close

Showing 11 of total 11 results (show query)

bioc

immunogenViewer:Visualization and evaluation of protein immunogens

Plots protein properties and visualizes position of peptide immunogens within protein sequence. Allows evaluation of immunogens based on structural and functional annotations to infer suitability for antibody-based methods aiming to detect native proteins.

Maintained by Katharina Waury. Last updated 1 months ago.

featureextractionproteomicssoftwarevisualization

25.2 match 4.65 score 10 scripts

pharmaverse

pharmaversesdtm:SDTM Test Data for the 'Pharmaverse' Family of Packages

A set of Study Data Tabulation Model (SDTM) datasets from the Clinical Data Interchange Standards Consortium (CDISC) pilot project used for testing and developing Analysis Data Model (ADaM) datasets inside the pharmaverse family of packages. SDTM dataset specifications are described in the CDISC SDTM implementation guide, accessible by creating a free account on <https://www.cdisc.org/>.

Maintained by Edoardo Mancini. Last updated 3 days ago.

7.3 match 15 stars 7.40 score 143 scripts

vavrycuk-zz

PoDBAY:Vaccine Efficacy Estimation Package

Set of functions that implement the PoDBAY method, described in the publication 'A method to estimate probability of disease and vaccine efficacy from clinical trial immunogenicity data' by Julie Dudasova, Regina Laube, Chandni Valiathan, Matthew C. Wiener, Ferdous Gheyas, Pavel Fiser, Justina Ivanauskaite, Frank Liu and Jeffrey R. Sachs (NPJ Vaccines, 2021), <doi:10.1038/s41541-021-00377-6>.

Maintained by Julie Dudasova (MSD). Last updated 3 years ago.

cpp

8.6 match 3.30 score 10 scripts

cran

PepMapViz:A Versatile Toolkit for Peptide Mapping, Visualization, and Comparative Exploration

A versatile R visualization package that empowers researchers with comprehensive visualization tools for seamlessly mapping peptides to protein sequences, identifying distinct domains and regions of interest, accentuating mutations, and highlighting post-translational modifications, all while enabling comparisons across diverse experimental conditions. Potential applications of 'PepMapViz' include the visualization of cross-software mass spectrometry results at the peptide level for specific protein and domain details in a linearized format and post-translational modification coverage across different experimental conditions; unraveling insights into disease mechanisms. It also enables visualization of major histocompatibility complex-presented peptides in different antibody regions predicting immunogenicity in antibody drug development.

Maintained by Zhenru Zhou. Last updated 4 months ago.

immunogenicitymassspectrometryproteomicspeptidomicssoftwarevisualization

10.5 match 2.70 score

pharmaverse

admiralvaccine:Vaccine Extension Package for ADaM in 'R' Asset Library

Programming vaccine specific Clinical Data Interchange Standards Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in 'R'. Flat model is followed as per Center for Biologics Evaluation and Research (CBER) guidelines for creating vaccine specific domains. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team (2021), <https://www.cdisc.org/standards/foundational/adam/adamig-v1-3-release-package>). The package is an extension package of the 'admiral' package.

Maintained by Sukalpo Saha. Last updated 2 months ago.

cdisc

3.4 match 6 stars 7.44 score 23 scripts

mjuraska

CoRpower:Power Calculations for Assessing Correlates of Risk in Clinical Efficacy Trials

Calculates power for assessment of intermediate biomarker responses as correlates of risk in the active treatment group in clinical efficacy trials, as described in Gilbert, Janes, and Huang, Power/Sample Size Calculations for Assessing Correlates of Risk in Clinical Efficacy Trials (2016, Statistics in Medicine). The methods differ from past approaches by accounting for the level of clinical treatment efficacy overall and in biomarker response subgroups, which enables the correlates of risk results to be interpreted in terms of potential correlates of efficacy/protection. The methods also account for inter-individual variability of the observed biomarker response that is not biologically relevant (e.g., due to technical measurement error of the laboratory assay used to measure the biomarker response), which is important because power to detect a specified correlate of risk effect size is heavily affected by the biomarker's measurement error. The methods can be used for a general binary clinical endpoint model with a univariate dichotomous, trichotomous, or continuous biomarker response measured in active treatment recipients at a fixed timepoint after randomization, with either case-cohort Bernoulli sampling or case-control without-replacement sampling of the biomarker (a baseline biomarker is handled as a trivial special case). In a specified two-group trial design, the computeN() function can initially be used for calculating additional requisite design parameters pertaining to the target population of active treatment recipients observed to be at risk at the biomarker sampling timepoint. Subsequently, the power calculation employs an inverse probability weighted logistic regression model fitted by the tps() function in the 'osDesign' package. Power results as well as the relationship between the correlate of risk effect size and treatment efficacy can be visualized using various plotting functions. To link power calculations for detecting a correlate of risk and a correlate of treatment efficacy, a baseline immunogenicity predictor (BIP) can be simulated according to a specified classification rule (for dichotomous or trichotomous BIPs) or correlation with the biomarker response (for continuous BIPs), then outputted along with biomarker response data under assignment to treatment, and clinical endpoint data for both treatment and placebo groups.

