenesis of gastric cancer (GC), which is one of the most common and lethal cancers worldwide. Understanding the prognostic significance of FRGs in GC can shed light on GC treatment and diagnosis. In this study, we proposed a new gene co-expression network analysis method, namely EP-WGCNA. This method used Euclidean and Pearson weighted distance (EP_dis) to construct a weighted gene co-expression network instead of the Pearson’s correlation coefficient used in the original WGCNA method. The aim was to better capture the interactions and functional associations among genes. We used EP-WGCNA to identify the FRGs related to GC phenotype and applied bioinformatics methods to select the FRGs associated with the prognosis (P-FRGs) of GC patients. Firstly, we screened the FRGs that were differentially expressed based on the TCGA and GTEx databases. Then, we selected the P-FRGs using EP-WGCNA, Cox regression, and Kaplan–Meier analysis. The prognostic model based on P-FRGs-Cox (ALB, BNIP3, DPEP1, GLS2, MEG3, PDK4, TF, and TSC22D3) was constructed on the TCGA-GTEx dataset. According to the median risk score, all patients in the TCGA training dataset and GSE84426 testing dataset were classified into a high- or low-risk group. GC patients in the low-risk group showed higher survival probability than those in the high-risk group. The time-dependent receiver operating characteristic (timeROC) showed that EP-WGCNA-Cox predicted 0.77 in the training set and 0.64 in the testing set for the 5-year survival rate of GC patients, which was better than traditional WGCNA-Cox (P-WGCNA-Cox). In addition, we validated that the P-FRGs were significantly differentially expressed in the adjacent non-tumor gastric tissues and tumor tissues by immunohistochemical staining from the Human Protein Atlas (HPA) database. We also found that the P-FRGs were enriched in tumorigenic pathways by enrichment analysis. In conclusion, our study demonstrated that EP-WGCNA can mine the key FRGs related to the phenotype of GC and is superior to the P-WGCNA. The EP-WGCNA-Cox model based on P-FRGs is reliable in predicting the survival rate of GC patients and can provide potential biomarkers and therapeutic targets for GC. Full article
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Review

Jump to: Research

16 pages, 1241 KiB  
Review
Integrating Artificial Intelligence for Academic Advanced Therapy Medicinal Products: Challenges and Opportunities
by Cristobal Aguilar-Gallardo and Ana Bonora-Centelles
Appl. Sci. 2024, 14(3), 1303; https://doi.org/10.3390/app14031303 - 5 Feb 2024
Cited by 2 | Viewed by 3476
Abstract
Cell and gene therapies represent promising new treatment options for many diseases, but also face challenges for clinical translation and delivery. Hospital-based GMP facilities enable rapid bench-to-bedside development and patient access but require significant adaptation to implement pharmaceutical manufacturing in healthcare infrastructures constrained [...] Read more.
Cell and gene therapies represent promising new treatment options for many diseases, but also face challenges for clinical translation and delivery. Hospital-based GMP facilities enable rapid bench-to-bedside development and patient access but require significant adaptation to implement pharmaceutical manufacturing in healthcare infrastructures constrained by space, regulations, and resources. This article reviews key considerations, constraints, and solutions for establishing hospital facilities for advanced therapy medicinal products (ATMPs). Technologies like process analytical technology (PAT), continuous manufacturing, and artificial intelligence (AI) can aid these facilities through enhanced process monitoring, control, and automation. However, quality systems tailored for product quality rather than just compliance, and substantial investment in infrastructure, equipment, personnel, and multi-departmental coordination, remain crucial for successful hospital ATMP facilities and to drive new therapies from research to clinical impact. Full article
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