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NutriFd: Proving the medicinal value of food nutrition based on food-disease association and treatment networks

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arXiv 2023年

作者： Su, Wanting Liu, dongwei Tan, Feng Hu, Lun Hu, Pengwei Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of Sciences Urumqi China Merck KGaA Science and Technology Office Germany The University of Manchester Division of Informatics Imaging & Data Sciences United Kingdom Technology R&D Co. Ltd. China

There is rising evidence of the health benefit associated with specific dietary interventions. Current food-disease databases focus on associations and treatment relationships but haven't provided a reasonable assessment of the strength of the relationship, and lack of attention on food nutrition. There is an unmet need for a large database that can guide dietary therapy. We fill the gap by developing NutriFd, a scoring network based on associations and therapeutic relationships between foods and diseases. NutriFd integrates 9 databases including foods, nutrients, diseases, genes, mirNAs, compounds, disease ontology and their relationships. To our best knowledge, this database is the only one that can score the associations and therapeutic relationships of everyday foods and diseases by weighting inference scores of food compounds to diseases. In addition, NutriFd demonstrates the predictive nature of nutrients on the therapeutic relationships between foods and diseases through machine learning models, laying the foundation for a mechanistic understanding of food therapy. © 2023, CC BY-NC-Nd.

关键词： Nutrition

The Ontoverse: democratising Access to Knowledge Graph-based data Through a Cartographic Interface

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arXiv 2024年

作者： Zimmermann, Johannes Wiktorek, dariusz Meusburger, Thomas Monge-dalmau, Miquel Fabregat, Antonio Jarasch, Alexander Schmidt, Günter reis-Filho, Jorge S. Simpson, T. Ian Computational Pathology GmbH AstraZeneca Munich Germany ADLA IT Ltd. Hurlingham Studios London United Kingdom Knowledge Graph Team Enterprise Data Office AstraZeneca Barcelona Spain Knowledge Graph Team Enterprise Data Office AstraZeneca Cambridge United Kingdom Neo4j Germany GmbH Life Sciences Dept. Munich Germany Cancer Biomarker Development Oncology R&D AstraZeneca GaithersburgMD United States School of Informatics The University of Edinburgh Edinburgh United Kingdom

As the number of scientific publications and preprints is growing exponentially, several attempts have been made to navigate the sheer volume of this complex and increasingly detailed landscape. These have almost exclusively taken unsupervised approaches that fail to incorporate domain knowledge. As a consequence, these emerging landscapes lack the structural organisation required for intuitive interactive human exploration and discovery. Especially in highly interdisciplinary fields, a deep understanding of the connectedness of research works across topics is essential for generating insights. We have developed a unique approach to data navigation that leans on geographical visualisation and uses hierarchically structured domain knowledge to enable end-users to explore knowledge spaces grounded in their desired domains of interest. This can take advantage of existing ontologies, proprietary intelligence schemata, or be directly derived from the underlying data through hierarchical topic modelling. Our approach uses natural language processing techniques to extract named entities from the underlying data and normalise them against relevant domain references and navigational structures. The knowledge is integrated by first calculating similarities between entities based on their shared extracted feature space and then by alignment to the navigational structures. The result is a knowledge graph that allows for full text and semantic graph query and structured topic driven navigation. This allows end-users to identify entities relevant to their needs and access extensive graph analytics. The user interface facilitates graphical interaction with the underlying knowledge graph and mimics a cartographic map to maximise ease of use and widen adoption. We demonstrate an exemplar project using our generalisable and scalable infrastructure for an academic biomedical literature corpus that is grounded against hundreds of different named domain entities. © 2024, CC BY.

关键词： Knowledge graph

Challenges and Best Practices in Corporate AI Governance: Lessons from the Biopharmaceutical Industry

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SSRN

SSrN 2022年

作者： Mökander, Jakob Sheth, Margi Gersbro-Sundler, Mimmi Blomgren, Peder Floridi, Luciano Oxford Internet Institute University of Oxford 1 St Giles’ OxfordOX1 3JS United Kingdom Center for Information Technology Policy Princeton University PrincetonNJ08544 United States R&D Data Office Data Sciences and AI BioPharmaceuticals R&D AstraZeneca Cambridge United Kingdom Department of Legal Studies University of Bologna Via Zamboni 33 Bologna40126 Italy

While the use of artificial intelligence (AI) systems promises to bring significant economic and social benefits, it is also coupled with ethical, legal, and technical challenges. Business leaders thus face the question of how to best reap the benefits of automation whilst managing the associated risks. As a first step, many companies have committed themselves to various sets of ethics principles aimed at guiding the design and use of AI systems. So far so good. But how can well-intentioned ethical principles be translated into effective practice? And what challenges await companies that attempt to operationalize AI governance? In this article, we address these questions by drawing on our first-hand experience of shaping and driving the roll-out of AI governance within AstraZeneca, a biopharmaceutical company. The examples we discuss highlight challenges that any organization attempting to operationalize AI governance will have to face. These include questions concerning how to define the material scope of AI governance, how to harmonize standards across decentralized organizations, and how to measure the impact of specific AI governance initiatives. By showcasing how AstraZeneca managed these operational questions, we hope to provide project managers, CIOs, AI practitioners, and data privacy officers responsible for designing and implementing AI governance frameworks within other organizations with generalizable best practices. In essence, companies seeking to operationalize AI governance are encouraged to build on existing policies and governance structures, use pragmatic and action-oriented terminology, focus on risk management in development and procurement, and empower employees through continuous education and change management. © 2022, The Authors. All rights reserved.

关键词： Artificial intelligence

Challenges and Best Practices in Corporate AI Governance: Lessons from the Biopharmaceutical Industry

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arXiv 2024年

作者： Mökander, Jakob Sheth, Margi Gersbro-Sundler, Mimmi Blomgren, Peder Floridi, Luciano Oxford Internet Institute University of Oxford 1 St Giles’ OxfordOX1 3JS United Kingdom Center for Information Technology Policy Princeton University PrincetonNJ08544 United States R&D Data Office AstraZeneca Cambridge United Kingdom Department of Legal Studies University of Bologna Via Zamboni 33 Bologna40126 Italy

While the use of artificial intelligence (AI) systems promises to bring significant economic and social benefits, it is also coupled with ethical, legal, and technical challenges. Business leaders thus face the question of how to best reap the benefits of automation whilst managing the associated risks. As a first step, many companies have committed themselves to various sets of ethics principles aimed at guiding the design and use of AI systems. So far so good. But how can well-intentioned ethical principles be translated into effective practice? And what challenges await companies that attempt to operationalize AI governance? In this article, we address these questions by drawing on our firsthand experience of shaping and driving the roll-out of AI governance within AstraZeneca, a biopharmaceutical company. The examples we discuss highlight challenges that any organization attempting to operationalize AI governance will have to face. These include questions concerning how to define the material scope of AI governance, how to harmonize standards across decentralized organizations, and how to measure the impact of specific AI governance initiatives. By showcasing how AstraZeneca managed these operational questions, we hope to provide project managers, CIOs, AI practitioners, and data privacy officers responsible for designing and implementing AI governance frameworks within other organizations with generalizable best practices. In essence, companies seeking to operationalize AI governance are encouraged to build on existing policies and governance structures, use pragmatic and action-oriented terminology, focus on risk management in development and procurement, and empower employees through continuous education and change management. © 2024, CC BY.

关键词： risk management

Electronic Near Point of Convergence: A Tool to Aid the Assessment of Concussion

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Electronic Near Point of Convergence: A Tool to Aid the Asse...

