Identification Analysis by Neural Networks - Revision history

Laschp at 16:04, 11 April 2025

2025-04-11T16:04:35Z

← Older revision		Revision as of 18:04, 11 April 2025
Line 39:		Line 39:
	6. Train and validate the ANN model by the NeuroDeveloper™ software by using the exported spectra files. Store the network model (*.snt data format). For details please refer to the NeuroDeveloper™ software manual		6. Train and validate the ANN model by the NeuroDeveloper™ software by using the exported spectra files. Store the network model (*.snt data format). For details please refer to the NeuroDeveloper™ software manual

	== ~~Classification~~ ==		== Identification ==

	[[File:Ann-main-figure.jpg\|335px\|right\|thumb\|Screenshot of the the MicrobeMS - Neurodeveloper interface]]		[[File:Ann-main-figure.jpg\|335px\|right\|thumb\|Screenshot of the the MicrobeMS - Neurodeveloper interface]]
Line 65:		Line 65:
	<br>		<br>

	== ~~Classification~~ reports ==		== Identification reports ==

	[[File:Ann-text.jpg\|600px\|right\|thumb\|Screenshot of the ANN text report produced by MicrobeMS v.082]]		[[File:Ann-text.jpg\|600px\|right\|thumb\|Screenshot of the ANN text report produced by MicrobeMS v.082]]

Laschp: /* Create Neurodeveloper™ ANN models */

2025-04-11T16:03:31Z

Create Neurodeveloper™ ANN models

← Older revision		Revision as of 18:03, 11 April 2025
Line 27:		Line 27:
	'''Compilation of mass spectra subsets for teaching and internal validation'''		'''Compilation of mass spectra subsets for teaching and internal validation'''

	1. Load the respective MALDI-~~TOF~~ mass spectra		1. Load the respective MALDI-ToF mass spectra

	2. Perform spectral pre-processing. All spectra used for teaching and internal validation are required to be processed in exactly the same way by utilizing the same sequence of pre-processing steps and identical pre-processing parameter		2. Perform spectral pre-processing. All spectra used for teaching and internal validation are required to be processed in exactly the same way by utilizing the same sequence of pre-processing steps and identical pre-processing parameter

Laschp: /* Classification reports */

2025-04-03T08:57:18Z

Classification reports

← Older revision		Revision as of 10:57, 3 April 2025
Line 71:		Line 71:
	[[File:Ann-html.jpg\|600px\|right\|thumb\|Screenshot of the ANN HTML report produced by MicrobeMS v.082]]		[[File:Ann-html.jpg\|600px\|right\|thumb\|Screenshot of the ANN HTML report produced by MicrobeMS v.082]]

	to be continued (Dec ~~2022~~)		to be continued (Dec 2024)

	<br>		<br>

Laschp: /* Classification */

2025-04-03T08:57:09Z

Classification

← Older revision		Revision as of 10:57, 3 April 2025
Line 45:		Line 45:
	[[File:Ann-settings.jpg\|399px\|right\|thumb\|Modify ANN identification parameters]]		[[File:Ann-settings.jpg\|399px\|right\|thumb\|Modify ANN identification parameters]]

	to be continued (Dec ~~2022~~)		to be continued (Dec 2024)

	<br>		<br>

Laschp: /* Create Neurodeveloper™ ANN models */

2025-04-03T08:56:43Z

Create Neurodeveloper™ ANN models

← Older revision		Revision as of 10:56, 3 April 2025
Line 27:		Line 27:
	'''Compilation of mass spectra subsets for teaching and internal validation'''		'''Compilation of mass spectra subsets for teaching and internal validation'''

	1. Load the respective MALDI-TOF mass spectra		1. Load the respective MALDI-TOF mass spectra

	2. Perform spectral pre-processing. All spectra used for teaching and internal validation are required		2. Perform spectral pre-processing. All spectra used for teaching and internal validation are required to be processed in exactly the same way by utilizing the same sequence of pre-processing steps and identical pre-processing parameter
	to be processed in exactly the same way by utilizing the same sequence of pre-processing steps and
	identical pre-processing ~~parameters~~

	3. Run the peak detection routine. Again the same parameters should be applied to all mass spectra,		3. Run the peak detection routine. Again the same parameters should be applied to all mass spectra, see function [[Peak Detection\|peak detection]] for details
	see function [[Peak Detection\|peak detection]] for details

	4. Assign mass spectra to individual classes by using the function [[Class Assignment\|class assignment]]		4. Assign mass spectra to individual classes by using the function [[Class Assignment\|class assignment]]

	5. Export the mass spectra by the function [[Store spectra (NeuroDeveloper format)\|Store peaklist data in a format specific to		5. Export the mass spectra by the function [[Store spectra (NeuroDeveloper format)\|Store peaklist data in a format specific to the NeuroDeveloper™ software]] (export to NeuroDeveloper™). When exporting spectra a log file will be automatically created that is required for ANN classification analysis (see below)
	the NeuroDeveloper™ software]] (export to NeuroDeveloper™). When exporting spectra a log file will
	be automatically created that is required for ANN classification analysis (see below)

	6. Train and validate the ANN model by the NeuroDeveloper™ software by using the exported spectra files.		6. Train and validate the ANN model by the NeuroDeveloper™ software by using the exported spectra files. Store the network model (*.snt data format). For details please refer to the NeuroDeveloper™ software manual
	Store the network model (*.snt data format). For details please refer to the NeuroDeveloper™ software
	manual

