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A special issue of BioMedInformatics (ISSN 2673-7426). This special issue belongs to the section "Computational Biology and Medicine".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 28073

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Dr. Shuncong Wang

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Laboratory of Lipid Metabolism and Cancer, Biomedical Group, Campus Gasthuisberg, KU Leuven, 3000 Leuven, Belgium
Interests: cancer imaging; artificial intelligence; radiomics; brain metastasis
Special Issues, Collections and Topics in MDPI journals

Dr. Peihua Zhao

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Laboratory for Mechanisms of Cell Transformation, Biomedical Group, Campus Gasthuisberg, KU Leuven, 3000 Leuven, Belgium
Interests: cancer imaging; artificial intelligence; radiomics; brain metastasis

Special Issue Information

Dear Colleagues,

Towards precision medicine, massive high-dimensional medical data are collected to achieve a better understanding of diseases. In this regard, analysis, interpretation, and translational application of these data demand state-of-the-art computational algorithms.

Genomic, histopathology, clinical imaging exam, lipidomic, and proteomic data, among others, are commonly collected in clinics and preclinical studies. Computational biology and artificial intelligence (AI) are commonly adopted strategies in the biomedical field. Computational biology analyzes genetic alterations and links these to clinical characteristics and prognosis, aiding in clinical decision making. AI can handle high-dimensional data, such as radiomics, and even multi-omics data. Integrating information from multiple sources, including histopathological images and multi-omics data, provides a great opportunity for improving cancer diagnosis and treatment. Taking advantage of the great computation potential of high-performance computing (HPC) clusters and meticulously designed algorithms, computational biology and AI can assist in understanding the data and discovering results that we have never seen before. However, the need for big data, the lack of explainability, and limited generalizability are among the most important issues that must be addressed.

This Special Issue welcomes studies on both applicational and methodological breakthroughs of computational biology and AI in the biomedical scenario.

Dr. Shuncong Wang
Dr. Peihua Zhao
Guest Editors

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Keywords

precision medicine
computational algorithm
multi-omics data
histopathological images
artificial intelligence

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Published Papers (11 papers)

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Research

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17 pages, 9137 KiB

Open AccessArticle

Utilizing Immunoinformatics for mRNA Vaccine Design against Influenza D Virus

by Elijah Kolawole Oladipo, Stephen Feranmi Adeyemo, Modinat Wuraola Akinboade, Temitope Michael Akinleye, Kehinde Favour Siyanbola, Precious Ayomide Adeogun, Victor Michael Ogunfidodo, Christiana Adewumi Adekunle, Olubunmi Ayobami Elutade, Esther Eghogho Omoathebu, Blessing Oluwatunmise Taiwo, Elizabeth Olawumi Akindiya, Lucy Ochola and Helen Onyeaka

BioMedInformatics 2024, 4(2), 1572-1588; https://doi.org/10.3390/biomedinformatics4020086 - 12 Jun 2024

Viewed by 1971

Abstract

Background: Influenza D Virus (IDV) presents a possible threat to animal and human health, necessitating the development of effective vaccines. Although no human illness linked to IDV has been reported, the possibility of human susceptibility to infection remains uncertain. Hence, there is a need for an animal vaccine to be designed. Such a vaccine will contribute to preventing and controlling IDV outbreaks and developing effective countermeasures against this emerging pathogen. This study, therefore, aimed to design an mRNA vaccine construct against IDV using immunoinformatic methods and evaluate its potential efficacy. Methods: A comprehensive methodology involving epitope prediction, vaccine construction, and structural analysis was employed. Viral sequences from six continents were collected and analyzed. A total of 88 Hemagglutinin Esterase Fusion (HEF) sequences from IDV isolates were obtained, of which 76 were identified as antigenic. Different bioinformatics tools were used to identify preferred CTL, HTL, and B-cell epitopes. The epitopes underwent thorough analysis, and those that can induce a lasting immunological response were selected for the construction. Results: The vaccine prototype comprised nine epitopes, an adjuvant, MHC I-targeting domain (MITD), Kozaq, 3′ UTR, 5′ UTR, and specific linkers. The mRNA vaccine construct exhibited antigenicity, non-toxicity, and non-allergenicity, with favourable physicochemical properties. The secondary and tertiary structure analyses revealed a stable and accurate vaccine construct. Molecular docking simulations also demonstrated strong binding affinity with toll-like receptors. Conclusions: The study provides a promising framework for developing an effective mRNA vaccine against IDV, highlighting its potential for mitigating the global impact of this viral infection. Further experimental studies are needed to confirm the vaccine’s efficacy and safety. Full article

