BioMedInformatics最新文献_第10页

Machine Learning Analysis of Genomic Factors Influencing Hyperbaric Oxygen Therapy in Parkinson’s Disease 影响帕金森病高压氧疗法的基因组因素的机器学习分析

BioMedInformatics Pub Date : 2024-01-09 DOI: 10.3390/biomedinformatics4010009

Eirini Banou, Aristidis G. Vrahatis, Marios G. Krokidis, Vlamos

{"title":"Machine Learning Analysis of Genomic Factors Influencing Hyperbaric Oxygen Therapy in Parkinson’s Disease","authors":"Eirini Banou, Aristidis G. Vrahatis, Marios G. Krokidis, Vlamos","doi":"10.3390/biomedinformatics4010009","DOIUrl":"https://doi.org/10.3390/biomedinformatics4010009","url":null,"abstract":"(1) Background: Parkinson’s disease (PD) is a progressively worsening neurodegenerative disorder affecting movement, mental well-being, sleep, and pain. While no cure exists, treatments like hyperbaric oxygen therapy (HBOT) offer potential relief. However, the molecular biology perspective, especially when intertwined with machine learning dynamics, remains underexplored. (2) Methods: We employed machine learning techniques to analyze single-cell RNA-seq data from human PD cell samples. This approach aimed to identify pivotal genes associated with PD and understand their relationship with HBOT. (3) Results: Our analysis indicated genes such as MAP2, CAP2, and WSB1, among others, as being crucially linked with Parkinson’s disease (PD) and showed their significant correlation with Hyperbaric oxygen therapy (HBOT) indicatively. This suggests that certain genomic factors might influence the efficacy of HBOT in PD treatment. (4) Conclusions: HBOT presents promising therapeutic potential for Parkinson’s disease, with certain genomic factors playing a pivotal role in its efficacy. Our findings emphasize the need for further machine learning-driven research harnessing diverse omics data to better understand and treat PD.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":"38 30","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139442888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Limitations of Protein Structure Prediction Algorithms in Therapeutic Protein Development 治疗性蛋白质开发中蛋白质结构预测算法的局限性

BioMedInformatics Pub Date : 2024-01-08 DOI: 10.3390/biomedinformatics4010007

Sarfaraz K. Niazi, Zamara Mariam, R. Z. Paracha

{"title":"Limitations of Protein Structure Prediction Algorithms in Therapeutic Protein Development","authors":"Sarfaraz K. Niazi, Zamara Mariam, R. Z. Paracha","doi":"10.3390/biomedinformatics4010007","DOIUrl":"https://doi.org/10.3390/biomedinformatics4010007","url":null,"abstract":"The three-dimensional protein structure is pivotal in comprehending biological phenomena. It directly governs protein function and hence aids in drug discovery. The development of protein prediction algorithms, such as AlphaFold2, ESMFold, and trRosetta, has given much hope in expediting protein-based therapeutic discovery. Though no study has reported a conclusive application of these algorithms, the efforts continue with much optimism. We intended to test the application of these algorithms in rank-ordering therapeutic proteins for their instability during the pre-translational modification stages, as may be predicted according to the confidence of the structure predicted by these algorithms. The selected molecules were based on a harmonized category of licensed therapeutic proteins; out of the 204 licensed products, 188 that were not conjugated were chosen for analysis, resulting in a lack of correlation between the confidence scores and structural or protein properties. It is crucial to note here that the predictive accuracy of these algorithms is contingent upon the presence of the known structure of the protein in the accessible database. Consequently, our conclusion emphasizes that these algorithms primarily replicate information derived from existing structures. While our findings caution against relying on these algorithms for drug discovery purposes, we acknowledge the need for a nuanced interpretation. Considering their limitations and recognizing that their utility may be constrained to scenarios where known structures are available is important. Hence, caution is advised when applying these algorithms to characterize various attributes of therapeutic proteins without the support of adequate structural information. It is worth noting that the two main algorithms, AlfphaFold2 and ESMFold, also showed a 72% correlation in their scores, pointing to similar limitations. While much progress has been made in computational sciences, the Levinthal paradox remains unsolved.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":"53 26","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139447275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interpretable Medical Imagery Diagnosis with Self-Attentive Transformers: A Review of Explainable AI for Health Care 利用自适应变压器进行可解释医学影像诊断：医疗保健领域可解释人工智能综述

BioMedInformatics Pub Date : 2024-01-08 DOI: 10.3390/biomedinformatics4010008

Tin Lai

引用次数: 0

Biomedical Informatics: State of the Art, Challenges, and Opportunities 生物医学信息学：技术现状、挑战和机遇

BioMedInformatics Pub Date : 2024-01-02 DOI: 10.3390/biomedinformatics4010006

Carson K. Leung

引用次数: 0

The Bioinformatics Identification of Potential Protein Glycosylation Genes Associated with a Glioma Stem Cell Signature 通过生物信息学鉴定与胶质瘤干细胞特征相关的潜在蛋白糖基化基因

