scholarly journals Performance of African-ancestry-specific polygenic hazard score varies according to local ancestry in 8q24

2021 ◽  
Author(s):  
Roshan A. Karunamuni ◽  
Minh-Phuong Huynh-Le ◽  
Chun C. Fan ◽  
Wesley Thompson ◽  
Asona Lui ◽  
...  
Keyword(s):  
Proportional Hazards ◽  
African Ancestry ◽  
Prostate Cancer Risk ◽  
Model Performance ◽  
Principal Component ◽  
Cox Proportional Hazards ◽  
Risk Scores ◽  
Improve Model ◽  
Improve Model Performance ◽  
Improve Health

AbstractWe previously developed an African-ancestry-specific polygenic hazard score (PHS46+African) that substantially improved prostate cancer risk stratification in men with African ancestry. The model consists of 46 SNPs identified in Europeans and 3 SNPs from 8q24 shown to improve model performance in Africans. Herein, we used principal component (PC) analysis to uncover subpopulations of men with African ancestry for whom the utility of PHS46+African may differ. Genotypic data were obtained from PRACTICAL consortium for 6,253 men with African genetic ancestry. Genetic variation in a window spanning 3 African-specific 8q24 SNPs was estimated using 93 PCs. A Cox proportional hazards framework was used to identify the pair of PCs most strongly associated with performance of PHS46+African. A calibration factor (CF) was formulated using estimated Cox coefficients to quantify the extent to which the performance of PHS46+African varies with PC. CF of PHS46+African was strongly associated with the first and twentieth PCs. Predicted CF ranged from 0.41 to 2.94, suggesting that PHS46+African may be up to 7 times more beneficial to some African men than others. The explained relative risk for PHS46+African varied from 3.6% to 9.9% for individuals with low and high CF values, respectively. By cross-referencing our dataset with 1000 Genomes, we identified statistically significant associations between continental and calibration groupings. In conclusion, we identified PCs within 8q24 SNP window that were strongly associated with performance of PHS46+African. Further research to improve clinical utility of polygenic risk scores (or models) is needed to improve health outcomes for men of African ancestry

scholarly journals Identification of a prognostic ferroptosis-related lncRNA signature in the tumor microenvironment of lung adenocarcinoma

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Yugang Guo ◽  
Zhongyu Qu ◽  
Dandan Li ◽  
Fanghui Bai ◽  
Juan Xing ◽  
...  
Keyword(s):  
High Risk ◽  
Lung Adenocarcinoma ◽  
Proportional Hazards ◽  
Risk Model ◽  
Principal Component ◽  
Cox Proportional Hazards ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Poor Overall Survival

AbstractFerroptosis is closely linked to various cancers, including lung adenocarcinoma (LUAD); however, the factors involved in the regulation of ferroptosis-related genes are not well established. In this study, we identified and characterized ferroptosis-related long noncoding RNAs (lncRNAs) in LUAD. In particular, a coexpression network of ferroptosis-related mRNAs and lncRNAs from The Cancer Genome Atlas (TCGA) was constructed. Univariate and multivariate Cox proportional hazards analyses were performed to establish a prognostic ferroptosis-related lncRNA signature (FerRLSig). We obtained a prognostic risk model consisting of 10 ferroptosis-related lncRNAs: AL606489.1, AC106047.1, LINC02081, AC090559.1, AC026355.1, FAM83A-AS1, AL034397.3, AC092171.5, AC010980.2, and AC123595.1. High risk scores according to the FerRLSig were significantly associated with poor overall survival (hazard ratio (HR) = 1.412, 95% CI = 1.271–1.568; P < 0.001). Receiver operating characteristic (ROC) curves and a principal component analysis further supported the accuracy of the model. Next, a prognostic nomogram combining FerRLSig with clinical features was established and showed favorable predictive efficacy for survival risk stratification. In addition, gene set enrichment analysis (GSEA) revealed that FerRLSig is involved in many malignancy-associated immunoregulatory pathways. Based on the risk model, we found that the immune status and response to immunotherapy, chemotherapy, and targeted therapy differed significantly between the high-risk and low-risk groups. These results offer novel insights into the pathogenesis of LUAD, including the contribution of ferroptosis-related lncRNAs, and reveal a prognostic indicator with the potential to inform immunological research and treatment.

scholarly journals Towards Robust Representations of Spatial Networks Using Graph Neural Networks

2021 ◽  
Vol 11 (15) ◽  
pp. 6918
Author(s):  
Chidubem Iddianozie ◽  
Gavin McArdle
Keyword(s):  
Machine Learning ◽  
Model Performance ◽  
Network Models ◽  
Data Representation ◽  
Spatial Networks ◽  
Neural Network Models ◽  
Improve Model ◽  
Graph Neural Networks ◽  
Spatial Entities ◽  
Improve Model Performance