(This article belongs to the Special Issue Computational Biology and Artificial Intelligence in Medicine)

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16 pages, 4106 KiB

Open AccessArticle

Advancing DNA Language Models through Motif-Oriented Pre-Training with MoDNA

by Weizhi An, Yuzhi Guo, Yatao Bian, Hehuan Ma, Jinyu Yang, Chunyuan Li and Junzhou Huang

BioMedInformatics 2024, 4(2), 1556-1571; https://doi.org/10.3390/biomedinformatics4020085 - 12 Jun 2024

Viewed by 1440

Abstract

Acquiring meaningful representations of gene expression is essential for the accurate prediction of downstream regulatory tasks, such as identifying promoters and transcription factor binding sites. However, the current dependency on supervised learning, constrained by the limited availability of labeled genomic data, impedes the ability to develop robust predictive models with broad generalization capabilities. In response, recent advancements have pivoted towards the application of self-supervised training for DNA sequence modeling, enabling the adaptation of pre-trained genomic representations to a variety of downstream tasks. Departing from the straightforward application of masked language learning techniques to DNA sequences, approaches such as MoDNA enrich genome language modeling with prior biological knowledge. In this study, we advance DNA language models by utilizing the Motif-oriented DNA (MoDNA) pre-training framework, which is established for self-supervised learning at the pre-training stage and is flexible enough for application across different downstream tasks. MoDNA distinguishes itself by efficiently learning semantic-level genomic representations from an extensive corpus of unlabeled genome data, offering a significant improvement in computational efficiency over previous approaches. The framework is pre-trained on a comprehensive human genome dataset and fine-tuned for targeted downstream tasks. Our enhanced analysis and evaluation in promoter prediction and transcription factor binding site prediction have further validated MoDNA’s exceptional capabilities, emphasizing its contribution to advancements in genomic predictive modeling. Full article

(This article belongs to the Special Issue Computational Biology and Artificial Intelligence in Medicine)

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23 pages, 8260 KiB

Open AccessArticle

Naturalize Revolution: Unprecedented AI-Driven Precision in Skin Cancer Classification Using Deep Learning

by Mohamad Abou Ali, Fadi Dornaika, Ignacio Arganda-Carreras, Hussein Ali and Malak Karaouni

BioMedInformatics 2024, 4(1), 638-660; https://doi.org/10.3390/biomedinformatics4010035 - 1 Mar 2024

Cited by 1 | Viewed by 2338

Abstract

Background: In response to the escalating global concerns surrounding skin cancer, this study aims to address the imperative for precise and efficient diagnostic methodologies. Focusing on the intricate task of eight-class skin cancer classification, the research delves into the limitations of conventional diagnostic approaches, often hindered by subjectivity and resource constraints. The transformative potential of Artificial Intelligence (AI) in revolutionizing diagnostic paradigms is underscored, emphasizing significant improvements in accuracy and accessibility. Methods: Utilizing cutting-edge deep learning models on the ISIC2019 dataset, a comprehensive analysis is conducted, employing a diverse array of pre-trained ImageNet architectures and Vision Transformer models. To counteract the inherent class imbalance in skin cancer datasets, a pioneering “Naturalize” augmentation technique is introduced. This technique leads to the creation of two indispensable datasets—the Naturalized 2.4K ISIC2019 and groundbreaking Naturalized 7.2K ISIC2019 datasets—catalyzing advancements in classification accuracy. The “Naturalize” augmentation technique involves the segmentation of skin cancer images using the Segment Anything Model (SAM) and the systematic addition of segmented cancer images to a background image to generate new composite images. Results: The research showcases the pivotal role of AI in mitigating the risks of misdiagnosis and under-diagnosis in skin cancer. The proficiency of AI in analyzing vast datasets and discerning subtle patterns significantly augments the diagnostic prowess of dermatologists. Quantitative measures such as confusion matrices, classification reports, and visual analyses using Score-CAM across diverse dataset variations are meticulously evaluated. The culmination of these endeavors resulted in an unprecedented achievement—100% average accuracy, precision, recall, and F1-score—within the groundbreaking Naturalized 7.2K ISIC2019 dataset. Conclusion: This groundbreaking exploration highlights the transformative capabilities of AI-driven methodologies in reshaping the landscape of skin cancer diagnosis and patient care. The research represents a pivotal stride towards redefining dermatological diagnosis, showcasing the remarkable impact of AI-powered solutions in surmounting the challenges inherent in skin cancer diagnosis. The attainment of 100% across crucial metrics within the Naturalized 7.2K ISIC2019 dataset serves as a testament to the transformative capabilities of AI-driven approaches in reshaping the trajectory of skin cancer diagnosis and patient care. This pioneering work paves the way for a new era in dermatological diagnostics, heralding the dawn of unprecedented precision and efficacy in the identification and classification of skin cancers. Full article