BioMedInformatics Pub Date : 2024-01-01 DOI: 10.3390/biomedinformatics4010005

Kazuya Tokumura, Koki Sadamori, Makoto Yoshimoto, Akane Tomizawa, Yuki Tanaka, Kazuya Fukasawa, E. Hinoi

{"title":"The Bioinformatics Identification of Potential Protein Glycosylation Genes Associated with a Glioma Stem Cell Signature","authors":"Kazuya Tokumura, Koki Sadamori, Makoto Yoshimoto, Akane Tomizawa, Yuki Tanaka, Kazuya Fukasawa, E. Hinoi","doi":"10.3390/biomedinformatics4010005","DOIUrl":"https://doi.org/10.3390/biomedinformatics4010005","url":null,"abstract":"Glioma stem cells (GSCs) contribute to the pathogenesis of glioblastoma (GBM), which is the most malignant form of glioma. The implications and underlying mechanisms of protein glycosylation in GSC phenotypes and GBM malignancy are not fully understood. The implication of protein glycosylation and the corresponding candidate genes on the stem cell properties of GSCs and poor clinical outcomes in GBM were investigated, using datasets from the Gene Expression Omnibus, The Cancer Genome Atlas, and the Chinese Glioma Genome Atlas, accompanied by biological validation in vitro. N-linked glycosylation was significantly associated with GSC properties and the prognosis of GBM in the integrated bioinformatics analyses of clinical specimens. N-linked glycosylation was associated with the glioma grade, molecular biomarkers, and molecular subtypes. The expression levels of the asparagine-linked glycosylation (ALG) enzyme family, which is essential for the early steps in the biosynthesis of N-glycans, were prominently associated with GSC properties and poor survival in patients with GBM with high stem-cell properties. Finally, the oxidative phosphorylation pathway was primarily enriched in GSCs with a high expression of the ALG enzyme family. These findings suggest the role of N-linked glycosylation in the regulation of GSC phenotypes and GBM malignancy.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":" 79","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139391980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Survey of Multimodal Medical Question Answering 多模态医学问题解答调查

BioMedInformatics Pub Date : 2023-12-31 DOI: 10.3390/biomedinformatics4010004

Hilmi Demirhan, Wlodek Zadrozny

{"title":"Survey of Multimodal Medical Question Answering","authors":"Hilmi Demirhan, Wlodek Zadrozny","doi":"10.3390/biomedinformatics4010004","DOIUrl":"https://doi.org/10.3390/biomedinformatics4010004","url":null,"abstract":"Multimodal medical question answering (MMQA) is a vital area bridging healthcare and Artificial Intelligence (AI). This survey methodically examines the MMQA research published in recent years. We collect academic literature through Google Scholar, applying bibliometric analysis to the publications and datasets used in these studies. Our analysis uncovers the increasing interest in MMQA over time, with diverse domains such as natural language processing, computer vision, and large language models contributing to the research. The AI methods used in multimodal question answering in the medical domain are a prominent focus, accompanied by applicability of MMQA to the medical field. MMQA in the medical field has its unique challenges due to the sensitive nature of medicine as a science dealing with human health. The survey reveals MMQA research to be in an exploratory stage, discussing different methods, datasets, and potential business models. Future research is expected to focus on application development by big tech companies, such as MedPalm. The survey aims to provide insights into the current state of multimodal medical question answering, highlighting the growing interest from academia and industry. The identified research gaps and trends will guide future investigations and encourage collaborative efforts to advance this transformative field.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":"87 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139132249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Supporting the Demand on Mental Health Services with AI-Based Conversational Large Language Models (LLMs) 利用基于人工智能的大语言对话模型（LLMs）支持心理健康服务需求

BioMedInformatics Pub Date : 2023-12-22 DOI: 10.3390/biomedinformatics4010002

Tin Lai, Yukun Shi, Zicong Du, Jiajie Wu, Ken Fu, Yichao Dou, Ziqi Wang

{"title":"Supporting the Demand on Mental Health Services with AI-Based Conversational Large Language Models (LLMs)","authors":"Tin Lai, Yukun Shi, Zicong Du, Jiajie Wu, Ken Fu, Yichao Dou, Ziqi Wang","doi":"10.3390/biomedinformatics4010002","DOIUrl":"https://doi.org/10.3390/biomedinformatics4010002","url":null,"abstract":"The demand for psychological counselling has grown significantly in recent years, particularly with the global outbreak of COVID-19, which heightened the need for timely and professional mental health support. Online psychological counselling emerged as the predominant mode of providing services in response to this demand. In this study, we propose the Psy-LLM framework, an AI-based assistive tool leveraging large language models (LLMs) for question answering in psychological consultation settings to ease the demand on mental health professions. Our framework combines pre-trained LLMs with real-world professional questions-and-answers (Q&A) from psychologists and extensively crawled psychological articles. The Psy-LLM framework serves as a front-end tool for healthcare professionals, allowing them to provide immediate responses and mindfulness activities to alleviate patient stress. Additionally, it functions as a screening tool to identify urgent cases requiring further assistance. We evaluated the framework using intrinsic metrics, such as perplexity, and extrinsic evaluation metrics, including human participant assessments of response helpfulness, fluency, relevance, and logic. The results demonstrate the effectiveness of the Psy-LLM framework in generating coherent and relevant answers to psychological questions. This article discusses the potential and limitations of using large language models to enhance mental health support through AI technologies.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":"5 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138944737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