The effectiveness of a machine learning model is impacted by the data representation used. Consequently, it is crucial to investigate robust representations for efficient machine learning methods. In this paper, we explore the link between data representations and model performance for inference tasks on spatial networks. We argue that representations which explicitly encode the relations between spatial entities would improve model performance. Specifically, we consider homogeneous and heterogeneous representations of spatial networks. We recognise that the expressive nature of the heterogeneous representation may benefit spatial networks and could improve model performance on certain tasks. Thus, we carry out an empirical study using Graph Neural Network models for two inference tasks on spatial networks. Our results demonstrate that heterogeneous representations improves model performance for down-stream inference tasks on spatial networks.

scholarly journals Population Pharmacokinetics of Doxycycline in Children

2019 ◽  
Vol 63 (12) ◽  
Author(s):  
Elizabeth J. Thompson ◽  
Huali Wu ◽  
Chiara Melloni ◽  
Stephen Balevic ◽  
Janice E. Sullivan ◽  
...  
Keyword(s):  
Model Performance ◽  
Standard Of Care ◽  
Volume Of Distribution ◽  
Clinical Value ◽  
Final Model ◽  
Childhood Infections ◽  
Obesity Status ◽  
Improve Model ◽  
Using Data ◽  
Improve Model Performance

ABSTRACT Doxycycline is a tetracycline-class antimicrobial labeled by the U.S. Food and Drug Administration for children >8 years of age for many common childhood infections. Doxycycline is not labeled for children ≤8 years of age, due to the association between tetracycline-class antibiotics and tooth staining, although doxycycline may be used off-label under severe conditions. Accordingly, there is a paucity of pharmacokinetic (PK) data to guide dosing in children 8 years and younger. We leveraged opportunistically collected plasma samples after intravenous (i.v.) and oral doxycycline doses received per standard of care to characterize the PK of doxycycline in children of different ages and evaluated the effect of obesity and fasting status on PK parameters. We developed a population PK model of doxycycline using data collected from 47 patients 0 to 18 years of age, including 14 participants ≤8 years. We developed a 1-compartment PK model and found doxycycline clearance to be 3.32 liters/h/70 kg of body weight and volume to be 96.8 liters/70 kg for all patients, comparable to values reported in adults. We estimated a bioavailability of 89.6%, also consistent with adult data. Allometrically scaled clearance and volume of distribution did not differ between children 2 to ≤8 years of age and children >8 to ≤18 years of age, suggesting that younger children may be given the same per-kilogram dosing. Obesity status and fasting status were not selected for inclusion in the final model. Additional doxycycline PK samples collected in future studies may be used to improve model performance and maximize its clinical value.

scholarly journals Improving biomedical named entity recognition with syntactic information

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Yuanhe Tian ◽  
Wang Shen ◽  
Yan Song ◽  
Fei Xia ◽  
Min He ◽  
...  
Keyword(s):  
Named Entity Recognition ◽  
Model Performance ◽  
Entity Recognition ◽  
Input Word ◽  
Named Entity ◽  
Improve Model ◽  
Syntactic Information ◽  
Biomedical Texts ◽  
Improve Model Performance ◽  
Biomedical Named Entity Recognition

Abstract Background Biomedical named entity recognition (BioNER) is an important task for understanding biomedical texts, which can be challenging due to the lack of large-scale labeled training data and domain knowledge. To address the challenge, in addition to using powerful encoders (e.g., biLSTM and BioBERT), one possible method is to leverage extra knowledge that is easy to obtain. Previous studies have shown that auto-processed syntactic information can be a useful resource to improve model performance, but their approaches are limited to directly concatenating the embeddings of syntactic information to the input word embeddings. Therefore, such syntactic information is leveraged in an inflexible way, where inaccurate one may hurt model performance. Results In this paper, we propose BioKMNER, a BioNER model for biomedical texts with key-value memory networks (KVMN) to incorporate auto-processed syntactic information. We evaluate BioKMNER on six English biomedical datasets, where our method with KVMN outperforms the strong baseline method, namely, BioBERT, from the previous study on all datasets. Specifically, the F1 scores of our best performing model are 85.29% on BC2GM, 77.83% on JNLPBA, 94.22% on BC5CDR-chemical, 90.08% on NCBI-disease, 89.24% on LINNAEUS, and 76.33% on Species-800, where state-of-the-art performance is obtained on four of them (i.e., BC2GM, BC5CDR-chemical, NCBI-disease, and Species-800). Conclusion The experimental results on six English benchmark datasets demonstrate that auto-processed syntactic information can be a useful resource for BioNER and our method with KVMN can appropriately leverage such information to improve model performance.