(This article belongs to the Special Issue Computational Biology and Artificial Intelligence in Medicine)

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25 pages, 3729 KiB

Open AccessArticle

Toward Cancer Chemoprevention: Mathematical Modeling of Chemically Induced Carcinogenesis and Chemoprevention

by Dimitrios G. Boucharas, Chryssa Anastasiadou, Spyridon Karkabounas, Efthimia Antonopoulou and George Manis

BioMedInformatics 2024, 4(1), 360-384; https://doi.org/10.3390/biomedinformatics4010021 - 2 Feb 2024

Viewed by 1440

Abstract

Cancer, which is currently rated as the second-leading cause of mortality across the globe, is one of the most hazardous disease groups that has plagued humanity for centuries. The experiments presented here span over two decades and were conducted on a specific species of mice, aiming to neutralize a highly carcinogenic agent by altering its chemical structure when combined with certain compounds. A plethora of growth models, each of which makes use of distinctive qualities, are utilized in the investigation and explanation of the phenomena of chemically induced oncogenesis and prevention. The analysis ultimately results in the formalization of the process of locating the growth model that provides the best descriptive power based on predefined criteria. This is accomplished through a methodological workflow that adopts a computational pipeline based on the Levenberg–Marquardt algorithm with pioneering and conventional metrics as well as a ruleset. The developed process simplifies the investigated phenomena as the parameter space of growth models is reduced. The predictability is proven strong in the near future (i.e., a 0.61% difference between the predicted and actual values). The parameters differentiate between active compounds (i.e., classification results reach up to 96% in sensitivity and other performance metrics). The distribution of parameter contribution complements the findings that the logistic growth model is the most appropriate (i.e., 44.47%). In addition, the dosage of chemicals is increased by a factor of two for the next round of trials, which exposes parallel behavior between the two dosages. As a consequence, the study reveals important information on chemoprevention and the cycles of cancer proliferation. If developed further, it might lead to the development of nutritional supplements that completely inhibit the expansion of cancerous tumors. The methodology provided can be used to describe other phenomena that progress over time and it has the power to estimate future results. Full article

(This article belongs to the Special Issue Computational Biology and Artificial Intelligence in Medicine)

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10 pages, 486 KiB

Open AccessArticle

Evaluation of Replies to Voice Queries in Gynecologic Oncology by Virtual Assistants Siri, Alexa, Google, and Cortana

by Jamie M. Land, Edward J. Pavlik, Elizabeth Ueland, Sara Ueland, Nicholas Per, Kristen Quick, Justin W. Gorski, McKayla J. Riggs, Megan L. Hutchcraft, Josie D. Llanora and Do Hyun Yun

BioMedInformatics 2023, 3(3), 553-562; https://doi.org/10.3390/biomedinformatics3030038 - 11 Jul 2023

Cited by 3 | Viewed by 1909

Abstract

Women that receive news that they have a malignancy of gynecologic origin can have questions about their diagnosis. These questions might be posed as voice queries to the virtual assistants Siri, Alexa, Google, and Cortana. Because our world has increasingly adopted smart phones and standalone voice query devices, this study focused on the accuracy of audible replies by the virtual assistants (VAs) Siri, Alexa, Google, and Cortana to voice queries related to gynecologic oncology. Twenty-one evaluators analyzed VA audible answers to select voice queries related to gynecologic oncology. Questions were posed in three different ways for each voice query in order to maximize the likelihood of acceptability to the VAs in a 24-question panel. For general queries that were not related to gynecologic oncology, Google provided the most correct audible replies (83.3% correct), followed by Alexa (66.7% correct), Siri (45.8% correct), and Cortana (20.8% correct). For gynecologic oncology-related queries, the accuracy of the VAs was considerably lower: Google provided the most correct audible replies (18.1%), followed by Alexa (6.5%), Siri (5.5%), and Cortana (2.3%). There was a considerable drop in the accuracy of audible replies to oral queries on topics in gynecologic oncology relative to general queries that were not related to gynecologic oncology. There is considerable room for improvement in VA performance, so that caution is advised when using VAs for medical queries in gynecologic oncology. Our specific findings related to gynecologic oncology extend the work of others with regard to the low usability of general medical information obtained from VAs, so that reliance on conversational assistants for actionable medical information represents a safety risk for patients and consumers. Full article

(This article belongs to the Special Issue Computational Biology and Artificial Intelligence in Medicine)

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10 pages, 1485 KiB

Open AccessArticle

Reliability of Artificial Intelligence-Assisted Cephalometric Analysis. A Pilot Study

by Anna Alessandri-Bonetti, Linda Sangalli, Martina Salerno and Patrizia Gallenzi

BioMedInformatics 2023, 3(1), 44-53; https://doi.org/10.3390/biomedinformatics3010003 - 10 Jan 2023

Cited by 3 | Viewed by 3485

Abstract

Recently, Artificial Intelligence (AI) has spread in orthodontics, in particular within cephalometric analysis, where computerized digital software is able to provide linear-angular measurements upon manual landmark identification. A step forward is constituted by fully automated AI-assisted cephalometric analysis, where the landmarks are automatically detected by software. The aim of the study was to compare the reliability of a fully automated AI-assisted cephalometric analysis with the one obtained by a computerized digital software upon manual landmark identification. Fully automated AI-assisted cephalometric analysis of 13 lateral cephalograms were retrospectively compared to the cephalometric analysis performed twice by a blinded operator with a computerized software. Intra- and inter-operator (fully automated AI-assisted vs. computerized software with manual landmark identification) reliability in cephalometric parameters (maxillary convexity, facial conicity, facial axis angle, posterior and lower facial height) was tested with the Dahlberg equation and Bland–Altman plot. The results revealed no significant difference in intra- and inter-operator measurements. Although not significant, higher errors were observed within intra-operator measurements of posterior facial height and inter-operator measurements of facial axis angle. In conclusion, despite the small sample, the cephalometric measurements of a fully automated AI-assisted cephalometric software were reliable and accurate. Nevertheless, digital technological advances cannot substitute the critical role of the orthodontist toward a correct diagnosis. Full article

(This article belongs to the Special Issue Computational Biology and Artificial Intelligence in Medicine)

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Review

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21 pages, 1557 KiB

Open AccessReview

Perspectives on Resolving Diagnostic Challenges between Myocardial Infarction and Takotsubo Cardiomyopathy Leveraging Artificial Intelligence

by Serin Moideen Sheriff, Aaftab Sethi, Divyanshi Sood, Sourav Bansal, Aastha Goudel, Manish Murlidhar, Devanshi N. Damani, Kanchan Kulkarni and Shivaram P. Arunachalam

BioMedInformatics 2024, 4(2), 1308-1328; https://doi.org/10.3390/biomedinformatics4020072 - 13 May 2024

