Showing 53 of total 53 results (show query)
smartdata-analysis-and-statistics
metamisc:Meta-Analysis of Diagnosis and Prognosis Research Studies
Facilitate frequentist and Bayesian meta-analysis of diagnosis and prognosis research studies. It includes functions to summarize multiple estimates of prediction model discrimination and calibration performance (Debray et al., 2019) <doi:10.1177/0962280218785504>. It also includes functions to evaluate funnel plot asymmetry (Debray et al., 2018) <doi:10.1002/jrsm.1266>. Finally, the package provides functions for developing multivariable prediction models from datasets with clustering (de Jong et al., 2021) <doi:10.1002/sim.8981>.
Maintained by Thomas Debray. Last updated 1 months ago.
meta-analysisprognosisprognostic-models
15.2 match 7 stars 7.48 score 102 scripts
bioc
genefu:Computation of Gene Expression-Based Signatures in Breast Cancer
This package contains functions implementing various tasks usually required by gene expression analysis, especially in breast cancer studies: gene mapping between different microarray platforms, identification of molecular subtypes, implementation of published gene signatures, gene selection, and survival analysis.
Maintained by Benjamin Haibe-Kains. Last updated 4 months ago.
differentialexpressiongeneexpressionvisualizationclusteringclassification
14.4 match 7.42 score 193 scripts 3 dependents
ready4-dev
ready4:Develop and Use Modular Health Economic Models
A template model module, tools to help find model modules derived from this template and a programming syntax to use these modules in health economic analyses. These elements are the foundation for a prototype software framework for developing living and transferable models and using those models in reproducible health economic analyses. The software framework is extended by other R libraries. For detailed documentation about the framework and how to use it visit <https://www.ready4-dev.com/>. For a background to the methodological issues that the framework is attempting to help solve, see Hamilton et al. (2024) <doi:10.1007/s40273-024-01378-8>.
Maintained by Matthew Hamilton. Last updated 4 months ago.
computational-modelinghealth-economicssoftware-framework
6.5 match 2 stars 6.84 score 95 scripts
feakster
QDiabetes:Type 2 Diabetes Risk Calculator
Calculate the risk of developing type 2 diabetes using risk prediction algorithms derived by 'ClinRisk'.
Maintained by Benjamin G. Feakins. Last updated 4 years ago.
clinriskdiabetesdiabetes-predictiondiabetes-riskdiabetes-risk-predictionprognosticqdiabetes-algorithmqtoolsrisk
10.0 match 7 stars 3.85 score 5 scripts
wviechtb
metadat:Meta-Analysis Datasets
A collection of meta-analysis datasets for teaching purposes, illustrating/testing meta-analytic methods, and validating published analyses.
Maintained by Wolfgang Viechtbauer. Last updated 3 days ago.
3.5 match 30 stars 10.54 score 65 scripts 93 dependents
cecileproust-lima
lcmm:Extended Mixed Models Using Latent Classes and Latent Processes
Estimation of various extensions of the mixed models including latent class mixed models, joint latent class mixed models, mixed models for curvilinear outcomes, mixed models for multivariate longitudinal outcomes using a maximum likelihood estimation method (Proust-Lima, Philipps, Liquet (2017) <doi:10.18637/jss.v078.i02>).
Maintained by Cecile Proust-Lima. Last updated 1 months ago.
3.2 match 62 stars 11.41 score 249 scripts 7 dependents
alanarnholt
BSDA:Basic Statistics and Data Analysis
Data sets for book "Basic Statistics and Data Analysis" by Larry J. Kitchens.
Maintained by Alan T. Arnholt. Last updated 2 years ago.
3.3 match 7 stars 9.11 score 1.3k scripts 6 dependents
thothorn
TH.data:TH's Data Archive
Contains data sets used in other packages Torsten Hothorn maintains.
Maintained by Torsten Hothorn. Last updated 2 months ago.
