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comparison immuneml_train_recept.xml @ 7:45ca02982e1f draft

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"planemo upload commit 8aef44a2b3bc8fc00a1efe0ce7ecab83eded053f-dirty"
author immuneml
date Tue, 27 Jul 2021 10:27:11 +0000
parents 2d3dd9ff7e84
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94 <help><![CDATA[ 94 <help><![CDATA[
95 The purpose of this tool is to train machine learning (ML) models to predict a characteristic per immune receptor, such as 95 The purpose of this tool is to train machine learning (ML) models to predict a characteristic per immune receptor, such as
96 antigen specificity. One or more ML models are trained to classify receptors based on the information within the CDR3 sequence(s). Finally, the performance 96 antigen specificity. One or more ML models are trained to classify receptors based on the information within the CDR3 sequence(s). Finally, the performance
97 of the different methods is compared. 97 of the different methods is compared.
98 Alternatively, if you want to predict a property per immune repertoire, such as disease status, check out the 98 Alternatively, if you want to predict a property per immune repertoire, such as disease status, check out the
99 `Train immune repertoire classifiers (simplified interface) <https://galaxy.immuneml.uio.no/root?tool_id=novice_immuneml_interface>`_ tool instead. 99 `Train immune repertoire classifiers (simplified interface) <root?tool_id=novice_immuneml_interface>`_ tool instead.
100 100
101 The full documentation can be found `here <https://docs.immuneml.uio.no/latest/galaxy/galaxy_simple_receptors.html>`_. 101 The full documentation can be found `here <https://docs.immuneml.uio.no/latest/galaxy/galaxy_simple_receptors.html>`_.
102 102
103 **Basic terminology** 103 **Basic terminology**
104 104
188 188
189 - optimal_ml_settings.zip: a .zip file containing the raw files for the optimal trained ML settings (ML model, encoding). 189 - optimal_ml_settings.zip: a .zip file containing the raw files for the optimal trained ML settings (ML model, encoding).
190 This .zip file can subsequently be used as an input when `applying previously trained ML models to a new AIRR dataset in Galaxy <https://docs.immuneml.uio.no/latest/galaxy/galaxy_apply_ml_models.html>`_. 190 This .zip file can subsequently be used as an input when `applying previously trained ML models to a new AIRR dataset in Galaxy <https://docs.immuneml.uio.no/latest/galaxy/galaxy_apply_ml_models.html>`_.
191 191
192 - receptor_classification.yaml: the YAML specification file that was used by immuneML internally to run the analysis. This file can be 192 - receptor_classification.yaml: the YAML specification file that was used by immuneML internally to run the analysis. This file can be
193 downloaded, altered, and run again by immuneML using the `Train machine learning models <https://galaxy.immuneml.uio.no/root?tool_id=immuneml_train_ml_model>`_ Galaxy tool. 193 downloaded, altered, and run again by immuneML using the `Train machine learning models <root?tool_id=immuneml_train_ml_model>`_ Galaxy tool.
194 194
195 **More analysis options** 195 **More analysis options**
196 196
197 A limited selection of immuneML options is available through this tool. If you wish to have full control of the analysis, consider using 197 A limited selection of immuneML options is available through this tool. If you wish to have full control of the analysis, consider using
198 the `Train machine learning models <https://galaxy.immuneml.uio.no/root?tool_id=immuneml_train_ml_model>`_ Galaxy tool. 198 the `Train machine learning models <root?tool_id=immuneml_train_ml_model>`_ Galaxy tool.
199 This tool provides other encodings and machine learning methods to choose from, as well as 199 This tool provides other encodings and machine learning methods to choose from, as well as
200 data preprocessing and settings for hyperparameter optimization. The interface of the YAML-based tool expects more independence and knowledge about 200 data preprocessing and settings for hyperparameter optimization. The interface of the YAML-based tool expects more independence and knowledge about
201 machine learning from the user. 201 machine learning from the user.
202 202
203 ]]> 203 ]]>