scholarly journals COGENT: evaluating the consistency of gene co-expression networks

2020 ◽  
Author(s):  
Lyuba V Bozhilova ◽  
Javier Pardo-Diaz ◽  
Gesine Reinert ◽  
Charlotte M Deane
Keyword(s):  
External Validation ◽  
Construction Method ◽  
Supplementary Information ◽  
Validation Data ◽  
Network Construction

Abstract Summary Gene co-expression networks can be constructed in multiple different ways, both in the use of different measures of co-expression, and in the thresholds applied to the calculated co-expression values, from any given dataset. It is often not clear which co-expression network construction method should be preferred. COGENT provides a set of tools designed to aid the choice of network construction method without the need for any external validation data. Availability and implementation https://github.com/lbozhilova/COGENT Supplementary information Supplementary information is available at Bioinformatics online.

scholarly journals COGENT: evaluating the consistency of gene co-expression networks

2020 ◽  
Author(s):  
Lyuba V. Bozhilova ◽  
Javier Pardo-Diaz ◽  
Gesine Reinert ◽  
Charlotte M. Deane
Keyword(s):  
External Validation ◽  
Construction Method ◽  
Validation Data ◽  
Network Construction ◽  
Link Type

AbstractGene co-expression networks can be constructed in multiple different ways, both in the use of different measures of co-expression, and in the thresholds applied to the calculated co-expression values, from any given dataset. It is often not clear which co-expression network construction method should be preferred. COGENT provides a set of tools designed to aid the choice of network construction method without the need for any external validation data.Availability and implementationhttps://github.com/lbozhilova/COGENT

scholarly journals Development and temporal external validation of a simple risk score tool for prediction of outcomes after severe head injury based on admission characteristics from level-1 trauma centre of India using retrospectively collected data

BMJ Open ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. e040778
Author(s):  
Vineet Kumar Kamal ◽  
Ravindra Mohan Pandey ◽  
Deepak Agrawal
Keyword(s):  
Hospital Mortality ◽  
External Validation ◽  
Trauma Centre ◽  
Unfavourable Outcome ◽  
Motor Score ◽  
Validation Data ◽  
Data Set ◽  
Development Data ◽  
Level 1 ◽  
Pupillary Reactivity

ObjectiveTo develop and validate a simple risk scores chart to estimate the probability of poor outcomes in patients with severe head injury (HI).DesignRetrospective.SettingLevel-1, government-funded trauma centre, India.ParticipantsPatients with severe HI admitted to the neurosurgery intensive care unit during 19 May 2010–31 December 2011 (n=946) for the model development and further, data from same centre with same inclusion criteria from 1 January 2012 to 31 July 2012 (n=284) for the external validation of the model.Outcome(s)In-hospital mortality and unfavourable outcome at 6 months.ResultsA total of 39.5% and 70.7% had in-hospital mortality and unfavourable outcome, respectively, in the development data set. The multivariable logistic regression analysis of routinely collected admission characteristics revealed that for in-hospital mortality, age (51–60, >60 years), motor score (1, 2, 4), pupillary reactivity (none), presence of hypotension, basal cistern effaced, traumatic subarachnoid haemorrhage/intraventricular haematoma and for unfavourable outcome, age (41–50, 51–60, >60 years), motor score (1–4), pupillary reactivity (none, one), unequal limb movement, presence of hypotension were the independent predictors as its 95% confidence interval (CI) of odds ratio (OR)_did not contain one. The discriminative ability (area under the receiver operating characteristic curve (95% CI)) of the score chart for in-hospital mortality and 6 months outcome was excellent in the development data set (0.890 (0.867 to 912) and 0.894 (0.869 to 0.918), respectively), internal validation data set using bootstrap resampling method (0.889 (0.867 to 909) and 0.893 (0.867 to 0.915), respectively) and external validation data set (0.871 (0.825 to 916) and 0.887 (0.842 to 0.932), respectively). Calibration showed good agreement between observed outcome rates and predicted risks in development and external validation data set (p>0.05).ConclusionFor clinical decision making, we can use of these score charts in predicting outcomes in new patients with severe HI in India and similar settings.

