Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.5
2.7
Journals
Applied Sciences
Special Issues
Recent Progress and Challenges of Artificial Intelligence in Bioinformatics and...
share announcement

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: 30 May 2024 | Viewed by 5609

Special Issue Editors

Dr. Tao Wang

E-Mail Website
Guest Editor
School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China
Interests: bioinformatics; machine learning; network modeling; deep learning; big data mining
Dr. Jiajie Peng

E-Mail Website
Guest Editor
School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China
Interests: machine learning; network modeling; deep learning
Dr. Yongtian Wang

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

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

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

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
Viewed by 957
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
(This article belongs to the Special Issue Recent Progress and Challenges of Artificial Intelligence in Bioinformatics and New Medicine)
►▼ Show Figures

Figure 1

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 - 04 Nov 2023
Cited by 4 | Viewed by 1067
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
(This article belongs to the Special Issue Recent Progress and Challenges of Artificial Intelligence in Bioinformatics and New Medicine)
►▼ Show Figures

Figure 1

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 717
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
(This article belongs to the Special Issue Recent Progress and Challenges of Artificial Intelligence in Bioinformatics and New Medicine)
►▼ Show Figures

Figure 1

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 999
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
(This article belongs to the Special Issue Recent Progress and Challenges of Artificial Intelligence in Bioinformatics and New Medicine)
►▼ Show Figures

Figure 1

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 - 05 Feb 2024
Viewed by 1162
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
(This article belongs to the Special Issue Recent Progress and Challenges of Artificial Intelligence in Bioinformatics and New Medicine)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Blockchain Technology for Advanced Therapy Medicinal Products: Applications in Tracking, Data Sharing, and Supply Chain Automation
Authors: Cristobal Aguilar-Gallardo; Ana Bonora-Centelles
Affiliation: Unidad de Terapias Avanzadas. Instituto Investigación Sanitaria La Fe. Av. Fernando Abril Martorell, 106. 46026 Valencia-Spain
Abstract: Advanced therapy medicinal products (ATMPs) like cell and gene therapies offer transformative treatment options for many diseases. However, coordinating the decentralized, patient-specific manufacturing of autologous ATMPs across multiple hospitals poses major supply chain challenges. This paper provides a comprehensive analysis of how blockchain technology can enhance decentralized ATMP manufacturing networks. First, background on ATMPs and complexities of decentralized production is reviewed. An overview of blockchain architecture, key attributes, and existing use cases then follows. The major opportunities for blockchain integration in ATMP manufacturing are discussed in depth, including tracking autologous products across locations, enabling data sharing between hospitals to power AI-based optimization, automating supply chain processes, and maintaining provenance records. Critical limitations around scalability, privacy, regulation, and adoption barriers are examined. Design considerations for developing blockchain ecosystems tailored to the unique ATMP environment are also explored. Blockchain shows immense promise for transforming visibility, coordination, automation, and data unification in decentralized ATMP manufacturing networks. Despite current challenges, blockchain is prepared to profoundly impact the advancement of personalized cell and gene therapies through enhanced supply chain instrumentation. This paper provides a comprehensive analysis of this emerging technological innovation and its applications to address critical needs in ATMP translation and manufacturing.

Back to TopTop