Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 171, P. 108129 - 108129
Published: Feb. 7, 2024
Language: Английский
Future Internet, Journal Year: 2023, Volume and Issue: 15(8), P. 260 - 260
Published: July 31, 2023
Generative artificial intelligence (AI) has emerged as a powerful technology with numerous applications in various domains. There is need to identify the requirements and evaluation metrics for generative AI models designed specific tasks. The purpose of research aims investigate fundamental aspects systems, including their requirements, models, input–output formats, metrics. study addresses key questions presents comprehensive insights guide researchers, developers, practitioners field. Firstly, necessary implementing systems are examined categorized into three distinct categories: hardware, software, user experience. Furthermore, explores different types described literature by presenting taxonomy based on architectural characteristics, such variational autoencoders (VAEs), adversarial networks (GANs), diffusion transformers, language normalizing flow hybrid models. A classification input output formats used also provided. Moreover, proposes system discusses commonly AI. findings contribute advancements field, enabling effectively implement evaluate applications. significance lies understanding that crucial effective planning, design, optimal performance. aids selecting suitable options driving advancements. Classifying enables leveraging diverse customized while establish standardized methods assess model quality
Language: Английский
Citations
245
Journal of Medical Internet Research, Journal Year: 2024, Volume and Issue: 26, P. e53008 - e53008
Published: March 8, 2024
As advances in artificial intelligence (AI) continue to transform and revolutionize the field of medicine, understanding potential uses generative AI health care becomes increasingly important. Generative AI, including models such as adversarial networks large language models, shows promise transforming medical diagnostics, research, treatment planning, patient care. However, these data-intensive systems pose new threats protected information. This Viewpoint paper aims explore various categories care, drug discovery, virtual assistants, clinical decision support, while identifying security privacy within each phase life cycle (ie, data collection, model development, implementation phases). The objectives this study were analyze current state identify opportunities challenges posed by integrating technologies into existing infrastructure, propose strategies for mitigating risks. highlights importance addressing associated with ensure safe effective use systems. findings can inform development future help organizations better understand benefits risks By examining cases across diverse domains contributes theoretical discussions surrounding ethics, vulnerabilities, regulations. In addition, provides practical insights stakeholders looking adopt solutions their organizations.
Language: Английский
Citations
98
IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 69812 - 69837
Published: Jan. 1, 2024
Language: Английский
Citations
70
Journal of Cheminformatics, Journal Year: 2024, Volume and Issue: 16(1)
Published: Feb. 21, 2024
REINVENT 4 is a modern open-source generative AI framework for the design of small molecules. The software utilizes recurrent neural networks and transformer architectures to drive molecule generation. These generators are seamlessly embedded within general machine learning optimization algorithms, transfer learning, reinforcement curriculum learning. enables facilitates de novo design, R-group replacement, library linker scaffold hopping optimization. This contribution gives an overview describes its design. Algorithms their applications discussed in detail. command line tool which reads user configuration either TOML or JSON format. aim this release provide reference implementations some most common algorithms based An additional goal with create education future innovation molecular available from https://github.com/MolecularAI/REINVENT4 released under permissive Apache 2.0 license. Scientific contribution. provides implementation where also being used production support in-house drug discovery projects. publication one code full documentation thereof will increase transparency foster innovation, collaboration education.
Language: Английский
Citations
58
Diagnostics, Journal Year: 2023, Volume and Issue: 13(12), P. 1995 - 1995
Published: June 7, 2023
Artificial intelligence (AI) plays a more and important role in our everyday life due to the advantages that it brings when used, such as 24/7 availability, very low percentage of errors, ability provide real time insights, or performing fast analysis. AI is increasingly being used clinical medical dental healthcare analyses, with valuable applications, which include disease diagnosis, risk assessment, treatment planning, drug discovery. This paper presents narrative literature review use from multi-disciplinary perspective, specifically cardiology, allergology, endocrinology, fields. The highlights data recent research development efforts for healthcare, well challenges limitations associated implementation, privacy security considerations, along ethical legal concerns. regulation responsible design, development, still early stages rapid evolution field. However, duty carefully consider implications implementing respond appropriately. With potential reshape delivery enhance patient outcomes, systems continue reveal their capabilities.
