Scientific discovery in the age of artificial intelligence

Nature, Journal Year: 2023, Volume and Issue: 620(7972), P. 47 - 60

Published: Aug. 2, 2023

Language: Английский

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Building a knowledge graph to enable precision medicine

Scientific Data, Journal Year: 2023, Volume and Issue: 10(1)

Published: Feb. 2, 2023

Abstract Developing personalized diagnostic strategies and targeted treatments requires a deep understanding of disease biology the ability to dissect relationship between molecular genetic factors their phenotypic consequences. However, such knowledge is fragmented across publications, non-standardized repositories, evolving ontologies describing various scales biological organization genotypes clinical phenotypes. Here, we present PrimeKG, multimodal graph for precision medicine analyses. PrimeKG integrates 20 high-quality resources describe 17,080 diseases with 4,050,249 relationships representing ten major scales, including disease-associated protein perturbations, processes pathways, anatomical entire range approved drugs therapeutic action, considerably expanding previous efforts in disease-rooted graphs. contains an abundance ‘indications’, ‘contradictions’, ‘off-label use’ drug-disease edges that lack other graphs can support AI analyses how affect networks. We supplement PrimeKG’s structure language descriptions guidelines enable provide instructions continual updates as new data become available.

Language: Английский

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A comprehensive review of machine learning algorithms and their application in geriatric medicine: present and future

Aging Clinical and Experimental Research, Journal Year: 2023, Volume and Issue: 35(11), P. 2363 - 2397

Published: Sept. 8, 2023

Abstract The increasing access to health data worldwide is driving a resurgence in machine learning research, including data-hungry deep algorithms. More computationally efficient algorithms now offer unique opportunities enhance diagnosis, risk stratification, and individualised approaches patient management. Such are particularly relevant for the management of older patients, group that characterised by complex multimorbidity patterns significant interindividual variability homeostatic capacity, organ function, response treatment. Clinical tools utilise determine optimal choice treatment slowly gaining necessary approval from governing bodies being implemented into healthcare, with implications virtually all medical disciplines during next phase digital medicine. Beyond obtaining regulatory approval, crucial element implementing these trust support people use them. In this context, an increased understanding clinicians artificial intelligence provides appreciation possible benefits, risks, uncertainties, improves chances successful adoption. This review broad taxonomy algorithms, followed more detailed description each algorithm class, their purpose capabilities, examples applications, geriatric Additional focus given on clinical challenges involved relying devices reduced interpretability progress made counteracting latter via development explainable learning.

Language: Английский

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A foundation model for clinician-centered drug repurposing

Nature Medicine, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 25, 2024

Drug repurposing-identifying new therapeutic uses for approved drugs-is often a serendipitous and opportunistic endeavour to expand the use of drugs diseases. The clinical utility drug-repurposing artificial intelligence (AI) models remains limited because these focus narrowly on diseases which some already exist. Here we introduce TxGNN, graph foundation model zero-shot drug repurposing, identifying candidates even with treatment options or no existing drugs. Trained medical knowledge graph, TxGNN neural network metric learning module rank as potential indications contraindications 17,080 When benchmarked against 8 methods, improves prediction accuracy by 49.2% 35.1% under stringent evaluation. To facilitate interpretation, TxGNN's Explainer offers transparent insights into multi-hop paths that form predictive rationales. Human evaluation showed predictions explanations perform encouragingly multiple axes performance beyond accuracy. Many align well off-label prescriptions clinicians previously made in large healthcare system. are accurate, consistent use, can be investigated human experts through interpretable

Language: Английский

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Multimodal data integration for oncology in the era of deep neural networks: a review

Frontiers in Artificial Intelligence, Journal Year: 2024, Volume and Issue: 7

Published: July 25, 2024

Cancer research encompasses data across various scales, modalities, and resolutions, from screening diagnostic imaging to digitized histopathology slides types of molecular clinical records. The integration these diverse for personalized cancer care predictive modeling holds the promise enhancing accuracy reliability screening, diagnosis, treatment. Traditional analytical methods, which often focus on isolated or unimodal information, fall short capturing complex heterogeneous nature data. advent deep neural networks has spurred development sophisticated multimodal fusion techniques capable extracting synthesizing information disparate sources. Among these, Graph Neural Networks (GNNs) Transformers have emerged as powerful tools learning, demonstrating significant success. This review presents foundational principles learning including oncology taxonomy strategies. We delve into recent advancements in GNNs oncology, spotlighting key studies their pivotal findings. discuss unique challenges such heterogeneity complexities, alongside opportunities it a more nuanced comprehensive understanding cancer. Finally, we present some latest pan-cancer By surveying landscape our goal is underline transformative potential Transformers. Through technological methodological innovations presented this review, aim chart course future promising field. may be first that highlights current state applications using transformers, sources, sets stage evolution, encouraging further exploration care.