3.6 match 8.28 score 137 scripts 370 dependents
kogalur
randomForestSRC:Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC)
Fast OpenMP parallel computing of Breiman's random forests for univariate, multivariate, unsupervised, survival, competing risks, class imbalanced classification and quantile regression. New Mahalanobis splitting for correlated outcomes. Extreme random forests and randomized splitting. Suite of imputation methods for missing data. Fast random forests using subsampling. Confidence regions and standard errors for variable importance. New improved holdout importance. Case-specific importance. Minimal depth variable importance. Visualize trees on your Safari or Google Chrome browser. Anonymous random forests for data privacy.
Maintained by Udaya B. Kogalur. Last updated 2 months ago.
3.6 match 10 stars 7.90 score 1.2k scripts 12 dependents
markmfredrickson
optmatch:Functions for Optimal Matching
Distance based bipartite matching using minimum cost flow, oriented to matching of treatment and control groups in observational studies ('Hansen' and 'Klopfer' 2006 <doi:10.1198/106186006X137047>). Routines are provided to generate distances from generalised linear models (propensity score matching), formulas giving variables on which to limit matched distances, stratified or exact matching directives, or calipers, alone or in combination.
Maintained by Josh Errickson. Last updated 3 months ago.
1.8 match 47 stars 12.22 score 588 scripts 5 dependents
chengs94
BioPETsurv:Biomarker Prognostic Enrichment Tool for clinical trials with survival outcomes
Prognostic Enrichment is a clinical trial strategy of evaluating an intervention in a patient population with a higher rate of the unwanted clinical event than the broader patient population (R. Temple (2010) DOI:10.1038/clpt.2010.233). A higher event rate translates to a lower sample size for the clinical trial, which can have both practical and ethical advantages. This package provides tools to evaluate biomarkers for prognostic enrichment of clinical trials with survival/time-to-event outcomes.
Maintained by Si Cheng. Last updated 5 years ago.
7.0 match 2.70 score
weiliu123
PCLassoReg:Group Regression Models for Risk Protein Complex Identification
Two protein complex-based group regression models (PCLasso and PCLasso2) for risk protein complex identification. PCLasso is a prognostic model that identifies risk protein complexes associated with survival. PCLasso2 is a classification model that identifies risk protein complexes associated with classes. For more information, see Wang and Liu (2021) <doi:10.1093/bib/bbab212>.
Maintained by Wei Liu. Last updated 3 years ago.
4.5 match 1 stars 3.70 score 1 scripts
ngreifer
cobalt:Covariate Balance Tables and Plots
Generate balance tables and plots for covariates of groups preprocessed through matching, weighting or subclassification, for example, using propensity scores. Includes integration with 'MatchIt', 'WeightIt', 'MatchThem', 'twang', 'Matching', 'optmatch', 'CBPS', 'ebal', 'cem', 'sbw', and 'designmatch' for assessing balance on the output of their preprocessing functions. Users can also specify data for balance assessment not generated through the above packages. Also included are methods for assessing balance in clustered or multiply imputed data sets or data sets with multi-category, continuous, or longitudinal treatments.
Maintained by Noah Greifer. Last updated 11 months ago.
causal-inferencepropensity-scores
1.2 match 75 stars 12.98 score 1.0k scripts 8 dependentsbioc
GSgalgoR:An Evolutionary Framework for the Identification and Study of Prognostic Gene Expression Signatures in Cancer
A multi-objective optimization algorithm for disease sub-type discovery based on a non-dominated sorting genetic algorithm. The 'Galgo' framework combines the advantages of clustering algorithms for grouping heterogeneous 'omics' data and the searching properties of genetic algorithms for feature selection. The algorithm search for the optimal number of clusters determination considering the features that maximize the survival difference between sub-types while keeping cluster consistency high.
Maintained by Carlos Catania. Last updated 5 months ago.
geneexpressiontranscriptionclusteringclassificationsurvival
2.8 match 15 stars 5.48 score 6 scripts
cran
PPLasso:Prognostic Predictive Lasso for Biomarker Selection
We provide new tools for the identification of prognostic and predictive biomarkers. For further details we refer the reader to the paper: Zhu et al. Identification of prognostic and predictive biomarkers in high-dimensional data with PPLasso. BMC Bioinformatics. 2023 Jan 23;24(1):25.