BioMedInformatics, the Link between Biomedical Informatics, Biology and Computational Medicine 生物医学信息学，生物医学、生物学和计算医学之间的纽带

BioMedInformatics Pub Date : 2023-12-21 DOI: 10.3390/biomedinformatics4010001

Alexandre G. de Brevern

引用次数: 0

Transforming Drug Design: Innovations in Computer-Aided Discovery for Biosimilar Agents 变革药物设计：生物仿制药的计算机辅助发现创新

BioMedInformatics Pub Date : 2023-12-08 DOI: 10.3390/biomedinformatics3040070

Shadi Askari, Alireza Ghofrani, Hamed Taherdoost

{"title":"Transforming Drug Design: Innovations in Computer-Aided Discovery for Biosimilar Agents","authors":"Shadi Askari, Alireza Ghofrani, Hamed Taherdoost","doi":"10.3390/biomedinformatics3040070","DOIUrl":"https://doi.org/10.3390/biomedinformatics3040070","url":null,"abstract":"In pharmaceutical research and development, pursuing novel therapeutics and optimizing existing drugs have been revolutionized by the fusion of cutting-edge technologies and computational methodologies. Over the past few decades, the field of drug design has undergone a remarkable transformation, catalyzed by the rapid advancement of computer-aided discovery techniques and the emergence of biosimilar agents. This dynamic interplay between scientific innovation and technological prowess has expedited the drug discovery process and paved the way for more targeted, effective, and personalized treatment approaches. This review investigates the transformative computer-aided discovery techniques for biosimilar agents in reshaping drug design. It examines how computational methods expedite drug candidate identification and explores the rise of cost-effective biosimilars as alternatives to biologics. Through this analysis, this study highlights the potential of these innovations to enhance the efficiency and accessibility of pharmaceutical development. It represents a pioneering effort to examine how computer-aided discovery is revolutionizing biosimilar agent development, exploring its applications, challenges, and prospects.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":"59 38","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138587907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Genomics for Emerging Pathogen Identification and Monitoring: Prospects and Obstacles 新病原体鉴定和监测的基因组学：前景与障碍

BioMedInformatics Pub Date : 2023-12-07 DOI: 10.3390/biomedinformatics3040069

Vishakha Vashisht, A. Vashisht, A. Mondal, Jaspreet Farmaha, Ahmet Alptekin, Harmanpreet Singh, P. Ahluwalia, Anaka Srinivas, R. Kolhe

{"title":"Genomics for Emerging Pathogen Identification and Monitoring: Prospects and Obstacles","authors":"Vishakha Vashisht, A. Vashisht, A. Mondal, Jaspreet Farmaha, Ahmet Alptekin, Harmanpreet Singh, P. Ahluwalia, Anaka Srinivas, R. Kolhe","doi":"10.3390/biomedinformatics3040069","DOIUrl":"https://doi.org/10.3390/biomedinformatics3040069","url":null,"abstract":"Emerging infectious diseases (EIDs) pose an increasingly significant global burden, driven by urbanization, population explosion, global travel, changes in human behavior, and inadequate public health systems. The recent SARS-CoV-2 pandemic highlights the urgent need for innovative and robust technologies to effectively monitor newly emerging pathogens. Rapid identification, epidemiological surveillance, and transmission mitigation are crucial challenges for ensuring public health safety. Genomics has emerged as a pivotal tool in public health during pandemics, enabling the diagnosis, management, and prediction of infections, as well as the analysis and identification of cross-species interactions and the categorization of infectious agents. Recent advancements in high-throughput DNA sequencing tools have facilitated rapid and precise identification and characterization of emerging pathogens. This review article provides insights into the latest advances in various genomic techniques for pathogen detection and tracking and their applications in global outbreak surveillance. We assess methods that leverage pathogen sequences and explore the role of genomic analysis in understanding the epidemiology of newly emerged infectious diseases. Additionally, we address technical challenges and limitations, ethical and legal considerations, and highlight opportunities for integrating genomics with other surveillance approaches. By delving into the prospects and obstacles of genomics, we can gain valuable insights into its role in mitigating the threats posed by emerging pathogens and improving global preparedness in the face of future outbreaks.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":"11 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138591918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0