Artificial intelligence in the life sciences最新文献_第3页

Unraveling the co-morbidity between COVID-19 and neurodegenerative diseases through multi-scale graph analysis: A systematic investigation of biological databases and text mining 通过多尺度图分析揭示COVID-19与神经退行性疾病的共发病：生物数据库和文本挖掘的系统调查

IF 5.4

Artificial intelligence in the life sciences Pub Date : 2025-12-01 Epub Date: 2025-07-28 DOI: 10.1016/j.ailsci.2025.100138

Negin Sadat Babaiha , Stefan Geissler , Vincent Nibart , Heval Atas Güvenilir , Vinay Srinivas Bharadhwaj , Alpha Tom Kodamullil , Juergen Klein , Marc Jacobs , Martin Hofmann-Apitius

{"title":"Unraveling the co-morbidity between COVID-19 and neurodegenerative diseases through multi-scale graph analysis: A systematic investigation of biological databases and text mining","authors":"Negin Sadat Babaiha , Stefan Geissler , Vincent Nibart , Heval Atas Güvenilir , Vinay Srinivas Bharadhwaj , Alpha Tom Kodamullil , Juergen Klein , Marc Jacobs , Martin Hofmann-Apitius","doi":"10.1016/j.ailsci.2025.100138","DOIUrl":"10.1016/j.ailsci.2025.100138","url":null,"abstract":"<div><div>The COVID-19 pandemic has generated a vast volume of research, yet much of it focuses on individual diseases, overlooking complex comorbidity relationships. While extensive literature exists on both neurodegenerative diseases (NDDs), such as Alzheimer’s and Parkinson’s, and COVID-19, their intersection remains underexplored. Co-morbidity modeling is crucial, particularly for hospitalized patients often presenting with multiple conditions. This study investigates the interplay between COVID-19 and NDDs by integrating knowledge graphs (KGs) built from curated biomedical datasets and text mining tools. We performed comprehensive analyses—including path analysis, phenotype coverage, and mapping of cellular and genetic factors—across multiple KGs, such as PrimeKG, DrugBank, OpenTargets, and those generated via natural language processing (NLP) methods. Our findings reveal notable variability in graph density and connectivity, with each KG offering unique insights into molecular and phenotypic links between COVID-19 and NDDs. Key genetic and inflammatory markers, especially immune response genes, consistently appeared across graphs, suggesting a shared pathogenic basis. By unifying structured biological data with unstructured textual evidence, we enhance co-morbidity modeling and improve recall in identifying mechanisms underlying COVID-19–NDD interactions. This integrative framework supports the development of a co-morbidity hypothesis database aimed at facilitating therapeutic target discovery. All data, methods, and instructions for accessing the co-morbidity hypothesis database are publicly available at: <span><span>https://github.com/SCAI-BIO/covid-NDD-comorbidity-NLP</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"8 ","pages":"Article 100138"},"PeriodicalIF":5.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144748670","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

Artificial intelligence methods and approaches to improve data quality in helthcare data 提高医疗保健数据质量的人工智能方法和途径

Artificial intelligence in the life sciences Pub Date : 2025-12-01 Epub Date: 2025-07-04 DOI: 10.1016/j.ailsci.2025.100135

Jarmakoviča Agate

{"title":"Artificial intelligence methods and approaches to improve data quality in helthcare data","authors":"Jarmakoviča Agate","doi":"10.1016/j.ailsci.2025.100135","DOIUrl":"10.1016/j.ailsci.2025.100135","url":null,"abstract":"<div><div>This study explores artificial intelligence (AI) methods and approaches used to improve data quality, with a particular focus on healthcare data. Applying a systematic literature review based on the PRISMA framework, the research examines publications from 2020 to 2025 that analyze AI applications across key data quality dimensions—accuracy, completeness, consistency, timeliness, uniqueness, and validity. The study aims to identify which AI methods are most commonly employed and how they align with these quality attributes. A conceptual map was developed to visualize the relationships between dimensions and AI techniques such as deep learning, federated learning, data-centric AI, and ontology-based data governance. Findings reveal that accuracy and consistency are the most emphasized dimensions in the literature, with methods like supervised learning, NLP, and isolation forest frequently applied. In contrast, dimensions like timeliness and validity receive comparatively limited attention. The study concludes that certain AI methods—particularly data-centric and cross-cutting approaches—are effective in addressing multiple data quality challenges simultaneously. These insights offer practical guidance for selecting AI strategies in healthcare data quality improvement and highlight areas for future research.</div></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"8 ","pages":"Article 100135"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632718","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