Maintained by Wencan Zhu. Last updated 2 years ago.
7.5 match 2.00 score
zhangh12
multipleOutcomes:Asymptotic Covariance Matrix of Regression Models for Multiple Outcomes
Regression models can be fitted for multiple outcomes simultaneously. This package computes estimates of parameters across fitted models and returns the matrix of asymptotic covariance. Various applications of this package, including PATED (Prognostic Variables Assisted Treatment Effect Detection), multiple comparison adjustment, are illustrated.
Maintained by Han Zhang. Last updated 4 months ago.
3.8 match 3.60 score 1 scriptsbioc
kmcut:Optimized Kaplan Meier analysis and identification and validation of prognostic biomarkers
The purpose of the package is to identify prognostic biomarkers and an optimal numeric cutoff for each biomarker that can be used to stratify a group of test subjects (samples) into two sub-groups with significantly different survival (better vs. worse). The package was developed for the analysis of gene expression data, such as RNA-seq. However, it can be used with any quantitative variable that has a sufficiently large proportion of unique values.
Maintained by Igor Kuznetsov. Last updated 5 months ago.
softwarestatisticalmethodgeneexpressionsurvival
3.3 match 3.60 score 1 scripts
declaredesign
DesignLibrary:Library of Research Designs
A simple interface to build designs using the package 'DeclareDesign'. In one line of code, users can specify the parameters of individual designs and diagnose their properties. The designers can also be used to compare performance of a given design across a range of combinations of parameters, such as effect size, sample size, and assignment probabilities.
Maintained by Jasper Cooper. Last updated 1 months ago.
1.7 match 30 stars 6.30 score 144 scripts
raikens1
stratamatch:Stratification and Matching for Large Observational Data Sets
A pilot matching design to automatically stratify and match large datasets. The manual_stratify() function allows users to manually stratify a dataset based on categorical variables of interest, while the auto_stratify() function does automatically by allocating a held-aside (pilot) data set, fitting a prognostic score (see Hansen (2008) <doi:10.1093/biomet/asn004>) on the pilot set, and stratifying the data set based on prognostic score quantiles. The strata_match() function then does optimal matching of the data set in parallel within strata.
Maintained by Rachael C. Aikens. Last updated 3 years ago.
4.4 match 2.30 score 6 scriptscran
BioPET:Biomarker Prognostic Enrichment Tool
Prognostic Enrichment is a clinical trial strategy of evaluating an intervention in a patient population with a higher rate of the unwanted event than the broader patient population (R. Temple (2010) <DOI:10.1038/clpt.2010.233>). A higher event rate translates to a lower sample size for the clinical trial, which can have both practical and ethical advantages. This package is a tool to help evaluate biomarkers for prognostic enrichment of clinical trials.
Maintained by Jeremy Roth. Last updated 7 years ago.
9.5 match 1.00 score 3 scripts
stochastictree
stochtree:Stochastic Tree Ensembles (XBART and BART) for Supervised Learning and Causal Inference
Flexible stochastic tree ensemble software. Robust implementations of Bayesian Additive Regression Trees (BART) Chipman, George, McCulloch (2010) <doi:10.1214/09-AOAS285> for supervised learning and Bayesian Causal Forests (BCF) Hahn, Murray, Carvalho (2020) <doi:10.1214/19-BA1195> for causal inference. Enables model serialization and parallel sampling and provides a low-level interface for custom stochastic forest samplers.
Maintained by Drew Herren. Last updated 6 hours ago.
bartbayesian-machine-learningbayesian-methodsdecision-treesgradient-boosted-treesmachine-learningprobabilistic-modelstree-ensemblescpp
1.1 match 22 stars 8.57 score 40 scriptsbioc
CPSM:CPSM: Cancer patient survival model
The CPSM package provides a comprehensive computational pipeline for predicting the survival probability of cancer patients. It offers a series of steps including data processing, splitting data into training and test subsets, and normalization of data. The package enables the selection of significant features based on univariate survival analysis and generates a LASSO prognostic index score. It supports the development of predictive models for survival probability using various features and provides visualization tools to draw survival curves based on predicted survival probabilities. Additionally, SPM includes functionalities for generating bar plots that depict the predicted mean and median survival times of patients, making it a versatile tool for survival analysis in cancer research.
Maintained by Harpreet Kaur. Last updated 5 days ago.
geneexpressionnormalizationsurvival
2.3 match 3.90 score
foucher-y
survivalSL:Super Learner for Survival Prediction from Censored Data
Several functions and S3 methods to construct a super learner in the presence of censored times-to-event and to evaluate its prognostic capacities.
Maintained by Yohann Foucher. Last updated 2 months ago.
2.4 match 2 stars 3.70 score
ovgu-sh
desk:Didactic Econometrics Starter Kit
Written to help undergraduate as well as graduate students to get started with R for basic econometrics without the need to import specific functions and datasets from many different sources. Primarily, the package is meant to accompany the German textbook Auer, L.v., Hoffmann, S., Kranz, T. (2024, ISBN: 978-3-662-68263-0) from which the exercises cover all the topics from the textbook Auer, L.v. (2023, ISBN: 978-3-658-42699-6).
Maintained by Soenke Hoffmann. Last updated 11 months ago.
1.8 match 4.30 score 10 scriptsbioc
messina:Single-gene classifiers and outlier-resistant detection of differential expression for two-group and survival problems
Messina is a collection of algorithms for constructing optimally robust single-gene classifiers, and for identifying differential expression in the presence of outliers or unknown sample subgroups. The methods have application in identifying lead features to develop into clinical tests (both diagnostic and prognostic), and in identifying differential expression when a fraction of samples show unusual patterns of expression.
Maintained by Mark Pinese. Last updated 5 months ago.
geneexpressiondifferentialexpressionbiomedicalinformaticsclassificationsurvivalcpp
2.2 match 3.30 score 1 scriptscran
sMSROC:Assessment of Diagnostic and Prognostic Markers
Provides estimations of the Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) based on the two-stages mixed-subjects ROC curve estimator (Diaz-Coto et al. (2020) <doi:10.1515/ijb-2019-0097> and Diaz-Coto et al. (2020) <doi:10.1080/00949655.2020.1736071>).
Maintained by Susana Diaz-Coto. Last updated 1 years ago.
6.6 match 1.00 scorebioc
SigCheck:Check a gene signature's prognostic performance against random signatures, known signatures, and permuted data/metadata
While gene signatures are frequently used to predict phenotypes (e.g. predict prognosis of cancer patients), it it not always clear how optimal or meaningful they are (cf David Venet, Jacques E. Dumont, and Vincent Detours' paper "Most Random Gene Expression Signatures Are Significantly Associated with Breast Cancer Outcome"). Based on suggestions in that paper, SigCheck accepts a data set (as an ExpressionSet) and a gene signature, and compares its performance on survival and/or classification tasks against a) random gene signatures of the same length; b) known, related and unrelated gene signatures; and c) permuted data and/or metadata.
Maintained by Rory Stark. Last updated 1 months ago.
geneexpressionclassificationgenesetenrichment
2.7 match 2.00 score 1 scriptscran
BioPred:An R Package for Biomarkers Analysis in Precision Medicine
Provides functions for training extreme gradient boosting model using propensity score A-learning and weight-learning methods. For further details, see Liu et al. (2024) <doi:10.1093/bioinformatics/btae592>.
Maintained by Zihuan Liu. Last updated 4 months ago.
1.8 match 3.00 scorebioc
PDATK:Pancreatic Ductal Adenocarcinoma Tool-Kit
Pancreatic ductal adenocarcinoma (PDA) has a relatively poor prognosis and is one of the most lethal cancers. Molecular classification of gene expression profiles holds the potential to identify meaningful subtypes which can inform therapeutic strategy in the clinical setting. The Pancreatic Cancer Adenocarcinoma Tool-Kit (PDATK) provides an S4 class-based interface for performing unsupervised subtype discovery, cross-cohort meta-clustering, gene-expression-based classification, and subsequent survival analysis to identify prognostically useful subtypes in pancreatic cancer and beyond. Two novel methods, Consensus Subtypes in Pancreatic Cancer (CSPC) and Pancreatic Cancer Overall Survival Predictor (PCOSP) are included for consensus-based meta-clustering and overall-survival prediction, respectively. Additionally, four published subtype classifiers and three published prognostic gene signatures are included to allow users to easily recreate published results, apply existing classifiers to new data, and benchmark the relative performance of new methods. The use of existing Bioconductor classes as input to all PDATK classes and methods enables integration with existing Bioconductor datasets, including the 21 pancreatic cancer patient cohorts available in the MetaGxPancreas data package. PDATK has been used to replicate results from Sandhu et al (2019) [https://doi.org/10.1200/cci.18.00102] and an additional paper is in the works using CSPC to validate subtypes from the included published classifiers, both of which use the data available in MetaGxPancreas. The inclusion of subtype centroids and prognostic gene signatures from these and other publications will enable researchers and clinicians to classify novel patient gene expression data, allowing the direct clinical application of the classifiers included in PDATK. Overall, PDATK provides a rich set of tools to identify and validate useful prognostic and molecular subtypes based on gene-expression data, benchmark new classifiers against existing ones, and apply discovered classifiers on novel patient data to inform clinical decision making.
Maintained by Benjamin Haibe-Kains. Last updated 5 months ago.
geneexpressionpharmacogeneticspharmacogenomicssoftwareclassificationsurvivalclusteringgeneprediction
1.0 match 1 stars 4.31 score 17 scripts
rvaradhan
anoint:Analysis of Interactions
The tools in this package are intended to help researchers assess multiple treatment-covariate interactions with data from a parallel-group randomized controlled clinical trial. The methods implemented in the package were proposed in Kovalchik, Varadhan and Weiss (2013) <doi: 10.1002/sim.5881>.
Maintained by Ravi Varadhan. Last updated 6 months ago.
3.6 match 1.15 score 14 scripts
sahirbhatnagar
casebase:Fitting Flexible Smooth-in-Time Hazards and Risk Functions via Logistic and Multinomial Regression
Fit flexible and fully parametric hazard regression models to survival data with single event type or multiple competing causes via logistic and multinomial regression. Our formulation allows for arbitrary functional forms of time and its interactions with other predictors for time-dependent hazards and hazard ratios. From the fitted hazard model, we provide functions to readily calculate and plot cumulative incidence and survival curves for a given covariate profile. This approach accommodates any log-linear hazard function of prognostic time, treatment, and covariates, and readily allows for non-proportionality. We also provide a plot method for visualizing incidence density via population time plots. Based on the case-base sampling approach of Hanley and Miettinen (2009) <DOI:10.2202/1557-4679.1125>, Saarela and Arjas (2015) <DOI:10.1111/sjos.12125>, and Saarela (2015) <DOI:10.1007/s10985-015-9352-x>.
Maintained by Sahir Bhatnagar. Last updated 7 months ago.
competing-riskscox-regressionregression-modelssurvival-analysis
0.5 match 9 stars 7.16 score 94 scripts
ddsjoberg
dcurves:Decision Curve Analysis for Model Evaluation
Diagnostic and prognostic models are typically evaluated with measures of accuracy that do not address clinical consequences. Decision-analytic techniques allow assessment of clinical outcomes, but often require collection of additional information may be cumbersome to apply to models that yield a continuous result. Decision curve analysis is a method for evaluating and comparing prediction models that incorporates clinical consequences, requires only the data set on which the models are tested, and can be applied to models that have either continuous or dichotomous results. See the following references for details on the methods: Vickers (2006) <doi:10.1177/0272989X06295361>, Vickers (2008) <doi:10.1186/1472-6947-8-53>, and Pfeiffer (2020) <doi:10.1002/bimj.201800240>.
Maintained by Daniel D. Sjoberg. Last updated 8 months ago.
0.5 match 40 stars 6.77 score 95 scripts
atanubhattacharjee
highMLR:Feature Selection for High Dimensional Survival Data
Perform high dimensional Feature Selection in the presence of survival outcome. Based on Feature Selection method and different survival analysis, it will obtain the best markers with optimal threshold levels according to their effect on disease progression and produce the most consistent level according to those threshold values. The functions' methodology is based on by Sonabend et al (2021) <doi:10.1093/bioinformatics/btab039> and Bhattacharjee et al (2021) <arXiv:2012.02102>.
Maintained by Atanu Bhattacharjee. Last updated 3 years ago.
3.3 match 1.00 scorecran
longROC:Time-Dependent Prognostic Accuracy with Multiply Evaluated Bio Markers or Scores
Time-dependent Receiver Operating Characteristic curves, Area Under the Curve, and Net Reclassification Indexes for repeated measures. It is based on methods in Barbati and Farcomeni (2017) <doi:10.1007/s10260-017-0410-2>.
Maintained by Alessio Farcomeni. Last updated 7 years ago.
2.8 match 1.08 score 12 scripts
glenmartin31
predRupdate:Prediction Model Validation and Updating
Evaluate the predictive performance of an existing (i.e. previously developed) prediction/ prognostic model given relevant information about the existing prediction model (e.g. coefficients) and a new dataset. Provides a range of model updating methods that help tailor the existing model to the new dataset; see Su et al. (2018) <doi:10.1177/0962280215626466>. Techniques to aggregate multiple existing prediction models on the new data are also provided; see Debray et al. (2014) <doi:10.1002/sim.6080> and Martin et al. (2018) <doi:10.1002/sim.7586>).
Maintained by Glen P. Martin. Last updated 7 months ago.
0.5 match 7 stars 5.62 score 9 scripts
miriamesteve
GSSTDA:Progression Analysis of Disease with Survival using Topological Data Analysis
Mapper-based survival analysis with transcriptomics data is designed to carry out. Mapper-based survival analysis is a modification of Progression Analysis of Disease (PAD) where survival data is taken into account in the filtering function. More details in: J. Fores-Martos, B. Suay-Garcia, R. Bosch-Romeu, M.C. Sanfeliu-Alonso, A. Falco, J. Climent, "Progression Analysis of Disease with Survival (PAD-S) by SurvMap identifies different prognostic subgroups of breast cancer in a large combined set of transcriptomics and methylation studies" <doi:10.1101/2022.09.08.507080>.
Maintained by Miriam Esteve. Last updated 8 months ago.
0.5 match 2 stars 5.15 score 7 scriptsbioc
SVMDO:Identification of Tumor-Discriminating mRNA Signatures via Support Vector Machines Supported by Disease Ontology
It is an easy-to-use GUI using disease information for detecting tumor/normal sample discriminating gene sets from differentially expressed genes. Our approach is based on an iterative algorithm filtering genes with disease ontology enrichment analysis and wilk and wilks lambda criterion connected to SVM classification model construction. Along with gene set extraction, SVMDO also provides individual prognostic marker detection. The algorithm is designed for FPKM and RPKM normalized RNA-Seq transcriptome datasets.
Maintained by Mustafa Erhan Ozer. Last updated 5 months ago.
genesetenrichmentdifferentialexpressionguiclassificationrnaseqtranscriptomicssurvivalmachine-learningrna-seqshiny
0.5 match 4.60 score 2 scriptsbioc
iPath:iPath pipeline for detecting perturbed pathways at individual level
iPath is the Bioconductor package used for calculating personalized pathway score and test the association with survival outcomes. Abundant single-gene biomarkers have been identified and used in the clinics. However, hundreds of oncogenes or tumor-suppressor genes are involved during the process of tumorigenesis. We believe individual-level expression patterns of pre-defined pathways or gene sets are better biomarkers than single genes. In this study, we devised a computational method named iPath to identify prognostic biomarker pathways, one sample at a time. To test its utility, we conducted a pan-cancer analysis across 14 cancer types from The Cancer Genome Atlas and demonstrated that iPath is capable of identifying highly predictive biomarkers for clinical outcomes, including overall survival, tumor subtypes, and tumor stage classifications. We found that pathway-based biomarkers are more robust and effective than single genes.
Maintained by Kenong Su. Last updated 5 months ago.
pathwayssoftwaregeneexpressionsurvivalcpp
0.5 match 2 stars 4.60 score 3 scriptsbioc
RLassoCox:A reweighted Lasso-Cox by integrating gene interaction information
RLassoCox is a package that implements the RLasso-Cox model proposed by Wei Liu. The RLasso-Cox model integrates gene interaction information into the Lasso-Cox model for accurate survival prediction and survival biomarker discovery. It is based on the hypothesis that topologically important genes in the gene interaction network tend to have stable expression changes. The RLasso-Cox model uses random walk to evaluate the topological weight of genes, and then highlights topologically important genes to improve the generalization ability of the Lasso-Cox model. The RLasso-Cox model has the advantage of identifying small gene sets with high prognostic performance on independent datasets, which may play an important role in identifying robust survival biomarkers for various cancer types.
Maintained by Wei Liu. Last updated 5 months ago.
survivalregressiongeneexpressiongenepredictionnetwork
0.5 match 3 stars 4.48 score 2 scriptsbioc
consICA:consensus Independent Component Analysis
consICA implements a data-driven deconvolution method – consensus independent component analysis (ICA) to decompose heterogeneous omics data and extract features suitable for patient diagnostics and prognostics. The method separates biologically relevant transcriptional signals from technical effects and provides information about the cellular composition and biological processes. The implementation of parallel computing in the package ensures efficient analysis of modern multicore systems.
Maintained by Petr V. Nazarov. Last updated 5 months ago.
technologystatisticalmethodsequencingrnaseqtranscriptomicsclassificationfeatureextraction
0.5 match 4.30 score 2 scripts
statapps
bhm:Biomarker Threshold Models
Contains tools to fit both predictive and prognostic biomarker effects using biomarker threshold models and continuous threshold models. Evaluate the treatment effect, biomarker effect and treatment-biomarker interaction using probability index measurement. Test for treatment-biomarker interaction using residual bootstrap method.
Maintained by Bingshu E. Chen. Last updated 4 months ago.
0.5 match 1 stars 3.40 score 9 scripts
syedhaider5
SIMMS:Subnetwork Integration for Multi-Modal Signatures
Algorithms to create prognostic biomarkers using biological genesets or networks.
Maintained by Syed Haider. Last updated 3 years ago.
0.6 match 2.30 score 20 scripts
hanjunwei-lab
pathwayTMB:Pathway Based Tumor Mutational Burden
A systematic bioinformatics tool to develop a new pathway-based gene panel for tumor mutational burden (TMB) assessment (pathway-based tumor mutational burden, PTMB), using somatic mutations files in an efficient manner from either The Cancer Genome Atlas sources or any in-house studies as long as the data is in mutation annotation file (MAF) format. Besides, we develop a multiple machine learning method using the sample's PTMB profiles to identify cancer-specific dysfunction pathways, which can be a biomarker of prognostic and predictive for cancer immunotherapy.
Maintained by Junwei Han. Last updated 3 years ago.
0.5 match 2.48 score 2 scripts 1 dependents
cran
SubgrpID:Patient Subgroup Identification for Clinical Drug Development
Implementation of Sequential BATTing (bootstrapping and aggregating of thresholds from trees) for developing threshold-based multivariate (prognostic/predictive) biomarker signatures. Variable selection is automatically built-in. Final signatures are returned with interaction plots for predictive signatures. Cross-validation performance evaluation and testing dataset results are also output. Detail algorithms are described in Huang et al (2017) <doi:10.1002/sim.7236>.
Maintained by Xin Huang. Last updated 1 years ago.
0.5 match 1.00 score