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1.
Nat Commun ; 13(1): 5304, 2022 Sep 09.
Artigo em Inglês | MEDLINE | ID: mdl-36085310

RESUMO

Biomedical data is accumulating at a fast pace and integrating it into a unified framework is a major challenge, so that multiple views of a given biological event can be considered simultaneously. Here we present the Bioteque, a resource of unprecedented size and scope that contains pre-calculated biomedical descriptors derived from a gigantic knowledge graph, displaying more than 450 thousand biological entities and 30 million relationships between them. The Bioteque integrates, harmonizes, and formats data collected from over 150 data sources, including 12 biological entities (e.g., genes, diseases, drugs) linked by 67 types of associations (e.g., 'drug treats disease', 'gene interacts with gene'). We show how Bioteque descriptors facilitate the assessment of high-throughput protein-protein interactome data, the prediction of drug response and new repurposing opportunities, and demonstrate that they can be used off-the-shelf in downstream machine learning tasks without loss of performance with respect to using original data. The Bioteque thus offers a thoroughly processed, tractable, and highly optimized assembly of the biomedical knowledge available in the public domain.


Assuntos
Conhecimento , Reconhecimento Automatizado de Padrão , Bases de Conhecimento , Aprendizado de Máquina , Proteínas
2.
Stud Health Technol Inform ; 296: 1-8, 2022 Aug 17.
Artigo em Inglês | MEDLINE | ID: mdl-36073482

RESUMO

Chronic wounds have significant impacts on patient health-related quality of life (HRQoL) and the healthcare expenditures. Various complex decision-making scenarios arise from wound management. Clinical decision-making systems (CDSS) can assist in relieving healthcare providers in these complex decision-making processes and improve the quality of care. In our study, we used the Decision Model & Notation (DMN) standard as a knowledge representation format to implement a knowledge base for chronic wound material recommendation in phase-based therapy. The resulting decision model is theoretically consistent and sustainable. With this study, we also emphasized the need of a semantic interoperability framework. This opens further research possibilities regarding the improvement of the model and the interest of DMN for decision models in clinical fields.


Assuntos
Bases de Conhecimento , Qualidade de Vida , Tomada de Decisão Clínica , Atenção à Saúde , Pessoal de Saúde , Humanos
3.
Comput Intell Neurosci ; 2022: 1424097, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-36093493

RESUMO

A personalized tourism recommendation system provides convenient and economically affordable travel information for individuals/groups. This recommendation system banks on accumulated and analyzed data for providing context-aware travel solutions. For improving the recommendation efficiency and data analysis of such systems, this article introduces a mining and filtering harmonized collaborative process, named as the collaborative mining and filtering process (CMFP), for reducing the data processing overheads and improving the recommendation ratio. In this process, the accumulated data from the global and personal travel, expenditure, and other information are collaboratively analyzed. This analysis is powered by knowledge-based transfer learning for reducing the retardation in the large data processing. Based on the context-based data analysis, the filtering and mining are jointly performed for providing recommendations. In the filtering process, the maximum processed contextual data are extracted for updating the current knowledge base. From this base, the recommendation for adaptable travel is recommended for the user. This process's performance is analyzed using the metrics accuracy, data handling rate, mining time, and overhead.


Assuntos
Mineração de Dados , Turismo , Algoritmos , Humanos , Bases de Conhecimento
4.
Phys Med Biol ; 67(18)2022 Sep 12.
Artigo em Inglês | MEDLINE | ID: mdl-36093921