Co-folding, the future of docking – prediction of allosteric and orthosteric ligands 共折叠，对接的未来——变构配体和正构配体的预测

Artificial intelligence in the life sciences Pub Date : 2025-12-01 Epub Date: 2025-06-29 DOI: 10.1016/j.ailsci.2025.100136

Eva Nittinger , Özge Yoluk , Alessandro Tibo , Gustav Olanders , Christian Tyrchan

{"title":"Co-folding, the future of docking – prediction of allosteric and orthosteric ligands","authors":"Eva Nittinger , Özge Yoluk , Alessandro Tibo , Gustav Olanders , Christian Tyrchan","doi":"10.1016/j.ailsci.2025.100136","DOIUrl":"10.1016/j.ailsci.2025.100136","url":null,"abstract":"<div><div>In drug discovery understanding protein structures is essential for comprehending their functions and interactions with drugs. Traditional methods like X-ray crystallography and cryo-electron microscopy have been used to solve these structures. Recently, computational biology has seen a breakthrough with deep learning algorithms capable of predicting protein structures based on amino acid sequences. These methods have now evolved into predicting protein-ligand interactions from sequence – co-folding methods. Despite the great advancement in the field during the last year, there are still open challenges. Here, we focus on the prediction of allosteric binding sites, using a dataset of 17 orthosteric/allosteric ligand sets. Three different co-folding methods – NeuralPLexer, RoseTTAFold All-Atom and Boltz-1/Boltz-1x – were used to predict both allosteric and orthosteric ligands. Using PoseBusters, the ligand quality was checked, with >90 % of ligands predicted by Boltz-1x passing the default quality criteria. Boltz-1, NeuralPLexer and RoseTTAFold All-Atom still showing high quality drawbacks. The orthosteric ligands were well placed. However, instead of the allosteric pocket these deep learning approaches generally favor the orthosteric site, which is the one most represented in the training data.</div></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"8 ","pages":"Article 100136"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144572530","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

MegaEye: Applying multiple machine learning approaches to identify oral compounds with ocular bioactivity MegaEye：应用多种机器学习方法识别具有眼部生物活性的口服化合物

IF 5.4

Artificial intelligence in the life sciences Pub Date : 2025-12-01 Epub Date: 2025-09-15 DOI: 10.1016/j.ailsci.2025.100143

Fabio Urbina , Scott H. Greenwald , Patricia A. Vignaux , Thomas R. Lane , Joshua S. Harris , Mayssa Attar , Keith Luhrs , Sean Ekins

{"title":"MegaEye: Applying multiple machine learning approaches to identify oral compounds with ocular bioactivity","authors":"Fabio Urbina , Scott H. Greenwald , Patricia A. Vignaux , Thomas R. Lane , Joshua S. Harris , Mayssa Attar , Keith Luhrs , Sean Ekins","doi":"10.1016/j.ailsci.2025.100143","DOIUrl":"10.1016/j.ailsci.2025.100143","url":null,"abstract":"<div><div>The eye is a complex organ with the critical role of mediating the optical and initial signal processing steps of vision. As such, the eye has multiple physiological and dynamic barriers to protect ocular tissues and compartments. Oral administration of pharmacological agents to treat ocular diseases have often failed to demonstrate efficacy in clinical trials. The ability of a molecule to reach a specific target in the eye (e.g. cells in the anterior versus posterior segment) is largely determined by whether its physicochemical properties permit passage across the various ocular barriers (e.g. cornea, sclera, tear dilution, blood-retinal barrier, lymphatic outflow) that are relevant to the route of administration and the target location. The use of machine learning to predict ocular bioactivity of molecules is underexplored. We now describe the curation of several datasets, generated by a wide array of computational approaches, that are used to identify drugs predicted to reach the eye following oral delivery. These datasets included simple molecular properties (e.g. molecular weight), using the blood-brain barrier MPO score, and machine learning models as a proxy for the blood-retinal barrier using transporter and other relevant literature datasets. FDA approved drugs with reported ocular activity were used to validate the models’ ability to identify additional molecules not in the models. Finally, we used a large language model, to rank over 400,000 natural compounds by potential activity in the eye. In summary, we illustrate machine learning model applications that can be expanded for ocular applications in future to repurpose molecules.</div></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"8 ","pages":"Article 100143"},"PeriodicalIF":5.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145104219","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

From task-specific language models to research agents 从特定任务的语言模型到研究代理

IF 5.4

Artificial intelligence in the life sciences Pub Date : 2025-12-01 Epub Date: 2025-08-26 DOI: 10.1016/j.ailsci.2025.100141

Jürgen Bajorath

引用次数: 0

A dystopian nightmare for science and how to survive it 科学的反乌托邦噩梦以及如何生存

IF 5.4

Artificial intelligence in the life sciences Pub Date : 2025-08-05 DOI: 10.1016/j.ailsci.2025.100139

Sean Ekins

引用次数: 0

The path to adoption of open source AI for drug discovery in Africa 在非洲采用开源人工智能进行药物发现的途径

Artificial intelligence in the life sciences Pub Date : 2025-06-01 Epub Date: 2024-12-05 DOI: 10.1016/j.ailsci.2024.100118

Gemma Turon, Miquel Duran-Frigola

引用次数: 0

“Foundation models for research: A matter of trust?” “研究的基础模型：信任问题？”

Artificial intelligence in the life sciences Pub Date : 2025-06-01 Epub Date: 2025-02-06 DOI: 10.1016/j.ailsci.2025.100126

Koen Bruynseels , Lotte Asveld , Jeroen van den Hoven

{"title":"“Foundation models for research: A matter of trust?”","authors":"Koen Bruynseels , Lotte Asveld , Jeroen van den Hoven","doi":"10.1016/j.ailsci.2025.100126","DOIUrl":"10.1016/j.ailsci.2025.100126","url":null,"abstract":"<div><div>Science would not be possible without trust among experts, trust of the public in experts, and reliance on scientific instruments and methods. The rapid adoption of scientific foundation models and their use in AI agents is changing scientific practices and thereby impacting this epistemic fabric which hinges on trust and reliance. Foundation models are machine learning models that are trained on large bodies of data and can be applied to a multitude of tasks. Their application in science raises the question of whether scientific foundation models can be relied upon as a research tool and to what extent, or even be trusted as if they were research partners.</div><div>Conceptual clarification of the notions of trust and reliance in science is pivotal in the face of foundation models. Trust and reliance form the glue for the increasingly distributed epistemic labour within contemporary technoscientific systems. We build on two concepts of trust in science, namely trust in science as shared values, and trust in science based on commitments to processes that provide objective claims. We analyse whether scientific foundation models are research tools to which the concept of reliance applies, or research partners that can be trustworthy or not. We consider these foundation models within their socio-technical contexts.</div><div>Allocation of trust should be reserved for human agents and the organizations they operate in. Reliance applies to foundation models and artificial intelligence agents. This distinction is important to unambiguously allocate responsibility, which is crucial in maintaining the fabric of trust that underpins science.</div></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"7 ","pages":"Article 100126"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Enhancing uncertainty quantification in drug discovery with censored regression labels 用删节回归标签增强药物发现中的不确定度量化

Artificial intelligence in the life sciences Pub Date : 2025-06-01 Epub Date: 2025-02-13 DOI: 10.1016/j.ailsci.2025.100128

Emma Svensson , Hannah Rosa Friesacher , Susanne Winiwarter , Lewis Mervin , Adam Arany , Ola Engkvist

{"title":"Enhancing uncertainty quantification in drug discovery with censored regression labels","authors":"Emma Svensson , Hannah Rosa Friesacher , Susanne Winiwarter , Lewis Mervin , Adam Arany , Ola Engkvist","doi":"10.1016/j.ailsci.2025.100128","DOIUrl":"10.1016/j.ailsci.2025.100128","url":null,"abstract":"<div><div>In the early stages of drug discovery, decisions regarding which experiments to pursue can be influenced by computational models for quantitative structure–activity relationships (QSAR). These decisions are critical due to the time-consuming and expensive nature of the experiments. Therefore, it is becoming essential to accurately quantify the uncertainty in machine learning predictions, such that resources can be used optimally and trust in the models improves. While computational methods for QSAR modeling often suffer from limited data and sparse experimental observations, additional information can exist in the form of censored labels that provide thresholds rather than precise values of observations. However, the standard approaches that quantify uncertainty in machine learning cannot fully utilize censored labels. In this work, we adapt ensemble-based, Bayesian, and Gaussian models with tools to learn from censored labels by using the Tobit model from survival analysis. Our results demonstrate that despite the partial information available in censored labels, they are essential to reliably estimate uncertainties in real pharmaceutical settings where approximately one-third or more of experimental labels are censored.</div></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":"7 ","pages":"Article 100128"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Clinical diagnostics and medical image analysis 临床诊断和医学图像分析

Artificial intelligence in the life sciences Pub Date : 2025-06-01 Epub Date: 2024-12-09 DOI: 10.1016/j.ailsci.2024.100119

Jürgen Bajorath

引用次数: 0