Language: Английский
Citations
53
Nature Machine Intelligence, Journal Year: 2024, Volume and Issue: 6(3), P. 338 - 353
Published: March 22, 2024
Language: Английский
Citations
53
Briefings in Bioinformatics, Journal Year: 2023, Volume and Issue: 24(4)
Published: July 1, 2023
Recent advances and achievements of artificial intelligence (AI) as well deep graph learning models have established their usefulness in biomedical applications, especially drug-drug interactions (DDIs). DDIs refer to a change the effect one drug presence another human body, which plays an essential role discovery clinical research. prediction through traditional trials experiments is expensive time-consuming process. To correctly apply advanced AI learning, developer user meet various challenges such availability encoding data resources, design computational methods. This review summarizes chemical structure based, network NLP based hybrid methods, providing updated accessible guide broad researchers development community with different domain knowledge. We introduce widely-used molecular representation describe theoretical frameworks neural for representing structures. present advantages disadvantages methods by performing comparative experiments. discuss potential technical highlight future directions accelerating prediction.
Language: Английский
Citations
47
Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: unknown
Published: Aug. 14, 2024
Abstract In recent years, the study of artificial intelligence (AI) has undergone a paradigm shift. This been propelled by groundbreaking capabilities generative models both in supervised and unsupervised learning scenarios. Generative AI shown state-of-the-art performance solving perplexing real-world conundrums fields such as image translation, medical diagnostics, textual imagery fusion, natural language processing, beyond. paper documents systematic review analysis advancements techniques with detailed discussion their applications including application-specific models. Indeed, major impact that made to date, generation development large models, field translation several other interdisciplinary AI. Moreover, primary contribution this lies its coherent synthesis latest these areas, seamlessly weaving together contemporary breakthroughs field. Particularly, how it shares an exploration future trajectory for conclusion, ends Responsible principles, necessary ethical considerations sustainability growth
Language: Английский
Citations
44
Cell Reports Medicine, Journal Year: 2024, Volume and Issue: 5(2), P. 101379 - 101379
Published: Feb. 1, 2024
The high failure rate of clinical trials in Alzheimer's disease (AD) and AD-related dementia (ADRD) is due to a lack understanding the pathophysiology disease, this deficit may be addressed by applying artificial intelligence (AI) "big data" rapidly effectively expand therapeutic development efforts. Recent accelerations computing power availability big data, including electronic health records multi-omics profiles, have converged provide opportunities for scientific discovery treatment development. Here, we review potential utility AI approaches data disease-modifying medicines AD/ADRD. We illustrate how tools can applied AD/ADRD drug pipeline through collaborative efforts among neurologists, gerontologists, geneticists, pharmacologists, medicinal chemists, computational scientists. open science expedite therapeutics other neurodegenerative diseases.
Language: Английский
Citations
22
Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)
Published: Aug. 30, 2024
Development of potent and broad-spectrum antimicrobial peptides (AMPs) could help overcome the resistance crisis. We develop a peptide language-based deep generative framework (deepAMP) for identifying potent, AMPs. Using deepAMP to reduce enhance membrane-disrupting abilities AMPs, we identify, synthesize, experimentally test 18 T1-AMP (Tier 1) 11 T2-AMP 2) candidates in two-round design by employing cross-optimization-validation. More than 90% designed AMPs show better inhibition penetratin both Gram-positive (i.e., S. aureus) Gram-negative bacteria K. pneumoniae P. aeruginosa). T2-9 shows strongest antibacterial activity, comparable FDA-approved antibiotics. that three (T1-2, T1-5 T2-10) significantly aureus compared ciprofloxacin are effective against skin wound infection female mouse model infected with aeruginosa. In summary, expedites discovery effective, drug-resistant bacteria.
Language: Английский
Citations
18