RESUMO

Objective.To establish an open framework for developing plan optimization models for knowledge-based planning (KBP).Approach.Our framework includes radiotherapy treatment data (i.e. reference plans) for 100 patients with head-and-neck cancer who were treated with intensity-modulated radiotherapy. That data also includes high-quality dose predictions from 19 KBP models that were developed by different research groups using out-of-sample data during the OpenKBP Grand Challenge. The dose predictions were input to four fluence-based dose mimicking models to form 76 unique KBP pipelines that generated 7600 plans (76 pipelines × 100 patients). The predictions and KBP-generated plans were compared to the reference plans via: the dose score, which is the average mean absolute voxel-by-voxel difference in dose; the deviation in dose-volume histogram (DVH) points; and the frequency of clinical planning criteria satisfaction. We also performed a theoretical investigation to justify our dose mimicking models.Main results.The range in rank order correlation of the dose score between predictions and their KBP pipelines was 0.50-0.62, which indicates that the quality of the predictions was generally positively correlated with the quality of the plans. Additionally, compared to the input predictions, the KBP-generated plans performed significantly better (P< 0.05; one-sided Wilcoxon test) on 18 of 23 DVH points. Similarly, each optimization model generated plans that satisfied a higher percentage of criteria than the reference plans, which satisfied 3.5% more criteria than the set of all dose predictions. Lastly, our theoretical investigation demonstrated that the dose mimicking models generated plans that are also optimal for an inverse planning model.Significance.This was the largest international effort to date for evaluating the combination of KBP prediction and optimization models. We found that the best performing models significantly outperformed the reference dose and dose predictions. In the interest of reproducibility, our data and code is freely available.


Assuntos
Planejamento da Radioterapia Assistida por Computador , Radioterapia de Intensidade Modulada , Humanos , Bases de Conhecimento , Dosagem Radioterapêutica , Planejamento da Radioterapia Assistida por Computador/métodos , Radioterapia de Intensidade Modulada/métodos , Reprodutibilidade dos Testes
5.
Comput Intell Neurosci ; 2022: 1158509, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-36072713

RESUMO

In this paper, a comprehensive quantitative and biological neural network optimization model of sports industry structure is thoroughly studied and analyzed using knowledge graphs. To address the problems of poor performance interpretability deficiency of knowledge graph-based recommendation methods in the face of relational sparse graphs, a pretraining-based implicit characterization algorithm strategy is proposed for the recall stage, which can solve the problems of difficulty in going online and high delay in the recall stage of the recommendation system while improving the accuracy, and not only this can be applied in the recall stage, but also the sorting and postsorting modules can be used as features. To study the relationship between signaling activity and energy metabolism of pyramidal neurons, an empirical model of the synaptic vesicle cycle is proposed to simulate the synaptic transmission process, the role played by energy metabolism in synaptic transmission is studied from the perspective of feedback control, and the quantitative relationship between neuronal pulse discharge frequency, energy consumption, and information quantity in dendritic integration is analyzed using the cable theory and atrial chamber model. It was found that, when 0 ≤ ε ≤ 0.6, the chaotic region shrinks and eventually disappears with the increase of the memory factor ε; however, when 0.6 ≤ ε ≤ 1 is used, chaos is recreated and the chaotic area gradually increases with the increase of the memory factor ε. This paper conducts comparative experiments on data sets in the recommendation domain and verifies that the proposed model and the feature intersection module can effectively perform feature interaction between items and entities, thus enhancing the recommendation effect.


Assuntos
Algoritmos , Redes Neurais de Computação , Bases de Conhecimento , Neurônios
6.
J Adv Res ; 40: 223-231, 2022 09.
Artigo em Inglês | MEDLINE | ID: mdl-36100329

