Elsevier eBooks, Год журнала: 2023, Номер unknown
Опубликована: Янв. 1, 2023
Язык: Английский
Drug Discovery Today, Год журнала: 2024, Номер 29(6), С. 104009 - 104009
Опубликована: Апрель 30, 2024
AI techniques are making inroads into the field of drug discovery. As a result, growing number drugs and vaccines have been discovered using AI. However, questions remain about success these molecules in clinical trials. To address questions, we conducted first analysis pipelines AI-native Biotech companies. In Phase I find AI-discovered an 80–90% rate, substantially higher than historic industry averages. This suggests, argue, that is highly capable designing or identifying with drug-like properties. II rate ∼40%, albeit on limited sample size, comparable to Our findings highlight early signs potential for molecules.
Язык: Английский
Процитировано
35
Gut, Год журнала: 2024, Номер unknown, С. gutjnl - 331740
Опубликована: Авг. 22, 2024
Advancements in omics technologies and artificial intelligence (AI) methodologies are fuelling our progress towards personalised diagnosis, prognosis treatment strategies hepatology. This review provides a comprehensive overview of the current landscape AI methods used for analysis data liver diseases. We present an prevalence different levels across various diseases, as well categorise methodology studies. Specifically, we highlight predominance transcriptomic genomic profiling relatively sparse exploration other such proteome methylome, which represent untapped potential novel insights. Publicly available database initiatives The Cancer Genome Atlas International Consortium have paved way advancements diagnosis hepatocellular carcinoma. However, same availability large datasets remains limited Furthermore, application sophisticated to handle complexities multiomics requires substantial train validate models faces challenges achieving bias-free results with clinical utility. Strategies address paucity capitalise on opportunities discussed. Given global burden chronic it is imperative that multicentre collaborations be established generate large-scale early disease recognition intervention. Exploring advanced also necessary maximise these improve detection strategies.
Язык: Английский
Процитировано
12
Heliyon, Год журнала: 2024, Номер 10(3), С. e25369 - e25369
Опубликована: Фев. 1, 2024
In recent years, scientific data on cancer has expanded, providing potential for a better understanding of malignancies and improved tailored care. Advances in Artificial Intelligence (AI) processing power algorithmic development position Machine Learning (ML) Deep (DL) as crucial players predicting Leukemia, blood cancer, using integrated multi-omics technology. However, realizing these goals demands novel approaches to harness this deluge. This study introduces Leukemia diagnosis approach, analyzing accuracy ML DL algorithms. techniques, including Random Forest (RF), Naive Bayes (NB), Decision Tree (DT), Logistic Regression (LR), Gradient Boosting (GB), methods such Recurrent Neural Networks (RNN) Feedforward (FNN) are compared. GB achieved 97 % ML, while RNN outperformed by achieving 98 DL. approach filters unclassified effectively, demonstrating the significance leukemia prediction. The testing validation was based 17 different features patient age, sex, mutation type, treatment methods, chromosomes, others. Our compares techniques chooses best technique that gives optimum results. emphasizes implications high-throughput technology healthcare, offering
Язык: Английский
Процитировано
9
Biocatalysis and Agricultural Biotechnology, Год журнала: 2024, Номер 59, С. 103260 - 103260
Опубликована: Май 29, 2024
Язык: Английский
Процитировано
5
Current Cardiology Reports, Год журнала: 2025, Номер 27(1)
Опубликована: Фев. 19, 2025
Язык: Английский
Процитировано
0
Computers in Biology and Medicine, Год журнала: 2025, Номер 189, С. 109918 - 109918
Опубликована: Март 3, 2025
Язык: Английский
Процитировано
0
FASEB BioAdvances, Год журнала: 2025, Номер unknown
Опубликована: Март 3, 2025
Abstract A “quiet revolution” in medicine has been taking place over the past two decades. There are converging dynamic forces that have propelled precision to limelight, garnering wide public attention. The first driver is realization populations within a disease area can be stratified, thus developing therapies tailored their specific needs, and capability identify these by analyzing large, diverse datasets. second technology advances multi‐omics approaches applications (i.e., molecularly informed medicine) enabling more comprehensive portrait of biology. This promises not only accelerate development processes but also presents challenges for healthcare professionals health systems struggling interconnect integrate disparate data sources into cohesive clinical strategy benefit patients. We coin here term next‐generation (ngPM), which bound become conventional clinics sooner or later. Artificial intelligence (AI) machine learning (ML) transformative potential strategic response today's tomorrow's opportunities. chief how well (PM) permeates primary care standard drive toward wellness lifestyle while ensuring access feasible, streamlined, routine. present perspective would harness power ngPM wellness.
Язык: Английский
Процитировано
0
Expert Review of Gastroenterology & Hepatology, Год журнала: 2025, Номер unknown, С. 1 - 9
Опубликована: Март 26, 2025
Celiac disease (CeD) is an immune-mediated condition that occurs in genetically predisposed individuals ingesting gluten. It characterized by enteropathy leading to both gastrointestinal and extra-intestinal symptoms. The prevalence of CeD has increased world-wide. Evidence suggests genetic predisposition exposure gluten are necessary but not sufficient for onset, implying other unknown factors at play its pathogenesis. This review summarizes the current knowledge on contribution gut microbiota pathogenesis, aiming address question whether it cause or consequence celiac enteropathy. We reviewed literature (studies published PubMed database between 2007 2023), linking dysbiosis CeD, focusing specifically prospective birth cohorts' studies discussing how multi-omics artificial intelligence (AI) could transform diagnosis a personalized medicine approach. A multi-omic approach will allow better clarification pivotal role microbiome epigenetically triggering Further, combination results with AI would pave way improved identification new therapeutic interventions.
Язык: Английский
Процитировано
0
Food Bioscience, Год журнала: 2025, Номер unknown, С. 106687 - 106687
Опубликована: Апрель 1, 2025
Язык: Английский
Процитировано
0
The Innovation Life, Год журнала: 2024, Номер 2(3), С. 100079 - 100079
Опубликована: Янв. 1, 2024
<p>Biomedical data encompasses images, texts, physiological signals, and molecular omics data. As the costs of various acquisition methods, such as genomic sequencing, continue to decrease, availability biomedical is increasing. However, this often exhibits high dimensionality, heterogeneity, multimodal characteristics, necessitating use advanced computational modeling. Transforming raw into meaningful biological insights a critical aspect modeling, which plays an increasingly important role in research era big This review outlines collection types challenges faced including standardization, privacy protection. Additionally, it addresses complexity interpretability models used guide knowledge discoveries. The also discusses architectures parallel computing, cloud edge are essential meet demands large-scale computation. Furthermore, highlights driving force modeling advancing medical research. With foundation data, models, computation, transitioning from experimental observation theoretical deduction data-driven approaches, profoundly impacting scientific methodologies paradigms. development steering toward intelligent medicine, redefining paradigm biomedicine.</p>
Язык: Английский
Процитировано
3