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Liang, Bo Prichep, Leslie S. Kanakia, Saloni Bazarian, Jeffrey J. Covassin, Tracey Elbin, robert J. Hotz, Gillian A. Struder, Jeb F. Armanañzas, rubén BrainScope Company Inc. R&d Department Chevy ChaseMD United States University of Rochester School of Medicine Department of Emergency Medicine RochesterNY United States Michigan State University Department of Kinesiology East LansingMI United States University of Arkansas Office Sports Concussion Research FayettevilleAR United States University of Miami UHealth Concussion Program MiamiFL United States University of Connecticut Korey Stringer Institute StorrsCT United States University of Navarra Institute of Data Science and Artificial Intelligence Tecnun School of Engineering Pamplona Spain

ISBN: (纸本)9798350337488

Near Point of Convergence (NPC) is a widely used test when assessing oculomotor dysfunction in patients with mild Traumatic Brain Injury (mTBI) or concussion. A new electronic version of the Near Point Convergence test (eNPC) was developed to automatically measure NPC distance, addressing the need to improve the administration and precision of NPC testing. Age-adjusted normative values were achieved by using goodness-of-fit and generalized linear regression models. Target subjects included males and females aged 13 to 50 years. Normative values were computed as a function of age for records from 230 healthy volunteers. eNPC values from an independent cohort of 237 patients clinically deemed to have concussion and 245 matched controls were transformed into z-scores relative to age-expected normal eNPC values and then compared to demonstrate performance in a clinical population. Mean differences between matched controls and concussed patients showed a highly significant difference for eNPC z-scores between groups. The maximum Odds-ratio (Or) value for z-scores was reached in the cutoff interval 0.75 to 1 with a mean of 2.614 and a median of 2.627. Usability for the eNPC test was high with a rate of obtaining measurements between 92% and 98%. Using z-scores helped reduce variance of the measurement and accounted for the effect of age. Significant separation between concussed patients and matched controls validated the use of the eNPC mathematical model. Further, high Or values using z-scores demonstrated potential improved clinical utility of the eNPC in the prediction of oculomotor dysfunction. © 2023 IEEE.

关键词： Concussion Medical device Mild Traumatic Brain Injury Near Point of Convergence

Models for Classifying AI Systems: the Switch, the Ladder, and the Matrix 22

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Models for Classifying AI Systems: the Switch, the Ladder, a...

Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency

作者： Jakob Mökander Margi Sheth david Watson Luciano Floridi Oxford Internet Institute University of Oxford United Kingdom R&D Data Office AstraZeneca United Kingdom Department of Statistical Science University College London United Kingdom Oxford Internet Institute University of Oxford United Kingdom and Department of Legal Studies University of Italy

ISBN: (纸本)9781450393522

Organisations that design and deploy systems based on artificial intelligence (AI) increasingly commit themselves to high-level, ethical principles. However, there still exists a gap between principles and practices in AI ethics. A major obstacle to operationalise AI Ethics is the lack of a well-defined material scope. Put differently, the question to which systems and processes AI ethics principles ought to apply remains unanswered. Of course, there exists no universally accepted definition of AI, and different systems pose different ethical challenges. Nevertheless, pragmatic problem-solving demands that things should be sorted so that their grouping will promote successful actions for some specific end. In this article, we review and compare previous attempts to classify AI systems for the practical purpose of implementing AI governance in practice. We find that attempts to classify AI systems found in previous literature use one of three mental models: the Switch, i.e., a binary approach according to which systems either are or are not considered AI systems depending on their characteristics; the Ladder, i.e., a risk-based approach that classifies systems according to the ethical risks they pose; and the Matrix, i.e., a multi-dimensional classification of systems that take various aspects into account, such as context, data input, and decision-model. Each of these models for classifying AI systems comes with its own set of strengths and weaknesses. By conceptualising different ways of classifying AI systems into simple mental models, we hope to provide organisations that design, deploy, or regulate AI systems with the conceptual tools needed to operationalise AI governance in practice.

