Enables fitting dose-response curves with complex multiphasic features. Dr Fit is a software that reads the dose-response data and builds the model. The algorithm implemented in Dr-Fit first attempts to fit the data to a one process model. This is followed by an attempt to fit the data to a two inhibitory processes model. Then Dr-fit attempts to fit the data to a two processes model which includes a stimulatory process. Finally, the software attempts to fit the data to a three processes model, two of them being inhibitory (antagonist) and one stimulatory (agonist). Dr-Fit could be a useful tool for many experimentalists and analysts interested in the study of agent effects on biological systems.
Nonlinear dose-response models are primary tools for estimating the potency (e.g., half-maximum inhibitory concentration known as IC50) of anticancer drugs. drexplorer enables biologists to evaluate replicate reproducibility, detect outlier data points, fit different models, select the best model, estimate IC (inhibitory concentration) values at different percentiles, and assess drug-drug interactions. drexplorer serves as a computation engine within the R environment and a graphical interface for users who do not have programming backgrounds.
Provides functions for the design and analysis of dose-finding experiments (with focus on pharmaceutical Phase II clinical trials). It provides functions for: multiple contrast tests, fitting non-linear dose-response models (using Bayesian and non-Bayesian estimation), calculating optimal designs and an implementation of the MCPMod methodology.
Predicts the inhibitory activity of unknown molecules against 16 pancreatic cancer cell lines in terms of logIC50 value. DiPCell is a web application where user can design analogs and simultaneously predict their drug sensitivity on cancer cell lines. This webserver can be useful and can actively contribute in research on pancreatic cancer by helping in discovering the new candidate drug molecules.
A generalized platform for storing, normalizing, and dose-response modeling of large high-throughput and high-content chemical screening data. The tcpl package provides functionality for two screening paradigms: (i) single-concentration screening, intended to only identify potentially active compounds, and (ii) multiple-concentration screening intended to identify potentially active compounds and estimate the efficacy and potency through dose-response modeling. It also provides a categorization algorithm that segregates results into categories for rapid identification of potential false-positive and false-negative results.
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