Showing 13 of total 13 results (show query)
bioc
bioassayR:Cross-target analysis of small molecule bioactivity
bioassayR is a computational tool that enables simultaneous analysis of thousands of bioassay experiments performed over a diverse set of compounds and biological targets. Unique features include support for large-scale cross-target analyses of both public and custom bioassays, generation of high throughput screening fingerprints (HTSFPs), and an optional preloaded database that provides access to a substantial portion of publicly available bioactivity data.
Maintained by Thomas Girke. Last updated 5 months ago.
immunooncologymicrotitreplateassaycellbasedassaysvisualizationinfrastructuredataimportbioinformaticsproteomicsmetabolomics
14.4 match 5 stars 6.70 score 46 scripts
milesilab
BioRssay:Analyze Bioassays and Probit Graphs
A robust framework for analyzing mortality data from bioassays for one or several strains/lines/populations.
Maintained by Piyal Karunarathne. Last updated 4 months ago.
19.6 match 3 stars 4.18 score 4 scripts
avehtari
aaltobda:Functionality and Data for the Aalto Course on Bayesian Data Analysis
Functionality and Data for the Aalto University Course on Bayesian Data Analysis.
Maintained by Aki Vehtari. Last updated 4 months ago.
bayesbayesianbayesian-data-analysisbayesian-inferencebayesian-methodsbayesian-workflow
8.3 match 2.2k stars 8.93 score 159 scripts
cran
nlme:Linear and Nonlinear Mixed Effects Models
Fit and compare Gaussian linear and nonlinear mixed-effects models.
Maintained by R Core Team. Last updated 2 months ago.
3.8 match 6 stars 13.00 score 13k scripts 8.7k dependents
bgreenwell
investr:Inverse Estimation/Calibration Functions
Functions to facilitate inverse estimation (e.g., calibration) in linear, generalized linear, nonlinear, and (linear) mixed-effects models. A generic function is also provided for plotting fitted regression models with or without confidence/prediction bands that may be of use to the general user. For a general overview of these methods, see Greenwell and Schubert Kabban (2014) <doi:10.32614/RJ-2014-009>.
Maintained by Brandon M. Greenwell. Last updated 3 years ago.
calibrationinverse-estimationinverse-predictionregression
4.5 match 22 stars 8.22 score 162 scripts 2 dependentscran
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.
2.7 match 8 stars 8.39 score 1.4k scripts 28 dependentsbioc
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
3.6 match 4.78 score 4 scripts 2 dependents
stevencarlislewalker
MEMSS:Data Sets from Mixed-Effects Models in S
Data sets and sample analyses from Pinheiro and Bates, "Mixed-effects Models in S and S-PLUS" (Springer, 2000).
Maintained by Steve Walker. Last updated 6 years ago.
3.8 match 2.62 score 102 scriptscran
MCPAN:Multiple Comparisons Using Normal Approximation
Multiple contrast tests and simultaneous confidence intervals based on normal approximation. With implementations for binomial proportions in a 2xk setting (risk difference and odds ratio), poly-3-adjusted tumour rates, biodiversity indices (multinomial data) and expected values under lognormal assumption. Approximative power calculation for multiple contrast tests of binomial and Gaussian data.
Maintained by Frank Schaarschmidt. Last updated 7 years ago.
3.4 match 1 stars 2.53 score 38 scripts 3 dependents
selcukorkmaz
PubChemR:Interface to the 'PubChem' Database for Chemical Data Retrieval
Provides an interface to the 'PubChem' database via the PUG REST <https://pubchem.ncbi.nlm.nih.gov/docs/pug-rest> and PUG View <https://pubchem.ncbi.nlm.nih.gov/docs/pug-view> services. This package allows users to automatically access chemical and biological data from 'PubChem', including compounds, substances, assays, and various other data types. Functions are available to retrieve data in different formats, perform searches, and access detailed annotations.
Maintained by Selcuk Korkmaz. Last updated 6 months ago.
1.3 match 2 stars 5.62 score 23 scriptsbioc
drugTargetInteractions:Drug-Target Interactions
Provides utilities for identifying drug-target interactions for sets of small molecule or gene/protein identifiers. The required drug-target interaction information is obained from a local SQLite instance of the ChEMBL database. ChEMBL has been chosen for this purpose, because it provides one of the most comprehensive and best annotatated knowledge resources for drug-target information available in the public domain.
Maintained by Thomas Girke. Last updated 5 months ago.
cheminformaticsbiomedicalinformaticspharmacogeneticspharmacogenomicsproteomicsmetabolomics
1.3 match 1 stars 4.34 score 11 scriptscran
IntegratedJM:Joint Modeling of the Gene-Expression and Bioassay Data, Taking Care of the Effect Due to a Fingerprint Feature
Offers modeling the association between gene-expression and bioassay data, taking care of the effect due to a fingerprint feature and helps with several plots to better understand the analysis.
Maintained by Rudradev Sengupta. Last updated 8 years ago.
3.2 match 1.08 score 12 scripts