Language: Английский

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Current and future directions in network biology

Bioinformatics Advances, Journal Year: 2024, Volume and Issue: 4(1)

Published: Jan. 1, 2024

Abstract Summary Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions diseases across systems scales. Although been around for two decades, it remains nascent. It witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably growing complexity volume data together with increased diversity types describing different tiers organization. We discuss prevailing research directions network biology, focusing on molecular/cellular networks but also other such as biomedical knowledge graphs, patient similarity networks, brain social/contact relevant to disease spread. In more detail, we highlight areas inference comparison multimodal integration heterogeneous higher-order analysis, machine learning network-based personalized medicine. Following overview recent breakthroughs these five areas, offer a perspective future biology. Additionally, scientific communities, educational initiatives, importance fostering within field. This article establishes roadmap immediate long-term vision Availability implementation Not applicable.

Language: Английский

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A comprehensive review on triboelectric sensors and AI-integrated systems

Materials Today, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 1, 2024

Language: Английский

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Graph machine learning for integrated multi-omics analysis

British Journal of Cancer, Journal Year: 2024, Volume and Issue: 131(2), P. 205 - 211

Published: May 10, 2024

Abstract Multi-omics experiments at bulk or single-cell resolution facilitate the discovery of hypothesis-generating biomarkers for predicting response to therapy, as well aid in uncovering mechanistic insights into cellular and microenvironmental processes. Many methods data integration have been developed identification key elements that explain predict disease risk other biological outcomes. The heterogeneous graph representation multi-omics provides an advantage discerning patterns suitable predictive/exploratory analysis, thus permitting modeling complex relationships. Graph-based approaches—including neural networks—potentially offer a reliable methodological toolset can provide tangible alternative scientists clinicians seek ideas implementation strategies integrated analysis their omics sets biomedical research. workflows continue push limits technological envelope, this perspective focused literature review research articles which machine learning is utilized analyses, with several examples demonstrate effectiveness graph-based approaches.

Language: Английский

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AI-driven multi-omics integration for multi-scale predictive modeling of genotype-environment-phenotype relationships

Computational and Structural Biotechnology Journal, Journal Year: 2025, Volume and Issue: 27, P. 265 - 277

Published: Jan. 1, 2025

Despite the wealth of single-cell multi-omics data, it remains challenging to predict consequences novel genetic and chemical perturbations in human body. It requires knowledge molecular interactions at all biological levels, encompassing disease models humans. Current machine learning methods primarily establish statistical correlations between genotypes phenotypes but struggle identify physiologically significant causal factors, limiting their predictive power. Key challenges modeling include scarcity labeled generalization across different domains, disentangling causation from correlation. In light recent advances data integration, we propose a new artificial intelligence (AI)-powered biology-inspired multi-scale framework tackle these issues. This will integrate organism hierarchies, species genotype-environment-phenotype relationships under various conditions. AI inspired by biology may targets, biomarkers, pharmaceutical agents, personalized medicines for presently unmet medical needs.

Language: Английский

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Digital twins as global learning health and disease models for preventive and personalized medicine

Genome Medicine, Journal Year: 2025, Volume and Issue: 17(1)

Published: Feb. 7, 2025

Abstract Ineffective medication is a major healthcare problem causing significant patient suffering and economic costs. This issue stems from the complex nature of diseases, which involve altered interactions among thousands genes across multiple cell types organs. Disease progression can vary between patients over time, influenced by genetic environmental factors. To address this challenge, digital twins have emerged as promising approach, led to international initiatives aiming at clinical implementations. Digital are virtual representations health disease processes that integrate real-time data simulations predict, prevent, personalize treatments. Early applications DTs shown potential in areas like artificial organs, cancer, cardiology, hospital workflow optimization. However, widespread implementation faces several challenges: (1) characterizing dynamic molecular changes biological scales; (2) developing computational methods into DTs; (3) prioritizing mechanisms therapeutic targets; (4) creating interoperable DT systems learn each other; (5) designing user-friendly interfaces for clinicians; (6) scaling technology globally equitable access; (7) addressing ethical, regulatory, financial considerations. Overcoming these hurdles could pave way more predictive, preventive, personalized medicine, potentially transforming delivery improving outcomes.

Language: Английский

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