RESUMO

INTRODUCTION: Neurodegenerative diseases (NDDs) are a series of chronic diseases, which are associated with progressive loss of neuronal structure or function. The complex etiologies of the NDDs remain unclear, thus the prevention and early diagnosis of NDDs are critical to reducing the mortality and morbidity of these diseases. OBJECTIVES: To provide a systematic understanding of the heterogeneity of the risk factors associated with different NDDs (pan-neurodegenerative diseases or pan-NDDs), the knowledgebase is established to facilitate the personalized and knowledge-guided diagnosis, prevention and prediction of NDDs. METHODS: Before data collection, the medical, lifescienceand informatics experts as well as the potential users of the database were consulted and discussed for the scope of data and the classification of risk factors. The PubMed database was used as the resource of the data and knowledge extraction. Risk factors of NDDs were manually collected from literature published between 1975 and 2020. RESULTS: The comprehensive risk factors database for NDDs (NDDRF) was established including 998 single or combined risk factors, 2293 records and 1071 articles relevant to the 14 most common NDDs. The single risk factors are classified into 3 categories, i.e. epidemiological factors (469), genetic factors (324) and biochemical factors (153). Among all the factors, 179 factors are positive and protective, while 880 factors have negative influence for NDDs. The knowledgebase is available at http://sysbio.org.cn/NDDRF/. CONCLUSION: NDDRF provides the structured information and knowledge resource on risk factors of NDDs. It could benefit the future systematic and personalized investigation of pan-NDDs genesis and progression. Meanwhile it may be used for the future explainable artificial intelligence modeling for smart diagnosis and prevention of NDDs.


Assuntos
Doenças Neurodegenerativas , Inteligência Artificial , Humanos , Bases de Conhecimento , Doenças Neurodegenerativas/diagnóstico , Doenças Neurodegenerativas/genética , Doenças Neurodegenerativas/prevenção & controle , Fatores de Risco
7.
Front Immunol ; 13: 923528, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-36091046

RESUMO

Background: With significant advancements in the area of precision medicine, the breadth and complexity of the relevant knowledge in the field has increased significantly. However, the difficulty associated with dynamic modelling and the disorganization of such knowledge hinders its rapid development potential. Results: To overcome the difficulty in using the relational database model for dynamic modelling, and to aid in the organization of precision medicine knowledge, we developed the Mind Mapping Knowledgebase Prototyping (MMKP) tool. The MMKP implements a novel design that we call a "polymorphic foreign key", which allows the establishment of a logical linkage between a single table field and a record from any table. This design has advantages in supporting dynamic changes to the structural relationships in precision medicine knowledge. Knowledge stored in MMKP is presented as a mind map to facilitate human interaction. When using this tool, medical experts may curate the structure and content of the precision knowledge in a flow that is similar to the human thinking process. Conclusions: The design of polymorphic foreign keys natively supports knowledge modelling in the form of mind mapping, which avoids the hard-coding of medical logic into a rigid database schema and significantly reduces the workload that is required for adapting a relational data model to future changes to the medical logic. The MMKP tool provides a graphical user interface for both data management and knowledgebase prototyping. It supports the flexible customization of the data field constraints and annotations. MMKP is available as open-source code on GitHub: https://github.com/ZjuLiangsl/mmkp.


Assuntos
Bases de Conhecimento , Medicina de Precisão , Bases de Dados Factuais , Humanos , Software
8.
Commun Biol ; 5(1): 899, 2022 Sep 02.
Artigo em Inglês | MEDLINE | ID: mdl-36056235

RESUMO

The process of identifying suitable genome-wide association (GWA) studies and formatting the data to calculate multiple polygenic risk scores on a single genome can be laborious. Here, we present a centralized polygenic risk score calculator currently containing over 250,000 genetic variant associations from the NHGRI-EBI GWAS Catalog for users to easily calculate sample-specific polygenic risk scores with comparable results to other available tools. Polygenic risk scores are calculated either online through the Polygenic Risk Score Knowledge Base (PRSKB; https://prs.byu.edu ) or via a command-line interface. We report study-specific polygenic risk scores across the UK Biobank, 1000 Genomes, and the Alzheimer's Disease Neuroimaging Initiative (ADNI), contextualize computed scores, and identify potentially confounding genetic risk factors in ADNI. We introduce a streamlined analysis tool and web interface to calculate and contextualize polygenic risk scores across various studies, which we anticipate will facilitate a wider adaptation of polygenic risk scores in future disease research.


Assuntos
Estudo de Associação Genômica Ampla , Herança Multifatorial , Predisposição Genética para Doença , Humanos , Bases de Conhecimento , Polimorfismo de Nucleotídeo Único , Fatores de Risco
9.
BMJ Open ; 12(9): e067204, 2022 09 13.
Artigo em Inglês | MEDLINE | ID: mdl-36100301

RESUMO

INTRODUCTION: Despite a higher risk of severe COVID-19 disease in individuals with HIV, the interactions between SARS-CoV-2 and HIV infections remain unclear. To delineate these interactions, multicentre Electronic Health Records (EHR) hold existing promise to provide full-spectrum and longitudinal clinical data, demographics and sociobehavioural data at individual level. Presently, a comprehensive EHR-based cohort for the HIV/SARS-CoV-2 coinfection has not been established; EHR integration and data mining methods tailored for studying the coinfection are urgently needed yet remain underdeveloped. METHODS AND ANALYSIS: The overarching goal of this exploratory/developmental study is to establish an EHR-based cohort for individuals with HIV/SARS-CoV-2 coinfection and perform large-scale EHR-based data mining to examine the interactions between HIV and SARS-CoV-2 infections and systematically identify and validate factors contributing to the severe clinical course of the coinfection. We will use a nationwide EHR database in the USA, namely, National COVID Cohort Collaborative (N3C). Ultimately, collected clinical evidence will be implemented and used to pilot test a clinical decision support prototype to assist providers in screening and referral of at-risk patients in real-world clinics. ETHICS AND DISSEMINATION: The study was approved by the institutional review boards at the University of South Carolina (Pro00121828) as non-human subject study. Study findings will be presented at academic conferences and published in peer-reviewed journals. This study will disseminate urgently needed clinical evidence for guiding clinical practice for individuals with the coinfection at Prisma Health, a healthcare system in collaboration.


Assuntos
COVID-19 , Coinfecção , Infecções por HIV , COVID-19/epidemiologia , Coinfecção/epidemiologia , Mineração de Dados , Registros Eletrônicos de Saúde , Infecções por HIV/complicações , Infecções por HIV/epidemiologia , Humanos , Bases de Conhecimento , SARS-CoV-2
10.
Lakartidningen ; 1192022 09 14.
Artigo em Sueco | MEDLINE | ID: mdl-36106743

RESUMO

Skewed information about medicines in social media influence the healthcare-patient contact. Healthcare staff need situation adapted evidence that can be linked to patient data. For 20 years Sweden has provided praised Pharmacological Knowledge Bases (PKB). They include ¼Janusmed drug-drug interactions«, ¼Janusmed drugs and birth defects« and ¼e-Ped (electronic pediatric) instructions and drug dosage control«. PKBs need to be better integrated into digital tools adhering to a national guide for optimal interface presentation of information. They should be produced by medical editors and delivered through a national digital highway. Experts need to adhere to a policy for handling conflicts of interest and evaluate that information is appreciated and used. PKBs should be accessible as a public good for healthcare staff, students and the public to support personalized medical care.


Assuntos
Bases de Conhecimento , Mídias Sociais , Criança , Atenção à Saúde , Humanos , Suécia
11.
Brief Bioinform ; 23(5)2022 Sep 20.
Artigo em Inglês | MEDLINE | ID: mdl-36070624

RESUMO

Drug-drug interactions (DDIs) prediction is a challenging task in drug development and clinical application. Due to the extremely large complete set of all possible DDIs, computer-aided DDIs prediction methods are getting lots of attention in the pharmaceutical industry and academia. However, most existing computational methods only use single perspective information and few of them conduct the task based on the biomedical knowledge graph (BKG), which can provide more detailed and comprehensive drug lateral side information flow. To this end, a deep learning framework, namely DeepLGF, is proposed to fully exploit BKG fusing local-global information to improve the performance of DDIs prediction. More specifically, DeepLGF first obtains chemical local information on drug sequence semantics through a natural language processing algorithm. Then a model of BFGNN based on graph neural network is proposed to extract biological local information on drug through learning embedding vector from different biological functional spaces. The global feature information is extracted from the BKG by our knowledge graph embedding method. In DeepLGF, for fusing local-global features well, we designed four aggregating methods to explore the most suitable ones. Finally, the advanced fusing feature vectors are fed into deep neural network to train and predict. To evaluate the prediction performance of DeepLGF, we tested our method in three prediction tasks and compared it with state-of-the-art models. In addition, case studies of three cancer-related and COVID-19-related drugs further demonstrated DeepLGF's superior ability for potential DDIs prediction. The webserver of the DeepLGF predictor is freely available at http://120.77.11.78/DeepLGF/.


Assuntos
COVID-19 , Reconhecimento Automatizado de Padrão , COVID-19/tratamento farmacológico , Interações Medicamentosas , Humanos , Bases de Conhecimento , Redes Neurais de Computação
12.
Radiat Oncol ; 17(1): 151, 2022 Aug 29.
Artigo em Inglês | MEDLINE | ID: mdl-36038941

RESUMO

BACKGROUND: We developed a novel concept, equivalent uniform length (EUL), to describe the relationship between the generalized equivalent uniform dose (EUD) and the geometric anatomy around a tumor target. By correlating EUL with EUD, we established two EUD-EUL knowledge-based (EEKB) prediction models for the bladder and rectum that predict initial EUD values for generating quality treatment plans. METHODS: EUL metrics for the rectum and bladder were extracted and collected from the intensity-modulated radiotherapy therapy (IMRT) plans of 60 patients with cervical cancer. The two EEKB prediction models were built using linear regression to establish the relationships between EULr and EUDr (EUL and EUD of rectum) and EULb, and EUDb (EUL and EUD of bladder), respectively. The EE plans were optimized by incorporating the predicted initial EUD parameters for the rectum and bladder with the conventional pinnacle auto-planning (PAP) initial dose parameters for other organs. The efficiency of the predicted initial EUD values were then evaluated by comparing the consistency and quality of the EE plans, PAP plans (based on default PAP initial parameters), and manual plans (designed manually by different dosimetrists) for a sample of 20 patients. RESULTS: Linear regression analyses showed a significant correlation between EUL and EUD (R2 = 0.79 and 0.69 for EUDb and EUDr, respectively). In a sample of 20 patients, the average bladder V40 and V50 derived from the EE plans were significantly lower (V40: 30.00 ± 5.76, V50: 14.36 ± 4.00) than the V40 and V50 values derived from manual plans (V40: 36.03 ± 8.02, V50: 19.02 ± 5.42). Compared with the PAP plans, the EE plans produced significantly lower average V30 and Dmean values for the bladder (V30: 50.55 ± 6.33, Dmean: 31.48 ± 1.97 Gy). CONCLUSIONS: Our EEKB prediction models predicted reasonable initial EUD values for the rectum and bladder based on patient-specific geometric EUL values, thereby improving optimization and planning efficiency.


Assuntos
Radioterapia de Intensidade Modulada , Neoplasias do Colo do Útero , Feminino , Humanos , Bases de Conhecimento , Dosagem Radioterapêutica , Planejamento da Radioterapia Assistida por Computador , Radioterapia de Intensidade Modulada/efeitos adversos , Reto , Neoplasias do Colo do Útero/radioterapia
13.
J Mol Diagn ; 24(10): 1051-1063, 2022 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-35931343