scholarly journals A Novel Information Theoretical Criterion for Climate Network Construction

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1500
Author(s):  
Sara Cornejo-Bueno ◽  
Mihaela I. Chidean ◽  
Antonio J. Caamaño ◽  
Luis Prieto-Godino ◽  
Sancho Salcedo-Sanz
Keyword(s):  
Wind Speed ◽  
Wind Farms ◽  
Construction Method ◽  
Membership Probability ◽  
Network Construction ◽  
Wind Speed Prediction ◽  
Emergent Behaviour ◽  
Symmetry Properties ◽  
Leibler Divergence ◽  
Speed Prediction

This paper presents a novel methodology for Climate Network (CN) construction based on the Kullback-Leibler divergence (KLD) among Membership Probability (MP) distributions, obtained from the Second Order Data-Coupled Clustering (SODCC) algorithm. The proposed method is able to obtain CNs with emergent behaviour adapted to the variables being analyzed, and with a low number of spurious or missing links. We evaluate the proposed method in a problem of CN construction to assess differences in wind speed prediction at different wind farms in Spain. The considered problem presents strong local and mesoscale relationships, but low synoptic scale relationships, which have a direct influence in the CN obtained. We carry out a comparison of the proposed approach with a classical correlation-based CN construction method. We show that the proposed approach based on the SODCC algorithm and the KLD constructs CNs with an emergent behaviour according to underlying wind speed prediction data physics, unlike the correlation-based method that produces spurious and missing links. Furthermore, it is shown that the climate network construction method facilitates the evaluation of symmetry properties in the resulting complex networks.

scholarly journals INeo-Epp: A novel T-cell HLA class-I immunogenicity or neoantigenic epitope prediction method based on sequence related amino acid features

2019 ◽  
Author(s):  
Guangzhi Wang ◽  
Huihui Wan ◽  
Xingxing Jian ◽  
Yuyu Li ◽  
Jian Ouyang ◽  
...  
Keyword(s):  
Amino Acid ◽  
T Cell ◽  
Antigen Processing ◽  
External Validation ◽  
Cell Epitope ◽  
Epitope Prediction ◽  
Validation Data ◽  
Data Set ◽  
Immunogenic Peptide ◽  
Related Amino Acid

AbstractIn silico T-cell epitope prediction plays an important role in immunization experimental design and vaccine preparation. Currently, most epitope prediction research focuses on peptide processing and presentation, e.g. proteasomal cleavage, transporter associated with antigen processing (TAP) and major histocompatibility complex (MHC) combination. To date, however, the mechanism for immunogenicity of epitopes remains unclear. It is generally agreed upon that T-cell immunogenicity may be influenced by the foreignness, accessibility, molecular weight, molecular structure, molecular conformation, chemical properties and physical properties of target peptides to different degrees. In this work, we tried to combine these factors. Firstly, we collected significant experimental HLA-I T-cell immunogenic peptide data, as well as the potential immunogenic amino acid properties. Several characteristics were extracted, including amino acid physicochemical property of epitope sequence, peptide entropy, eluted ligand likelihood percentile rank (EL rank(%)) score and frequency score for immunogenic peptide. Subsequently, a random forest classifier for T cell immunogenic HLA-I presenting antigen epitopes and neoantigens was constructed. The classification results for the antigen epitopes outperformed the previous research (the optimal AUC=0.81, external validation data set AUC=0.77). As mutational epitopes generated by the coding region contain only the alterations of one or two amino acids, we assume that these characteristics might also be applied to the classification of the endogenic mutational neoepitopes also called ‘neoantigens’. Based on mutation information and sequence related amino acid characteristics, a prediction model of neoantigen was established as well (the optimal AUC=0.78). Further, an easy-to-use web-based tool ‘INeo-Epp’ was developed (available at http://www.biostatistics.online/INeo-Epp/neoantigen.php)for the prediction of human immunogenic antigen epitopes and neoantigen epitopes.

