Mercurial > repos > recetox > retip_train

comparison macros.xml @ 8:e7771b3d6a40 draft default tip

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
"planemo upload for repository https://github.com/RECETOX/galaxytools/tree/master/tools/retip commit 9bc547872c98a9c13c561d15e8990fe82bdc0e72"
author recetox
date Fri, 28 Jan 2022 16:29:45 +0000
parents 9012a9dba1db
children
comparison
equal deleted inserted replaced
7:fc3e026406c5 8:e7771b3d6a40
1 <macros> 1 <macros>
2 <token name="@TOOL_VERSION@">0.5.4</token> 2 <token name="@TOOL_VERSION@">0.5.4</token>
3
4 <xml name="creator">
5 <creator>
6 <person
7 givenName="Muhammad"
8 familyName="Usman"
9 url="https://github.com/smartx-usman"
10 identifier="0000-0002-9598-0704" />
11 <person
12 givenName="Aleš"
13 familyName="Křenek"
14 url="https://github.com/ljocha"
15 identifier="0000-0002-3395-3196" />
16 <organization
17 url="https://www.recetox.muni.cz/"
18 email="GalaxyToolsDevelopmentandDeployment@space.muni.cz"
19 name="RECETOX MUNI" />
20 </creator>
21 </xml>
22
3 <xml name="requirements"> 23 <xml name="requirements">
4 <requirements> 24 <requirements>
5 <container type="docker">recetox/retip:@TOOL_VERSION@-recetox4</container> 25 <container type="docker">recetox/retip:@TOOL_VERSION@-recetox4</container>
6 </requirements> 26 </requirements>
7 </xml> 27 </xml>
9 <citations> 29 <citations>
10 <citation type="doi">https://doi.org/10.1021/acs.analchem.9b05765</citation> 30 <citation type="doi">https://doi.org/10.1021/acs.analchem.9b05765</citation>
11 </citations> 31 </citations>
12 </xml> 32 </xml>
13 <token name="@HELP@"><![CDATA[ 33 <token name="@HELP@"><![CDATA[
14 **Retip** is an R package for predicting Retention Time (RT) for small molecules in a high pressure liquid 34 **Retip** is an R package for predicting Retention Time (RT) for small molecules in a high pressure liquid
15 chromatography (HPLC) Mass Spectrometry analysis. Retention time calculation can be useful in identifying 35 chromatography (HPLC) Mass Spectrometry analysis. Retention time calculation can be useful in identifying
16 unknowns and removing false positive annotations. It uses five different machine learning algorithms to built a 36 unknowns and removing false positive annotations. It uses five different machine learning algorithms to built a
17 stable, accurate and fast RT prediction model: 37 stable, accurate and fast RT prediction model:
18 38
19 - Random Forest: a decision tree algorithms 39 - Random Forest: a decision tree algorithms
20 - BRNN: Bayesian Regularized Neural Network 40 - BRNN: Bayesian Regularized Neural Network
21 - XGBoost: an extreme Gradient Boosting for tree algorithms 41 - XGBoost: an extreme Gradient Boosting for tree algorithms
22 - lightGBM: a gradient boosting framework that uses tree based learning algorithms. 42 - lightGBM: a gradient boosting framework that uses tree based learning algorithms.
23 - Keras: a high-level neural networks API for Tensorflow 43 - Keras: a high-level neural networks API for Tensorflow
24 44
25 Retip also includes useful biochemical databases like: BMDB, ChEBI, DrugBank, ECMDB, FooDB, HMDB, KNApSAcK, 45 Retip also includes useful biochemical databases like: BMDB, ChEBI, DrugBank, ECMDB, FooDB, HMDB, KNApSAcK,
26 PlantCyc, SMPDB, T3DB, UNPD, YMDB and STOFF. 46 PlantCyc, SMPDB, T3DB, UNPD, YMDB and STOFF.
27 47
28 **Get started** 48 **Get started**
29 49
30 To use Retip, a user needs to prepare a compound retention time library. The input file 50 To use Retip, a user needs to prepare a compound retention time library. The input file
31 needs compound Name, InChiKey, SMILES code and experimental retention time information for each compound. 51 needs compound Name, InChiKey, SMILES code and experimental retention time information for each compound.
32 The input must be a CSV file. Retip will use this input file to build a the model and will predict 52 The input must be a CSV file. Retip will use this input file to build a the model and will predict
33 retention times for other biochemical databases or an input query list of compounds. It is suggested that 53 retention times for other biochemical databases or an input query list of compounds. It is suggested that
34 the file has at least 300 compounds to build a good retention time prediction model. 54 the file has at least 300 compounds to build a good retention time prediction model.
35 ]]> 55 ]]>
36 </token> 56 </token>
37 </macros> 57 </macros>