Viewed by 1227

Abstract

Background: cardiovascular diseases, including acute myocardial infarction (AMI) and takotsubo cardiomyopathy (TTC), are significant causes of morbidity and mortality worldwide. Timely differentiation of these conditions is essential for effective patient management and improved outcomes. Methods: We conducted a review focusing on studies that applied artificial intelligence (AI) techniques to differentiate between acute myocardial infarction (AMI) and takotsubo cardiomyopathy (TTC). Inclusion criteria comprised studies utilizing various AI modalities, such as deep learning, ensemble methods, or other machine learning techniques, for discrimination between AMI and TTC. Additionally, studies employing imaging techniques, including echocardiography, cardiac magnetic resonance imaging, and coronary angiography, for cardiac disease diagnosis were considered. Publications included were limited to those available in peer-reviewed journals. Exclusion criteria were applied to studies not relevant to the discrimination between AMI and TTC, lacking detailed methodology or results pertinent to the AI application in cardiac disease diagnosis, not utilizing AI modalities or relying solely on invasive techniques for differentiation between AMI and TTC, and non-English publications. Results: The strengths and limitations of AI-based approaches are critically evaluated, including factors affecting performance, such as reliability and generalizability. The review delves into challenges associated with model interpretability, ethical implications, patient perspectives, and inconsistent image quality due to manual dependency, highlighting the need for further research. Conclusions: This review article highlights the promising advantages of AI technologies in distinguishing AMI from TTC, enabling early diagnosis and personalized treatments. However, extensive validation and real-world implementation are necessary before integrating AI tools into routine clinical practice. It is vital to emphasize that while AI can efficiently assist, it cannot entirely replace physicians. Collaborative efforts among clinicians, researchers, and AI experts are essential to unlock the potential of these transformative technologies fully. Full article

(This article belongs to the Special Issue Computational Biology and Artificial Intelligence in Medicine)

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16 pages, 918 KiB

Open AccessReview

Generative Pre-Trained Transformer-Empowered Healthcare Conversations: Current Trends, Challenges, and Future Directions in Large Language Model-Enabled Medical Chatbots

by James C. L. Chow, Valerie Wong and Kay Li

BioMedInformatics 2024, 4(1), 837-852; https://doi.org/10.3390/biomedinformatics4010047 - 14 Mar 2024

Cited by 9 | Viewed by 5591

Abstract

This review explores the transformative integration of artificial intelligence (AI) and healthcare through conversational AI leveraging Natural Language Processing (NLP). Focusing on Large Language Models (LLMs), this paper navigates through various sections, commencing with an overview of AI’s significance in healthcare and the role of conversational AI. It delves into fundamental NLP techniques, emphasizing their facilitation of seamless healthcare conversations. Examining the evolution of LLMs within NLP frameworks, the paper discusses key models used in healthcare, exploring their advantages and implementation challenges. Practical applications in healthcare conversations, from patient-centric utilities like diagnosis and treatment suggestions to healthcare provider support systems, are detailed. Ethical and legal considerations, including patient privacy, ethical implications, and regulatory compliance, are addressed. The review concludes by spotlighting current challenges, envisaging future trends, and highlighting the transformative potential of LLMs and NLP in reshaping healthcare interactions. Full article

(This article belongs to the Special Issue Computational Biology and Artificial Intelligence in Medicine)

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34 pages, 3584 KiB

Open AccessReview

Development and Practical Applications of Computational Intelligence Technology

by Yasunari Matsuzaka and Ryu Yashiro

BioMedInformatics 2024, 4(1), 566-599; https://doi.org/10.3390/biomedinformatics4010032 - 22 Feb 2024

Viewed by 1227

Abstract

Computational intelligence (CI) uses applied computational methods for problem-solving inspired by the behavior of humans and animals. Biological systems are used to construct software to solve complex problems, and one type of such system is an artificial immune system (AIS), which imitates the immune system of a living body. AISs have been used to solve problems that require identification and learning, such as computer virus identification and removal, image identification, and function optimization problems. In the body’s immune system, a wide variety of cells work together to distinguish between the self and non-self and to eliminate the non-self. AISs enable learning and discrimination by imitating part or all of the mechanisms of a living body’s immune system. Certainly, some deep neural networks have exceptional performance that far surpasses that of humans in certain tasks, but to build such a network, a huge amount of data is first required. These networks are used in a wide range of applications, such as extracting knowledge from a large amount of data, learning from past actions, and creating the optimal solution (the optimization problem). A new technique for pre-training natural language processing (NLP) software ver.9.1by using transformers called Bidirectional Encoder Representations (BERT) builds on recent research in pre-training contextual representations, including Semi-Supervised Sequence Learning, Generative Pre-Training, ELMo (Embeddings from Language Models), which is a method for obtaining distributed representations that consider context, and ULMFit (Universal Language Model Fine-Tuning). BERT is a method that can address the issue of the need for large amounts of data, which is inherent in large-scale models, by using pre-learning with unlabeled data. An optimization problem involves “finding a solution that maximizes or minimizes an objective function under given constraints”. In recent years, machine learning approaches that consider pattern recognition as an optimization problem have become popular. This pattern recognition is an operation that associates patterns observed as spatial and temporal changes in signals with classes to which they belong. It involves identifying and retrieving predetermined features and rules from data; however, the features and rules here are not logical information, but are found in images, sounds, etc. Therefore, pattern recognition is generally conducted by supervised learning. Based on a new theory that deals with the process by which the immune system learns from past infection experiences, the clonal selection of immune cells can be viewed as a learning rule of reinforcement learning. Full article

