Search Results for "optimize"
Sort By:
Showing 7 open source projects for "optimize"
View related business solutions-
Outgrown Windows Task Scheduler?Windows Task Scheduler wasn't built for complex, cross-platform automation. Get a free diagnostic that shows exactly where things are failing and provides remediation recommendations. Interactive HTML report delivered in minutes.
-
AI-generated apps that pass security reviewRetool lets you generate dashboards, admin panels, and workflows directly on your data. Type something like “Build me a revenue dashboard on my Stripe data” and get a working app with security, permissions, and compliance built in from day one. Whether on our cloud or self-hosted, create the internal software your team needs without compromising enterprise standards or control.
-
1
BioNeMo
BioNeMo Framework: For building and adapting AI models
BioNeMo is an AI-powered framework developed by NVIDIA for protein and molecular generation using deep learning models. It provides researchers and developers with tools to design, analyze, and optimize biological molecules, aiding in drug discovery and synthetic biology applications. -
2
Microarray assosiated motif analyzer
Cis-element prediction tool from microarray data
...Simulation of gene expression was performed using a support vector machine and based on the presence of predicted motifs and motif pairs. The accuracy of simulated gene expression was used to evaluate the quality of prediction and to optimize the parameters used in this method. After optimization, MAMA accurately simulated more than 87% of gene expression. See Kakei Y, Ogo Y, Itai RN, et al. (2013) Development of a novel prediction method of cis-elements to hypothesize collaborative functions of cis-element pairs in iron-deficient. Rice 6(1): 22. -
3
PBTK Optimizer
Application for optimization of parameters in PBTK models
...Other parameters can be determined through in-vitro experiments or through extrapolation using published equations. When it is impractical to use these methods to estimate a parameter, techniques can be used to optimize parameters so that model results best fit validation data. This tool was designed to optimize a user-specified list of parameters to a user-specified PBTK model. The user also controls validation data and optimization algorithms. In addition to optimized parameters, the tool outputs statistical information about the fit of the optimized model. -
4
Application to optimize DNA sequences coding protein to put in in the different organizm (f.e. human protein in E.Coli). It proposes the optimal cutting places to connect many shorter fragments into bigger one using ligaze.
-
Atera all-in-one platform IT management software with AI agentsAtera’s AI agents don’t just assist, they act. From detection to resolution, they handle incidents and requests instantly, taking your IT management from automated to autonomous.
-
5
InMethyl
Design of target-specific primers for bis. sequencing of CpG-islands
...This balance is based on the calculation of scoring factor including primer pair specificity, nucleotide composition (sequence complexity), thermodynamic features (melting temperature, dimers dG etc.), presence of CpG-sites and other parameters. Users are intended to customize the desired or limit ranges of these values, as well as penalties for out-of-bounds values. InMethyl software allows to optimize combination of PCR primer pairs to perform the amplification of large genomic regions, e.g. CpG-islands. -
6
PolyCTLDesigner
Python scripts for designing polyepitope T-cell immunogens
Given the peptides (T-cell epitopes) PolyCTLDesigner selects flanking sequences to optimize TAP-binding and joins resulting oligopeptides into a polyepitope in a way providing efficient liberation of potential epitopes by proteasomal and/or immunoproteasomal processing and minimizing the number of junctional epitopes. For constructing polyepitopes PolyCTLDesigner utilizes known amino acid patterns of proteasome cleavage and TAP-binding specificity. -
7
ARDEN
Specificity Control for Read Alignments Using an Artificial Reference
We introduce ARDEN (Artificial Reference Driven Estimation of false positives in NGS data), a novel benchmark that estimates error rates based on real experimental reads and an additionally generated artificial reference genome. It allows the computation of error rates specifically for a dataset and the construction of a ROC-curve. Thereby, it can be used to optimize parameters for read mappers, to select read mappers for a specific problem or also to filter alignments based on quality estimation.
- Previous
- You're on page 1
- Next