Potential for developing independent daytime/nighttime LUR models based on short-term mobile monitoring to improve model performance

2021 ◽  
Vol 268 ◽  
pp. 115951
Author(s):  
Xiangyu Xu ◽  
Ning Qin ◽  
Zhenchun Yang ◽  
Yunwei Liu ◽  
Suzhen Cao ◽  
...  
Keyword(s):  
Model Performance ◽  
Mobile Monitoring ◽  
Short Term ◽  
Improve Model ◽  
Improve Model Performance

scholarly journals Probing Behavior of Diaphorina citri (Hemiptera: Liviidae) on Valencia Orange Influenced by Sex, Color, and Size

2020 ◽  
Vol 20 (2) ◽  
Author(s):  
Timothy A Ebert ◽  
Michael E Rogers
Keyword(s):  
Body Size ◽  
Model Performance ◽  
Diaphorina Citri ◽  
Probing Behavior ◽  
Valencia Orange ◽  
Improve Model ◽  
Candidatus Liberibacter ◽  
Citrus Huanglongbing ◽  
Liberibacter Asiaticus ◽  
Improve Model Performance

Abstract Candidatus Liberibacter asiaticus Jagoueix, Bové, and Garnier (Rhizobiales: Rhizobiaceae) is transmitted by the psyllid Diaphorina citri Kuwayama and putatively causes Huanglongbing disease in citrus. Huanglongbing has reduced yields by 68% relative to pre-disease yields in Florida. Disease management is partly through vector control. Understanding vector biology is essential in this endeavor. Our goal was to document differences in probing behavior linked to sex. Based on both a literature review and our results, we conclude that there is either no effect of sex or that identifying such an effect requires a sample size at least four times larger than standard methodologies. Including both color and sex in statistical models did not improve model performance. Both sex and color are correlated with body size, and body size has not been considered in previous studies on sex in D. citri in terms of probing behavior. An effect of body size was found wherein larger psyllids took longer to reach ingestion behaviors and larger individuals spent more time-ingesting phloem, but these relationships explained little of the variability in these data. We suggest that the effects of sex can be ignored when running EPG experiments on healthy psyllids.

scholarly journals Nomogram for Predicting the Benefit of Adjuvant Chemoradiotherapy for Resected Gallbladder Cancer

2011 ◽  
Vol 29 (35) ◽  
pp. 4627-4632 ◽  
Author(s):  
Samuel J. Wang ◽  
Andrew Lemieux ◽  
Jayashree Kalpathy-Cramer ◽  
Celine B. Ord ◽  
Gary V. Walker ◽  
...  
Keyword(s):  
Adjuvant Chemotherapy ◽  
Gallbladder Cancer ◽  
Survival Benefit ◽  
Decision Aid ◽  
Proportional Hazards ◽  
Model Performance ◽  
Cox Proportional Hazards ◽  
Survival Model ◽  
Adjuvant Chemoradiotherapy ◽  
Medicare Database

Purpose Although adjuvant chemoradiotherapy for resected gallbladder cancer may improve survival for some patients, identifying which patients will benefit remains challenging because of the rarity of this disease. The specific aim of this study was to create a decision aid to help make individualized estimates of the potential survival benefit of adjuvant chemoradiotherapy for patients with resected gallbladder cancer. Methods Patients with resected gallbladder cancer were selected from the Surveillance, Epidemiology, and End Results (SEER) –Medicare database who were diagnosed between 1995 and 2005. Covariates included age, race, sex, stage, and receipt of adjuvant chemotherapy or chemoradiotherapy (CRT). Propensity score weighting was used to balance covariates between treated and untreated groups. Several types of multivariate survival regression models were constructed and compared, including Cox proportional hazards, Weibull, exponential, log-logistic, and lognormal models. Model performance was compared using the Akaike information criterion. The primary end point was overall survival with or without adjuvant chemotherapy or CRT. Results A total of 1,137 patients met the inclusion criteria for the study. The lognormal survival model showed the best performance. A Web browser–based nomogram was built from this model to make individualized estimates of survival. The model predicts that certain subsets of patients with at least T2 or N1 disease will gain a survival benefit from adjuvant CRT, and the magnitude of benefit for an individual patient can vary. Conclusion A nomogram built from a parametric survival model from the SEER-Medicare database can be used as a decision aid to predict which gallbladder patients may benefit from adjuvant CRT.