关键词： Artificial intelligence

The Switch, the Ladder, and the Matrix: Models for Classifying AI Systems

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arXiv 2024年

作者： Mökander, Jakob Sheth, Margi Watson, david S. Floridi, Luciano Oxford Internet Institute University of Oxford 1 St Giles' OxfordOX1 3JS United Kingdom R&D Data Office AstraZeneca PLC 1 Francis Crick Avenue CambridgeCB2 0AA United Kingdom Department of Statistical Science University College London LondonWC1E 6BT United Kingdom Department of Legal Studies University of Bologna Via Zamboni 33 Bologna40126 Italy

Organisations that design and deploy artificial intelligence (AI) systems increasingly commit themselves to high-level, ethical principles. However, there still exists a gap between principles and practices in AI ethics. One major obstacle organisations face when attempting to operationalise AI Ethics is the lack of a well-defined material scope. Put differently, the question to which systems and processes AI ethics principles ought to apply remains unanswered. Of course, there exists no universally accepted definition of AI, and different systems pose different ethical challenges. Nevertheless, pragmatic problem-solving demands that things should be sorted so that their grouping will promote successful actions for some specific end. In this article, we review and compare previous attempts to classify AI systems for the purpose of implementing AI governance in practice. We find that attempts to classify AI systems found in previous literature use one of three mental models: the Switch, i.e., a binary approach according to which systems either are or are not considered AI systems depending on their characteristics;the Ladder, i.e., a risk-based approach that classifies systems according to the ethical risks they pose;and the Matrix, i.e., a multi-dimensional classification of systems that take various aspects into account, such as context, data input, and decision-model. Each of these models for classifying AI systems comes with its own set of strengths and weaknesses. By conceptualising different ways of classifying AI systems into simple mental models, we hope to provide organisations that design, deploy, or regulate AI systems with the conceptual tools needed to operationalise AI governance in practice. © 2024, CC BY.

关键词： Ladders

Over-sampling algorithm for imbalanced data classification

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Journal of Systems Engineering and Electronics 2019年第6期30卷 1182-1191页

作者： XU Xiaolong CHEN Wen SUN Yanfei Jiangsu Key Laboratory of Big Data Security&Intelligent Processing Nanjing University of Posts and TelecommunicationsNanjing 210023China Institute of Big Data Research at Yancheng Nanjing University of Posts and TelecommunicationsYancheng 224000China Office of Scientific R&D Nanjing University of Posts and TelecommunicationsNanjing 210023China

For imbalanced datasets, the focus of classification is to identify samples of the minority class. The performance of current data mining algorithms is not good enough for processing imbalanced datasets. The synthetic minority over-sampling technique(SMOTE) is specifically designed for learning from imbalanced datasets, generating synthetic minority class examples by interpolating between minority class examples nearby. However, the SMOTE encounters the overgeneralization problem. The densitybased spatial clustering of applications with noise(dBSCAN) is not rigorous when dealing with the samples near the *** optimize the dBSCAN algorithm for this problem to make clustering more reasonable. This paper integrates the optimized dBSCAN and SMOTE, and proposes a density-based synthetic minority over-sampling technique(dSMOTE). First, the optimized dBSCAN is used to divide the samples of the minority class into three groups, including core samples, borderline samples and noise samples, and then the noise samples of minority class is removed to synthesize more effective samples. In order to make full use of the information of core samples and borderline samples,different strategies are used to over-sample core samples and borderline samples. Experiments show that dSMOTE can achieve better results compared with SMOTE and Borderline-SMOTE in terms of precision, recall and F-value.

关键词： imbalanced data density-based spatial clustering of applications with noise(dBSCAN) synthetic minority over sampling technique(SMOTE) over-sampling.

Electronic Near Point of Convergence: A Tool to Aid the Assessment of Concussion

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Electronic Near Point of Convergence: A Tool to Aid the Asse...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Bo Liang Leslie S. Prichep Saloni Kanakia Jeffrey J. Bazarian Tracey Covassin robert J. Elbin Gillian A. Hotz Jeb F. Struder rubén Armañanzas R&D Department BrainScope Company Inc. Chevy Chase MD USA Department of Emergency Medicine University of Rochester School of Medicine Rochester NY USA Department of Kinesiology Michigan State University East Lansing MI USA Office Sports Concussion Research University of Arkansas Fayetteville AR USA UHealth Concussion Program University of Miami Miami FL USA Korey Stringer Institute University of Connecticut CT USA Institute of Data Science and Artificial Intelligence TECNUN School of Engineering University of Navarra Pamplona Spain

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