(This article belongs to the Special Issue Computational Biology and Artificial Intelligence in Medicine)

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11 pages, 632 KiB

Open AccessReview

Artificial Intelligence in Bladder Cancer Diagnosis: Current Applications and Future Perspectives

by Giulio Rossin, Federico Zorzi, Luca Ongaro, Andrea Piasentin, Francesca Vedovo, Giovanni Liguori, Alessandro Zucchi, Alchiede Simonato, Riccardo Bartoletti, Carlo Trombetta, Nicola Pavan and Francesco Claps

BioMedInformatics 2023, 3(1), 104-114; https://doi.org/10.3390/biomedinformatics3010008 - 1 Feb 2023

Cited by 3 | Viewed by 3824

Abstract

Bladder cancer (BCa) is one of the most diagnosed urological malignancies. A timely and accurate diagnosis is crucial at the first assessment as well as at the follow up after curative treatments. Moreover, in the era of precision medicine, proper molecular characterization and pathological evaluation are key drivers of a patient-tailored management. However, currently available diagnostic tools still suffer from significant operator-dependent variability. To fill this gap, physicians have shown a constantly increasing interest towards new resources able to enhance diagnostic performances. In this regard, several reports have highlighted how artificial intelligence (AI) can produce promising results in the BCa field. In this narrative review, we aimed to analyze the most recent literature exploring current experiences and future perspectives on the role of AI in the BCa scenario. We summarized the most recently investigated applications of AI in BCa management, focusing on how this technology could impact physicians’ accuracy in three widespread diagnostic areas: cystoscopy, clinical tumor (cT) staging, and pathological diagnosis. Our results showed the wide potential of AI in BCa, although larger prospective and well-designed trials are pending to draw definitive conclusions allowing AI to be routinely applied to everyday clinical practice. Full article

(This article belongs to the Special Issue Computational Biology and Artificial Intelligence in Medicine)

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Other

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7 pages, 1882 KiB

Open AccessBrief Report

Immediate Autogenous Bone Transplantation Using a Novel Kinetic Bioactive Screw 3D Design as a Dental Implant

by Carlos Aurelio Andreucci, Elza M. M. Fonseca and Renato N. Jorge

BioMedInformatics 2023, 3(2), 299-305; https://doi.org/10.3390/biomedinformatics3020020 - 6 Apr 2023

Cited by 4 | Viewed by 2253

Abstract

The restoration of osseous defects is accomplished by bone grafts and bone substitutes, which are also called biomaterials. Autogenous grafts, which are derived from the same individual, can retain the viability of cells, mainly the osteoblasts and osteoprogenitor stem cells, and they do not lead to an immunologic response, which is known as the gold standard for bone grafts. There are both different techniques and devices that can be used to obtain bone grafts according to the needs of the patients, the location, and the size of the bone defect. Here, an innovative technique is presented in which the patient’s own bone is removed from the trigone retromolar region of the mandible and is inserted into a dental alveolus after the extraction and immediate insertion of an innovative dental implant, the BKS. The first step of the technique creates the surgical alveolus; the second step perforates the BKS in the retromolar region, and shortly after, the BKS containing the bone to be grafted is removed; the third step screws the BKS bone that collects in the created surgical alveolus. Experimental studies have shown the feasibility and practicality of this new technique and the new dental implant model for autogenous transplants. Full article

(This article belongs to the Special Issue Computational Biology and Artificial Intelligence in Medicine)

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