RESUMO

The goals of the Association for Molecular Pathology Clinical Practice Committee's Pharmacogenomics (PGx) Working Group are to define the key attributes of pharmacogenetic alleles recommended for clinical testing and a minimum set of variants that should be included in clinical PGx genotyping assays. This article provides recommendations for a minimum panel of variant alleles (Tier 1) and an extended panel of variant alleles (Tier 2) that will aid clinical laboratories when designing assays for PGx testing. The Association for Molecular Pathology PGx Working Group considered the functional impact of the variant alleles, allele frequencies in multiethnic populations, the availability of reference materials, as well as other technical considerations for PGx testing when developing these recommendations. The ultimate goal of this Working Group is to promote standardization of PGx gene/allele testing across clinical laboratories. This article focuses on clinical TPMT and NUDT15 PGx testing, which may be applied to all thiopurine S-methyltransferase (TPMT) and nudix hydrolase 15 (NUDT15)-related medications. These recommendations are not to be interpreted as prescriptive, but to provide a reference guide.


Assuntos
Patologia Molecular , Farmacogenética , Consenso , Genótipo , Humanos , Bases de Conhecimento , Metiltransferases , Patologistas , Farmacêuticos
14.
Zhongguo Zhong Yao Za Zhi ; 47(12): 3402-3408, 2022 Jun.
Artigo em Chinês | MEDLINE | ID: mdl-35851136

RESUMO

Chinese medicine pharmaceutical industry is in the process of digital and intelligent transformation. Intelligent methods are required for efficient analysis and mining of the valuable information in the history data including literature data, pharmaceutical big data, and expert knowledge. Therefore, it is urgent to establish a knowledge-driven intelligent system of pharmaceutical technologies of Chinese medicine for efficient supplying of high-quality Chinese medicinal products. The present study proposed the construction method of the knowledge base of Chinese medicine manufacturing, which was preliminarily established from literature mining, case-based reasoning, and real-time prediction based on vacuum belt drying process optimization. Integrating the technologies(such as deep learning, case-based reasoning, and simulation modeling), pharmaceutical mechanisms, and big data, the knowledge base of Chinese medicine manufacturing can realize knowledge automation and scientific decision-making. It provides an example for upgrading from experience-based manufacturing to intelligent Chinese medicine manufacturing.


Assuntos
Medicamentos de Ervas Chinesas , Medicina Tradicional Chinesa , Bases de Conhecimento , Controle de Qualidade , Tecnologia Farmacêutica
15.
J Med Internet Res ; 24(7): e37928, 2022 07 27.
Artigo em Inglês | MEDLINE | ID: mdl-35896020

RESUMO

BACKGROUND: A clinical decision support system (CDSS) is recognized as a technology that enhances clinical efficacy and safety. However, its full potential has not been realized, mainly due to clinical data standards and noninteroperable platforms. OBJECTIVE: In this paper, we introduce the common data model-based intelligent algorithm network environment (CANE) platform that supports the implementation and deployment of a CDSS. METHODS: CDSS reasoning engines, usually represented as R or Python objects, are deployed into the CANE platform and converted into C# objects. When a clinician requests CANE-based decision support in the electronic health record (EHR) system, patients' information is transformed into Health Level 7 Fast Healthcare Interoperability Resources (FHIR) format and transmitted to the CANE server inside the hospital firewall. Upon receiving the necessary data, the CANE system's modules perform the following tasks: (1) the preprocessing module converts the FHIRs into the input data required by the specific reasoning engine, (2) the reasoning engine module operates the target algorithms, (3) the integration module communicates with the other institutions' CANE systems to request and transmit a summary report to aid in decision support, and (4) creates a user interface by integrating the summary report and the results calculated by the reasoning engine. RESULTS: We developed a CANE system such that any algorithm implemented in the system can be directly called through the RESTful application programming interface when it is integrated with an EHR system. Eight algorithms were developed and deployed in the CANE system. Using a knowledge-based algorithm, physicians can screen patients who are prone to sepsis and obtain treatment guides for patients with sepsis with the CANE system. Further, using a nonknowledge-based algorithm, the CANE system supports emergency physicians' clinical decisions about optimum resource allocation by predicting a patient's acuity and prognosis during triage. CONCLUSIONS: We successfully developed a common data model-based platform that adheres to medical informatics standards and could aid artificial intelligence model deployment using R or Python.


Assuntos
Sistemas de Apoio a Decisões Clínicas , Sepse , Inteligência Artificial , Registros Eletrônicos de Saúde , Nível Sete de Saúde , Humanos , Bases de Conhecimento
16.
Sensors (Basel) ; 22(14)2022 Jul 11.
Artigo em Inglês | MEDLINE | ID: mdl-35890866

RESUMO

Aiming at early detection of subsurface cracks induced by contact fatigue in rotating machinery, the knowledge-based data analysis algorithm is proposed for health condition monitoring through the analysis of acoustic emission (AE) time series. A robust fault detector is proposed, and its effectiveness was demonstrated for the long-term durability test of a roller made of case-hardened steel. The reliability of subsurface crack detection was proven using independent ultrasonic inspections carried out periodically during the test. Subsurface cracks as small as 0.5 mm were identified, and their steady growth was tracked by the proposed AE technique. Challenges and perspectives of the proposed methodology are unveiled and discussed.


Assuntos
Acústica , Algoritmos , Teste de Materiais , Humanos , Bases de Conhecimento , Reprodutibilidade dos Testes
17.
PLoS One ; 17(7): e0271737, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35877764

RESUMO

More than 30 types of amyloids are linked to close to 50 diseases in humans, the most prominent being Alzheimer's disease (AD). AD is brain-related local amyloidosis, while another amyloidosis, such as AA amyloidosis, tends to be more systemic. Therefore, we need to know more about the biological entities' influencing these amyloidosis processes. However, there is currently no support system developed specifically to handle this extraordinarily complex and demanding task. To acquire a systematic view of amyloidosis and how this may be relevant to the brain and other organs, we needed a means to explore "amyloid network systems" that may underly processes that leads to an amyloid-related disease. In this regard, we developed the DES-Amyloidoses knowledgebase (KB) to obtain fast and relevant information regarding the biological network related to amyloid proteins/peptides and amyloid-related diseases. This KB contains information obtained through text and data mining of available scientific literature and other public repositories. The information compiled into the DES-Amyloidoses system based on 19 topic-specific dictionaries resulted in 796,409 associations between terms from these dictionaries. Users can explore this information through various options, including enriched concepts, enriched pairs, and semantic similarity. We show the usefulness of the KB using an example focused on inflammasome-amyloid associations. To our knowledge, this is the only KB dedicated to human amyloid-related diseases derived primarily through literature text mining and complemented by data mining that provides a novel way of exploring information relevant to amyloidoses.


Assuntos
Doença de Alzheimer , Amiloidose , Amiloide , Humanos , Bases de Conhecimento , Proteína Amiloide A Sérica
18.
J Appl Clin Med Phys ; 23(8): e13704, 2022 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-35791594

RESUMO

PURPOSE: Knowledge-based planning (KBP) has been shown to be an effective tool in quality control for intensity-modulated radiation therapy treatment planning and generating high-quality plans. Previous studies have evaluated its ability to create consistent plans across institutions and between planners within the same institution as well as its use as teaching tool for inexperienced planners. This study evaluates whether planning quality is consistent when using a KBP model to plan across different treatment machines. MATERIALS AND METHODS: This study used a RapidPlan model (Varian Medical Systems) provided by the vendor, to which we added additional planning objectives, maximum dose limits, and planning structures, such that a clinically acceptable plan is achieved in a single optimization. This model was used to generate and optimize volumetric-modulated arc therapy plans for a cohort of 50 patients treated for head-neck cancer. Plans were generated using the following treatment machines: Varian 2100, Elekta Versa HD, and Varian Halcyon. A noninferiority testing methodology was used to evaluate the hypothesis that normal and target metrics in our autoplans were no worse than a set of clinically-acceptable baseline plans by a margin of 1.8 Gy or 3% dose-volume. The quality of these plans were also compared through the use of common clinical dose-volume histogram criteria. RESULTS: The Versa HD met our noninferiority criteria for 23 of 34 normal and target metrics; while the Halcyon and Varian 2100 machines met our criteria for 24 of 34 and 26 of 34 metrics, respectively. The experimental plans tended to have less volume coverage for prescription dose planning target volume and larger hotspot volumes. However, comparable plans were generated across different treatment machines. CONCLUSIONS: These results support the use of a head-neck RapidPlan models in centralized planning workflows that support clinics with different linac models/vendors, although some fine-tuning for targets may be necessary.


Assuntos
Neoplasias de Cabeça e Pescoço , Radioterapia de Intensidade Modulada , Neoplasias de Cabeça e Pescoço/radioterapia , Humanos , Bases de Conhecimento , Órgãos em Risco , Dosagem Radioterapêutica , Planejamento da Radioterapia Assistida por Computador/métodos , Radioterapia de Intensidade Modulada/métodos
19.
J Biomed Inform ; 132: 104133, 2022 08.
Artigo em Inglês | MEDLINE | ID: mdl-35840060

RESUMO

The emergence of large-scale phenotypic, genetic, and other multi-model biochemical data has offered unprecedented opportunities for drug discovery including drug repurposing. Various knowledge graph-based methods have been developed to integrate and analyze complex and heterogeneous data sources to find new therapeutic applications for existing drugs. However, existing methods have limitations in modeling and capturing context-sensitive inter-relationships among tens of thousands of biomedical entities. In this paper, we developed KG-Predict: a knowledge graph computational framework for drug repurposing. We first integrated multiple types of entities and relations from various genotypic and phenotypic databases to construct a knowledge graph termed GP-KG. GP-KG was composed of 1,246,726 associations between 61,146 entities. KG-Predict then aggregated the heterogeneous topological and semantic information from GP-KG to learn low-dimensional representations of entities and relations, and further utilized these representations to infer new drug-disease interactions. In cross-validation experiments, KG-Predict achieved high performances [AUROC (the area under receiver operating characteristic) = 0.981, AUPR (the area under precision-recall) = 0.409 and MRR (the mean reciprocal rank) = 0.261], outperforming other state-of-art graph embedding methods. We applied KG-Predict in identifying novel repositioned candidate drugs for Alzheimer's disease (AD) and showed that KG-Predict prioritized both FDA-approved and active clinical trial anti-AD drugs among the top (AUROC = 0.868 and AUPR = 0.364).


Assuntos
Reposicionamento de Medicamentos , Reconhecimento Automatizado de Padrão , Conhecimento , Bases de Conhecimento , Semântica
20.
AMIA Annu Symp Proc ; 2022: 432-438, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35854751

RESUMO

To advance the application of clinical data to address maternal health we developed and implemented a Maternal Child Knowledgebase (MCK). The MCK integrates data from every pregnancy that received care at the University of Iowa Hospitals & Clinics (UIHC) and links information from the pregnancy episode to the delivery episode and between the mother and child. This knowledgebase contains integrated information regarding diagnoses, medications, mother and child vitals, hospital admissions, depression screenings, laboratory value results, and procedure information. It also collates information from the electronic health record (EPIC), the Social Security Death Index, and the Medication Administration Record into one knowledgebase. To enhance usability, we designed a custom viewer with several pre-designed queries and reports that eliminates the need for users to be proficient in SQL coding. The recent implementation of the MCK has supported multiple projects and reduced the number of Obstetrics-related data queries to the Biomedical Informatics group.


Assuntos
Registros Eletrônicos de Saúde , Bases de Conhecimento , Feminino , Humanos , Recém-Nascido , Gravidez
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