Il-8 as an underutilized prognostic factor in metastatic colorectal cancer.

2014 ◽  
Vol 32 (3_suppl) ◽  
pp. 409-409
Author(s):  
Manasi S Shah ◽  
David R. Fogelman ◽  
Carrie Daniel-MacDougall ◽  
Kanwal Pratap Singh Raghav ◽  
John Heymach ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Metastatic Colorectal Cancer ◽  
Proportional Hazards ◽  
Principal Component ◽  
Cox Proportional Hazards ◽  
Mortality Data ◽  
Subsequent Work ◽  
Term Survival ◽  
Inflammatory Biomarker ◽  
Kaplan Meier

409 Background: Cancer-associated inflammation has been identified as a key determinant of disease progression and survival in colorectal cancer. We investigated the association between circulating inflammatory cytokines and survival in metastatic colorectal cancer (mCRC) patients. Methods: Plasma levels of 47 cytokines were measured using multiplex-bead assays in a cohort of 168 previously untreated mCRC patients. Demographic, clinical-pathological features, body mass index, and mortality data were abstracted from patient medical records. Overall survival (OS) was evaluated by Kaplan-Meier analysis and Cox proportional hazards regression. Results: Using principal component analysis, we identified a subset of cytokines explaining the maximum variance in OS; and found interleukin (IL)-1b, IL-5, IL-8, IL-12 and VEGF to be significantly associated with OS. However, only IL-8 was significantly and independently associated with OS in multivariable-adjusted models. For each 100 pg/ml increase in the level of circulating IL-8, hazard rate for death increased by 1.6 (95% CI 1.24-1.97). IL-8 measurements ranged from <1 to 413 pg/ml with a median value of 22 pg/ml. Median uncensored survival was 26.5 and 15.5 months among patients with IL-8 levels below and above this value, respectively. ROC analysis of IL-8 demonstrated an AUC of 0.69 (95% CI 0.60-0.76), as compared to 0.52 for CEA (95% CI 0.46-0.59). Conclusions: We identified an association between IL-8 and OS in previously untreated mCRC patients, suggesting its potential role as a prognostic inflammatory biomarker. In this dataset, IL-8 outperformed CEA as a prognostic biomarker, a finding which requires validation in subsequent work. Appropriately identifying, monitoring and managing chronic inflammation and the host inflammatory response during colorectal cancer treatment may be important for improving long-term survival.

scholarly journals Phospholipid Levels at Seroconversion are Associated with Resolution of Persistent Islet Autoimmunity: The Diabetes Autoimmunity Study in the Young

2021 ◽  
Author(s):  
Patrick M. Carry ◽  
Lauren A. Vanderlinden ◽  
Randi K. Johnson ◽  
Teresa Buckner ◽  
Oliver Fiehn ◽  
...  
Keyword(s):  
Proportional Hazards ◽  
Principal Component ◽  
Enrichment Analysis ◽  
Cox Proportional Hazards ◽  
Hazard Ratio ◽  
Islet Autoimmunity ◽  
Potential Influence ◽  
Serum Samples ◽  
Sugar Intake ◽  
Cox Proportional Hazards Models

Reversion of islet autoimmunity (IA) may point to mechanisms that prevent IA progression. We followed 199 individuals who developed IA during the Diabetes Autoimmunity Study in the Young. Untargeted metabolomics was performed in serum samples following IA. Cox-proportional hazards models were used to test if the metabolites (2,487) predicted IA reversion, two or more consecutive visits negative for all autoantibodies. We conducted a principal component analysis (PCA) of the top metabolites, |hazard ratio (HR) >1.25| and nominal p<0.01. Phosphatidylcholine (16:0_18:1(9Z) was the strongest individual metabolite (hazard ratio (HR) per 1 standard deviation: 2.16, FDR adjusted p=0.0037). Enrichment analysis identified four clusters (FDR p<0.10) characterized by an overabundance of sphingomyelin (d40:0), phosphatidylcholine (16:0_18:1(9Z)), phosphatidylcholine (30:0), and L-decanoylcarnitine. Overall, 63 metabolites met the criteria for inclusion in the PCA. PC1 (HR: 1.4, p<0.0001), PC2 (HR: 0.85, p=0.0185), and PC4 (HR: 1.28, p=0.0103) were associated with IA reversion. Given the potential influence of diet on the metabolome, we investigated whether nutrients were correlated with PCs. We identified 20 nutrients that were correlated with the PCs (p<0.05). Total sugar intake was the top nutrient. Overall, we identified an association between phosphatidylcholine, sphingomyelin, and carnitine levels and reversion of IA.

Sign in / Sign up

Export Citation Format

Share Document