Validating vascular access data in the Swedish Renal Registry SRR

2020 ◽  
pp. 112972982095473
Author(s):  
Gunilla Welander ◽  
Birgitta Sigvant
Keyword(s):  
Vascular Access ◽  
Medical Records ◽  
External Validation ◽  
Internal Validity ◽  
Validation Data ◽  
Internal Validation ◽  
Clinical Utilization ◽  
Surgical Units ◽  
National Patient ◽  
Access Data

Background: All Swedish dialysis units register data on vascular access in the Swedish Renal Registry (SRR). This study assessed external and internal validity of vascular access data in the SRR and its use as a tool in clinical practice. Methods: For external validation, all procedures for placed fistulas, open and endovascular reinterventions registered in the SRR in 2011 to 2017 were cross-matched with data from the Swedish National Patient Registry. A two-stage sampling selected 12/60 dialysis units for internal validation. Data on current vascular access for 10 randomly selected patients at each unit were compared with medical record data. SRR data on placed fistulas from 2017 were cross-checked with data from local surgical units. Registrations of central venous catheters (CVCs) as temporary or permanent were used as a proxy for clinical utilization of the registry and analyzed separately. Results: External validity increased from 74% to 83% during the observation period. In all, 1037 datapoints were used in internal validation, with a 95% match between SRR registrations and medical records. Registrations of CVCs, fistulas, and interventions were reliable, with few missing data or mismatches. Vascular access type initiating hemodialysis was missing or incorrect in either the SRR or medical records for 14/120 patients. Registrations of placed fistulas in 2017 matched in all but four (pre-dialysis stage) of 135 cases. Some 35% of the CVCs validated ( n = 49) at 7/12 units were not categorized as temporary or permanent. Conclusion: The SRR provides a reliable resource on current vascular access care.

scholarly journals D3GRN: a data driven dynamic network construction method to infer gene regulatory networks

BMC Genomics ◽  
2019 ◽  
Vol 20 (S13) ◽  
Author(s):  
Xiang Chen ◽  
Min Li ◽  
Ruiqing Zheng ◽  
Fang-Xiang Wu ◽  
Jianxin Wang
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Dynamic Network ◽  
Construction Method ◽  
Data Driven ◽  
Regression Problem ◽  
Network Construction ◽  
Benchmark Datasets ◽  
Gene Regulatory ◽  
New Perspective

Abstract Background To infer gene regulatory networks (GRNs) from gene-expression data is still a fundamental and challenging problem in systems biology. Several existing algorithms formulate GRNs inference as a regression problem and obtain the network with an ensemble strategy. Recent studies on data driven dynamic network construction provide us a new perspective to solve the regression problem. Results In this study, we propose a data driven dynamic network construction method to infer gene regulatory network (D3GRN), which transforms the regulatory relationship of each target gene into functional decomposition problem and solves each sub problem by using the Algorithm for Revealing Network Interactions (ARNI). To remedy the limitation of ARNI in constructing networks solely from the unit level, a bootstrapping and area based scoring method is taken to infer the final network. On DREAM4 and DREAM5 benchmark datasets, D3GRN performs competitively with the state-of-the-art algorithms in terms of AUPR. Conclusions We have proposed a novel data driven dynamic network construction method by combining ARNI with bootstrapping and area based scoring strategy. The proposed method performs well on the benchmark datasets, contributing as a competitive method to infer gene regulatory networks in a new perspective.

A fast multi-output RBF neural network construction method

2010 ◽  
Vol 73 (10-12) ◽  
pp. 2196-2202 ◽  
Author(s):  
Dajun Du ◽  
Kang Li ◽  
Minrui Fei
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Construction Method ◽  
Network Construction
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