Maintained by Michal Juraska. Last updated 4 years ago.

4.4 match 4.15 score 14 scripts

bioc

signifinder:Collection and implementation of public transcriptional cancer signatures

signifinder is an R package for computing and exploring a compendium of tumor signatures. It allows to compute a variety of signatures, based on gene expression values, and return single-sample scores. Currently, signifinder contains more than 60 distinct signatures collected from the literature, relating to multiple tumors and multiple cancer processes.

Maintained by Stefania Pirrotta. Last updated 2 months ago.

geneexpressiongenetargetimmunooncologybiomedicalinformaticsrnaseqmicroarrayreportwritingvisualizationsinglecellspatialgenesignaling

2.3 match 7 stars 6.40 score 15 scripts

sistm

vici:Vaccine Induced Cellular Immunogenicity with Bivariate Modeling

A shiny app for accurate estimation of vaccine induced immunogenicity with bivariate linear modeling. Method is detailed in: Lhomme, Hejblum, Lacabaratz, Wiedemann, Lelievre, Levy, Thiebaut & Richert (2019). Submitted.

Maintained by Boris Hejblum. Last updated 9 months ago.

3.4 match 3.30 score 3 scripts

cran

visit:Vaccine Phase I Design with Simultaneous Evaluation of Immunogenicity and Toxicity

Phase I clinical trials are the first step in drug development to test a new drug or drug combination on humans. Typical designs of Phase I trials use toxicity as the primary endpoint and aim to find the maximum tolerable dosage. However, these designs are poorly applicable for the development of cancer therapeutic vaccines because the expected safety concerns for these vaccines are not as much as cytotoxic agents. The primary objectives of a cancer therapeutic vaccine phase I trial thus often include determining whether the vaccine shows biologic activity and the minimum dose necessary to achieve a full immune or even clinical response. This package implements a Bayesian Phase I cancer vaccine trial design that allows simultaneous evaluation of safety and immunogenicity outcomes. See Wang et al. (2019) <DOI:10.1002/sim.8021> for further details.

Maintained by Chenguang Wang. Last updated 2 years ago.

cpp

3.4 match 2.00 score

login-

immunaut:Machine Learning Immunogenicity and Vaccine Response Analysis

Used for analyzing immune responses and predicting vaccine efficacy using machine learning and advanced data processing techniques. 'Immunaut' integrates both unsupervised and supervised learning methods, managing outliers and capturing immune response variability. It performs multiple rounds of predictive model testing to identify robust immunogenicity signatures that can predict vaccine responsiveness. The platform is designed to handle high-dimensional immune data, enabling researchers to uncover immune predictors and refine personalized vaccination strategies across diverse populations.

Maintained by Ivan Tomic. Last updated 5 months ago.

3.4 match 1.70 score

vavrycuk-zz

vaxpmx:Vaccines Pharmacometrics

Estimate vaccine efficacy (VE) using immunogenicity data. The inclusion of immunogenicity data in regression models can increase precision in VE. The methods are described in the publications "Elucidating vaccine efficacy using a correlate of protection, demographics, and logistic regression" and "Improving precision of vaccine efficacy evaluation using immune correlate data in time-to-event models" by Julie Dudasova, Zdenek Valenta, and Jeffrey R. Sachs (2024).

Maintained by Julie Dudasova (MSD). Last updated 4 months ago.

0.8 match 2.00 score

: