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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.

27.3 match 7 stars 9.11 score 1.3k scripts 6 dependents

mrc-ide

malariasimulation:An individual based model for malaria

Specifies the latest and greatest malaria model.

Maintained by Giovanni Charles. Last updated 28 days ago.

cpp

21.3 match 16 stars 8.17 score 146 scripts

huanglabumn

oncoPredict:Drug Response Modeling and Biomarker Discovery

Allows for building drug response models using screening data between bulk RNA-Seq and a drug response metric and two additional tools for biomarker discovery that have been developed by the Huang Laboratory at University of Minnesota. There are 3 main functions within this package. (1) calcPhenotype is used to build drug response models on RNA-Seq data and impute them on any other RNA-Seq dataset given to the model. (2) GLDS is used to calculate the general level of drug sensitivity, which can improve biomarker discovery. (3) IDWAS can take the results from calcPhenotype and link the imputed response back to available genomic (mutation and CNV alterations) to identify biomarkers. Each of these functions comes from a paper from the Huang research laboratory. Below gives the relevant paper for each function. calcPhenotype - Geeleher et al, Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines. GLDS - Geeleher et al, Cancer biomarker discovery is improved by accounting for variability in general levels of drug sensitivity in pre-clinical models. IDWAS - Geeleher et al, Discovering novel pharmacogenomic biomarkers by imputing drug response in cancer patients from large genomics studies.

Maintained by Robert Gruener. Last updated 12 months ago.

svapreprocesscorestringrbiomartgenefilterorg.hs.eg.dbgenomicfeaturestxdb.hsapiens.ucsc.hg19.knowngenetcgabiolinksbiocgenericsgenomicrangesirangess4vectors

10.8 match 18 stars 6.47 score 41 scripts

kathbaum

DrDimont:Drug Response Prediction from Differential Multi-Omics Networks

While it has been well established that drugs affect and help patients differently, personalized drug response predictions remain challenging. Solutions based on single omics measurements have been proposed, and networks provide means to incorporate molecular interactions into reasoning. However, how to integrate the wealth of information contained in multiple omics layers still poses a complex problem. We present a novel network analysis pipeline, DrDimont, Drug response prediction from Differential analysis of multi-omics networks. It allows for comparative conclusions between two conditions and translates them into differential drug response predictions. DrDimont focuses on molecular interactions. It establishes condition-specific networks from correlation within an omics layer that are then reduced and combined into heterogeneous, multi-omics molecular networks. A novel semi-local, path-based integration step ensures integrative conclusions. Differential predictions are derived from comparing the condition-specific integrated networks. DrDimont's predictions are explainable, i.e., molecular differences that are the source of high differential drug scores can be retrieved. Our proposed pipeline leverages multi-omics data for differential predictions, e.g. on drug response, and includes prior information on interactions. The case study presented in the vignette uses data published by Krug (2020) <doi:10.1016/j.cell.2020.10.036>. The package license applies only to the software and explicitly not to the included data.

Maintained by Katharina Baum. Last updated 2 years ago.

27.2 match 2.00 score 2 scripts

mrcieu

MRInstruments:Data sources for genetic instruments to be used in MR

Datasets of eQTLs, GWAS catalogs, etc.

Maintained by Gibran Hemani. Last updated 5 years ago.

8.0 match 44 stars 5.15 score 212 scripts

gasparrini

dlnm:Distributed Lag Non-Linear Models

Collection of functions for distributed lag linear and non-linear models.

Maintained by Antonio Gasparrini. Last updated 3 years ago.

3.5 match 77 stars 10.30 score 392 scripts 6 dependents

merck

r2rtf:Easily Create Production-Ready Rich Text Format (RTF) Tables and Figures

Create production-ready Rich Text Format (RTF) tables and figures with flexible format.

Maintained by Benjamin Wang. Last updated 6 days ago.

3.3 match 78 stars 10.82 score 171 scripts 10 dependents

usepa

httk:High-Throughput Toxicokinetics

Pre-made models that can be rapidly tailored to various chemicals and species using chemical-specific in vitro data and physiological information. These tools allow incorporation of chemical toxicokinetics ("TK") and in vitro-in vivo extrapolation ("IVIVE") into bioinformatics, as described by Pearce et al. (2017) (<doi:10.18637/jss.v079.i04>). Chemical-specific in vitro data characterizing toxicokinetics have been obtained from relatively high-throughput experiments. The chemical-independent ("generic") physiologically-based ("PBTK") and empirical (for example, one compartment) "TK" models included here can be parameterized with in vitro data or in silico predictions which are provided for thousands of chemicals, multiple exposure routes, and various species. High throughput toxicokinetics ("HTTK") is the combination of in vitro data and generic models. We establish the expected accuracy of HTTK for chemicals without in vivo data through statistical evaluation of HTTK predictions for chemicals where in vivo data do exist. The models are systems of ordinary differential equations that are developed in MCSim and solved using compiled (C-based) code for speed. A Monte Carlo sampler is included for simulating human biological variability (Ring et al., 2017 <doi:10.1016/j.envint.2017.06.004>) and propagating parameter uncertainty (Wambaugh et al., 2019 <doi:10.1093/toxsci/kfz205>). Empirically calibrated methods are included for predicting tissue:plasma partition coefficients and volume of distribution (Pearce et al., 2017 <doi:10.1007/s10928-017-9548-7>). These functions and data provide a set of tools for using IVIVE to convert concentrations from high-throughput screening experiments (for example, Tox21, ToxCast) to real-world exposures via reverse dosimetry (also known as "RTK") (Wetmore et al., 2015 <doi:10.1093/toxsci/kfv171>).

Maintained by John Wambaugh. Last updated 1 months ago.

comptoxord

3.3 match 27 stars 10.22 score 307 scripts 1 dependents

bioc

DeepPINCS:Protein Interactions and Networks with Compounds based on Sequences using Deep Learning

The identification of novel compound-protein interaction (CPI) is important in drug discovery. Revealing unknown compound-protein interactions is useful to design a new drug for a target protein by screening candidate compounds. The accurate CPI prediction assists in effective drug discovery process. To identify potential CPI effectively, prediction methods based on machine learning and deep learning have been developed. Data for sequences are provided as discrete symbolic data. In the data, compounds are represented as SMILES (simplified molecular-input line-entry system) strings and proteins are sequences in which the characters are amino acids. The outcome is defined as a variable that indicates how strong two molecules interact with each other or whether there is an interaction between them. In this package, a deep-learning based model that takes only sequence information of both compounds and proteins as input and the outcome as output is used to predict CPI. The model is implemented by using compound and protein encoders with useful features. The CPI model also supports other modeling tasks, including protein-protein interaction (PPI), chemical-chemical interaction (CCI), or single compounds and proteins. Although the model is designed for proteins, DNA and RNA can be used if they are represented as sequences.

Maintained by Dongmin Jung. Last updated 5 months ago.

softwarenetworkgraphandnetworkneuralnetworkopenjdk

4.5 match 4.78 score 4 scripts 2 dependents

bips-hb

expard:Drug EXPosures and ADRs

An R package for fitting complex drug exposure and adverse drug reaction (ADR) relationships

Maintained by Louis Dijkstra. Last updated 1 months ago.

11.6 match 1 stars 1.81 score 13 scripts

cran

CFO:CFO-Type Designs in Phase I/II Clinical Trials

In phase I clinical trials, the primary objective is to ascertain the maximum tolerated dose (MTD) corresponding to a specified target toxicity rate. The subsequent phase II trials are designed to examine the potential efficacy of the drug based on the MTD obtained from the phase I trials, with the aim of identifying the optimal biological dose (OBD). The 'CFO' package facilitates the implementation of dose-finding trials by utilizing calibration-free odds type (CFO-type) designs. Specifically, it encompasses the calibration-free odds (CFO) (Jin and Yin (2022) <doi:10.1177/09622802221079353>), randomized CFO (rCFO), precision CFO (pCFO), two-dimensional CFO (2dCFO) (Wang et al. (2023) <doi:10.3389/fonc.2023.1294258>), time-to-event CFO (TITE-CFO) (Jin and Yin (2023) <doi:10.1002/pst.2304>), fractional CFO (fCFO), accumulative CFO (aCFO), TITE-aCFO, and f-aCFO (Fang and Yin (2024) <doi: 10.1002/sim.10127>). It supports phase I/II trials for the CFO design and only phase I trials for the other CFO-type designs. The ‘CFO' package accommodates diverse CFO-type designs, allowing users to tailor the approach based on factors such as dose information inclusion, handling of late-onset toxicity, and the nature of the target drug (single-drug or drug-combination). The functionalities embedded in 'CFO' package include the determination of the dose level for the next cohort, the selection of the MTD for a real trial, and the execution of single or multiple simulations to obtain operating characteristics. Moreover, these functions are equipped with early stopping and dose elimination rules to address safety considerations. Users have the flexibility to choose different distributions, thresholds, and cohort sizes among others for their specific needs. The output of the 'CFO' package can be summary statistics as well as various plots for better visualization. An interactive web application for CFO is available at the provided URL.

Maintained by Jialu Fang. Last updated 4 months ago.

10.9 match 1.78 score

cran

drc:Analysis of Dose-Response Curves

Analysis of dose-response data is made available through a suite of flexible and versatile model fitting and after-fitting functions.

Maintained by Christian Ritz. Last updated 9 years ago.

1.6 match 8 stars 8.39 score 1.4k scripts 28 dependents

peterkdunn

GLMsData:Generalized Linear Model Data Sets

Data sets from the book Generalized Linear Models with Examples in R by Dunn and Smyth.

Maintained by Peter K. Dunn. Last updated 3 years ago.

3.8 match 2.61 score 220 scripts

linlf

altmeta:Alternative Meta-Analysis Methods

Provides alternative statistical methods for meta-analysis, including: - bivariate generalized linear mixed models for synthesizing odds ratios, relative risks, and risk differences (Chu et al., 2012 <doi:10.1177/0962280210393712>) - heterogeneity tests and measures and penalization methods that are robust to outliers (Lin et al., 2017 <doi:10.1111/biom.12543>; Wang et al., 2022 <doi:10.1002/sim.9261>); - measures, tests, and visualization tools for publication bias or small-study effects (Lin and Chu, 2018 <doi:10.1111/biom.12817>; Lin, 2019 <doi:10.1002/jrsm.1340>; Lin, 2020 <doi:10.1177/0962280220910172>; Shi et al., 2020 <doi:10.1002/jrsm.1415>); - meta-analysis of combining standardized mean differences and odds ratios (Jing et al., 2023 <doi:10.1080/10543406.2022.2105345>); - meta-analysis of diagnostic tests for synthesizing sensitivities, specificities, etc. (Reitsma et al., 2005 <doi:10.1016/j.jclinepi.2005.02.022>; Chu and Cole, 2006 <doi:10.1016/j.jclinepi.2006.06.011>); - meta-analysis methods for synthesizing proportions (Lin and Chu, 2020 <doi:10.1097/ede.0000000000001232>); - models for multivariate meta-analysis, measures of inconsistency degrees of freedom in Bayesian network meta-analysis, and predictive P-score (Lin and Chu, 2018 <doi:10.1002/jrsm.1293>; Lin, 2020 <doi:10.1080/10543406.2020.1852247>; Rosenberger et al., 2021 <doi:10.1186/s12874-021-01397-5>).

Maintained by Lifeng Lin. Last updated 6 months ago.

jagscpp

6.7 match 1.04 score 11 scripts

linlf

fragility:Assessing and Visualizing Fragility of Clinical Results with Binary Outcomes

A collection of user-friendly functions for assessing and visualizing fragility of individual studies (Walsh et al., 2014 <doi:10.1016/j.jclinepi.2013.10.019>; Lin, 2021 <doi:10.1111/jep.13428>), conventional pairwise meta-analyses (Atal et al., 2019 <doi:10.1016/j.jclinepi.2019.03.012>), and network meta-analyses of multiple treatments with binary outcomes (Xing et al., 2020 <doi:10.1016/j.jclinepi.2020.07.003>). The included functions are designed to: 1) calculate the fragility index (i.e., the minimal event status modifications that can alter the significance or non-significance of the original result) and fragility quotient (i.e., fragility index divided by sample size) at a specific significance level; 2) give the cases of event status modifications for altering the result's significance or non-significance and visualize these cases; 3) visualize the trend of statistical significance as event status is modified; 4) efficiently derive fragility indexes and fragility quotients at multiple significance levels, and visualize the relationship between these fragility measures against the significance levels; and 5) calculate fragility indexes and fragility quotients of multiple datasets (e.g., a collection of clinical trials or meta-analyses) and produce plots of their overall distributions. The outputs from these functions may inform the robustness of clinical results in terms of statistical significance and aid the interpretation of fragility measures. The usage of this package is illustrated in Lin et al. (2023 <doi:10.1016/j.ajog.2022.08.053>) and detailed in Lin and Chu (2022 <doi:10.1371/journal.pone.0268754>).

Maintained by Lifeng Lin. Last updated 2 months ago.

3.6 match 1.48 score 5 scripts

rmaster1

nph:Planning and Analysing Survival Studies under Non-Proportional Hazards

Piecewise constant hazard functions are used to flexibly model survival distributions with non-proportional hazards and to simulate data from the specified distributions. A function to calculate weighted log-rank tests for the comparison of two hazard functions is included. Also, a function to calculate a test using the maximum of a set of test statistics from weighted log-rank tests (MaxCombo test) is provided. This test utilizes the asymptotic multivariate normal joint distribution of the separate test statistics. The correlation is estimated from the data. These methods are described in Ristl et al. (2021) <doi:10.1002/pst.2062>. Finally, a function is provided for the estimation and inferential statistics of various parameters that quantify the difference between two survival curves. Eligible parameters are differences in survival probabilities, log survival probabilities, complementary log log (cloglog) transformed survival probabilities, quantiles of the survival functions, log transformed quantiles, restricted mean survival times, as well as an average hazard ratio, the Cox model score statistic (logrank statistic), and the Cox-model hazard ratio. Adjustments for multiple testing and simultaneous confidence intervals are calculated using a multivariate normal approximation to the set of selected parameters.

Maintained by Robin Ristl. Last updated 3 years ago.

1.2 match 3.89 score 32 scripts 2 dependents

johnros

chords:Estimation in Respondent Driven Samples

Maximum likelihood estimation in respondent driven samples.

Maintained by Jonathan Rosenblatt. Last updated 8 years ago.

3.8 match 1.08 score 12 scripts