推进乳腺癌药物输送:生物信息学和人工智能的变革潜力

Dilpreet Singh, Diksha Sachdeva, Lovedeep Singh
{"title":"推进乳腺癌药物输送:生物信息学和人工智能的变革潜力","authors":"Dilpreet Singh, Diksha Sachdeva, Lovedeep Singh","doi":"10.2174/0115733947287709240229104857","DOIUrl":null,"url":null,"abstract":"\n\nBreast cancer remains a significant global health challenge, necessitating innovative approaches\nto improve treatment efficacy while minimizing side effects. This review explores the promising\nadvancements in breast cancer drug delivery driven by the transformative potential of bioinformatics\nand Artificial Intelligence (AI). Bioinformatics plays a pivotal role in unraveling the intricate\ngenomic landscape of breast cancer, enabling the identification of potential drug targets and biomarkers.\nThe integration of multi-omics data facilitates a comprehensive understanding of the disease,\nguiding personalized treatment strategies. Moreover, bioinformatics-driven approaches aid in\nbiomarker discovery and prediction, offering novel tools for prognosis and treatment response assessment.\nAI, particularly machine learning and deep learning, has revolutionized breast cancer research.\nMachine learning models empower accurate diagnosis through image analysis, improve survival\nprediction, and enhance risk assessment. Deep learning algorithms, such as convolutional neural\nnetworks, enable precise tumor detection and classification from medical imaging data, notably\nmammograms and MRI scans. Additionally, natural language processing techniques facilitate the\nmining of vast scientific literature, uncovering hidden insights and identifying potential drug targets.\nNetwork-based approaches integrated with AI algorithms facilitate the identification of central proteins\nas promising drug targets within complex biological networks. This review also examines AIoptimized\nnanoformulations designed to enhance targeted drug delivery. AI-guided design of drugloaded\nnanoparticles improves drug encapsulation efficiency, release kinetics, and site-specific delivery,\noffering promising solutions to overcome the challenges of conventional drug delivery.\n","PeriodicalId":503819,"journal":{"name":"Current Cancer Therapy Reviews","volume":"22 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Advancing Breast Cancer Drug Delivery: The Transformative Potential of\\nBioinformatics and Artificial Intelligence\",\"authors\":\"Dilpreet Singh, Diksha Sachdeva, Lovedeep Singh\",\"doi\":\"10.2174/0115733947287709240229104857\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n\\nBreast cancer remains a significant global health challenge, necessitating innovative approaches\\nto improve treatment efficacy while minimizing side effects. This review explores the promising\\nadvancements in breast cancer drug delivery driven by the transformative potential of bioinformatics\\nand Artificial Intelligence (AI). Bioinformatics plays a pivotal role in unraveling the intricate\\ngenomic landscape of breast cancer, enabling the identification of potential drug targets and biomarkers.\\nThe integration of multi-omics data facilitates a comprehensive understanding of the disease,\\nguiding personalized treatment strategies. Moreover, bioinformatics-driven approaches aid in\\nbiomarker discovery and prediction, offering novel tools for prognosis and treatment response assessment.\\nAI, particularly machine learning and deep learning, has revolutionized breast cancer research.\\nMachine learning models empower accurate diagnosis through image analysis, improve survival\\nprediction, and enhance risk assessment. Deep learning algorithms, such as convolutional neural\\nnetworks, enable precise tumor detection and classification from medical imaging data, notably\\nmammograms and MRI scans. Additionally, natural language processing techniques facilitate the\\nmining of vast scientific literature, uncovering hidden insights and identifying potential drug targets.\\nNetwork-based approaches integrated with AI algorithms facilitate the identification of central proteins\\nas promising drug targets within complex biological networks. This review also examines AIoptimized\\nnanoformulations designed to enhance targeted drug delivery. AI-guided design of drugloaded\\nnanoparticles improves drug encapsulation efficiency, release kinetics, and site-specific delivery,\\noffering promising solutions to overcome the challenges of conventional drug delivery.\\n\",\"PeriodicalId\":503819,\"journal\":{\"name\":\"Current Cancer Therapy Reviews\",\"volume\":\"22 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-03-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Current Cancer Therapy Reviews\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2174/0115733947287709240229104857\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Cancer Therapy Reviews","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2174/0115733947287709240229104857","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

乳腺癌仍然是一项重大的全球健康挑战,需要采用创新方法来提高治疗效果,同时最大限度地减少副作用。本综述探讨了在生物信息学和人工智能(AI)变革潜力的推动下,乳腺癌给药领域取得的令人鼓舞的进展。生物信息学在揭示乳腺癌的内部基因组图谱、确定潜在药物靶点和生物标志物方面发挥着关键作用。多组学数据的整合有助于全面了解疾病,指导个性化治疗策略。此外,生物信息学驱动的方法有助于生物标记物的发现和预测,为预后和治疗反应评估提供了新的工具。人工智能,尤其是机器学习和深度学习,已经彻底改变了乳腺癌研究。深度学习算法,如卷积神经网络,能够从医学影像数据,特别是乳房X光片和核磁共振扫描中精确检测肿瘤并进行分类。此外,自然语言处理技术有助于对浩瀚的科学文献进行分析,发掘隐藏的见解并确定潜在的药物靶点。本综述还探讨了旨在加强靶向给药的人工智能优化纳米制剂。人工智能指导下的载药纳米颗粒设计提高了药物封装效率、释放动力学和特定位点给药,为克服传统给药方式的挑战提供了有前景的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Advancing Breast Cancer Drug Delivery: The Transformative Potential of Bioinformatics and Artificial Intelligence
Breast cancer remains a significant global health challenge, necessitating innovative approaches to improve treatment efficacy while minimizing side effects. This review explores the promising advancements in breast cancer drug delivery driven by the transformative potential of bioinformatics and Artificial Intelligence (AI). Bioinformatics plays a pivotal role in unraveling the intricate genomic landscape of breast cancer, enabling the identification of potential drug targets and biomarkers. The integration of multi-omics data facilitates a comprehensive understanding of the disease, guiding personalized treatment strategies. Moreover, bioinformatics-driven approaches aid in biomarker discovery and prediction, offering novel tools for prognosis and treatment response assessment. AI, particularly machine learning and deep learning, has revolutionized breast cancer research. Machine learning models empower accurate diagnosis through image analysis, improve survival prediction, and enhance risk assessment. Deep learning algorithms, such as convolutional neural networks, enable precise tumor detection and classification from medical imaging data, notably mammograms and MRI scans. Additionally, natural language processing techniques facilitate the mining of vast scientific literature, uncovering hidden insights and identifying potential drug targets. Network-based approaches integrated with AI algorithms facilitate the identification of central proteins as promising drug targets within complex biological networks. This review also examines AIoptimized nanoformulations designed to enhance targeted drug delivery. AI-guided design of drugloaded nanoparticles improves drug encapsulation efficiency, release kinetics, and site-specific delivery, offering promising solutions to overcome the challenges of conventional drug delivery.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信