	== Classification ==		== Classification ==

Laschp at 11:47, 18 March 2023

2023-03-18T11:47:24Z

← Older revision		Revision as of 13:47, 18 March 2023
Line 78:		Line 78:
	[[File:Ann-html.jpg\|600px\|right\|thumb\|Screenshot of the ANN HTML report produced by MicrobeMS v.082]]		[[File:Ann-html.jpg\|600px\|right\|thumb\|Screenshot of the ANN HTML report produced by MicrobeMS v.082]]

	to be continued (Dec ~~2019~~)		to be continued (Dec 2022)

	<br>		<br>

Laschp: /* Classification */

2023-03-18T11:46:58Z

Classification

← Older revision		Revision as of 13:46, 18 March 2023
Line 52:		Line 52:
	[[File:Ann-settings.jpg\|399px\|right\|thumb\|Modify ANN identification parameters]]		[[File:Ann-settings.jpg\|399px\|right\|thumb\|Modify ANN identification parameters]]

	to be continued (Dec ~~2019~~)		to be continued (Dec 2022)

	<br>		<br>

Laschp at 11:46, 18 March 2023

2023-03-18T11:46:38Z

← Older revision		Revision as of 13:46, 18 March 2023
Line 14:		Line 14:
	''email'': info@synthon-analytics.com		''email'': info@synthon-analytics.com
	''internet'': http://www.synthon-analytics.de		''internet'': http://www.synthon-analytics.de
	[[File:~~synthon~~-logo.jpg\|\|right\|frameless\|link=http://www.synthon-analytics.de/\|Synthon company logo]]		[[File:Synthon-logo.jpg\|\|right\|frameless\|link=http://www.synthon-analytics.de/\|Synthon company logo]]

	<br>		<br>

Laschp: Created page with "FORCETOC == Introduction == The function ''Identification by Neural Networks'' is basically an interface between MicrobeMS and Synthon's NeuroDeveloper™, a software for teaching and validating ANN models with microbial mass spectra. Based on pre-defined neural network models, the interface can be used to classify mass spectra into pre-defined categories (i.e. microbial genera, species, subspecies, etc.). Spectral pre-processing, features selection and ANN cla..."

2023-03-18T09:30:40Z

Created page with "__FORCETOC__ == Introduction == The function ''Identification by Neural Networks'' is basically an interface between MicrobeMS and Synthon's NeuroDeveloper™, a software for teaching and validating ANN models with microbial mass spectra. Based on pre-defined neural network models, the interface can be used to classify mass spectra into pre-defined categories (i.e. microbial genera, species, subspecies, etc.). Spectral pre-processing, features selection and ANN cla..."

New page

__FORCETOC__

== Introduction ==

The function ''Identification by Neural Networks'' is basically an interface between MicrobeMS and Synthon's NeuroDeveloper™, a software for teaching and validating ANN models with microbial mass spectra. Based on pre-defined neural network models, the interface can be used to classify mass spectra into pre-defined categories (i.e. microbial genera, species, subspecies, etc.). Spectral pre-processing, features selection and ANN classification of a priori unknown spectra is automatically performed. This is achieved by utilizing the runtime environment of the NeuroDeveloper™ software which does not require a software license from Synthon. Spectral data can be therefore classified without the NeuroDeveloper™ software on the basis of predefined network libraries. The NeuroDeveloper™ is, however, required if you wish to create and validate own ANN models.

Synthon GmbH contact address:
Analytics and Pattern Recognition
Im Neuenheimer Feld
69120 Heidelberg
'''GERMANY'''
''phone'': +49 6221 50 257 900
''fax'': +49 6221 50 257 909
''email'': info@synthon-analytics.com
''internet'': http://www.synthon-analytics.de
[[File:synthon-logo.jpg||right|frameless|link=http://www.synthon-analytics.de/|Synthon company logo]]

 
Some classes of artificial neural networks (ANN) are computational models which are popular means to model complex relationships between inputs and outputs and to find patterns. In supervised learning so called multilayer-perceptron (MLP) ANNs require a set of training samples which can be used to infer a classifier for predicting class assignments. To avoid overfitting and to assess the robustness of ANN class assignment, the general strategy of analysis by MLP-ANNs routinely includes procedures of training, internal validation and (external) testing, ideally under blinded conditions. Training and internal validation requires samples with of known class assignments (i.e. labeled spectra); the model performance can be then determined on the basis of errors between the obtained outputs and desired target values of both, the training and the internal validation subsets. At the training stage ANN performance can be optimized by modifying the way of pre-processing, adding or eliminating spectral features, or changing the network's architecture. When training is finished the classifier can be challenged by an external validation (test) subset.

== Create Neurodeveloper™ ANN models ==

 
Of note, a license of the NeuroDeveloper™ software will be required.

 
'''Compilation of mass spectra subsets for teaching and internal validation'''

1. Load the respective MALDI-TOF mass spectra

2. Perform spectral pre-processing. All spectra used for teaching and internal validation are required
to be processed in exactly the same way by utilizing the same sequence of pre-processing steps and
identical pre-processing parameters

3. Run the peak detection routine. Again the same parameters should be applied to all mass spectra,
see function [[Peak Detection|peak detection]] for details

4. Assign mass spectra to individual classes by using the function [[Class Assignment|class assignment]]

5. Export the mass spectra by the function [[Store spectra (NeuroDeveloper format)|Store peaklist data in a format specific to
the NeuroDeveloper™ software]] (export to NeuroDeveloper™). When exporting spectra a log file will
be automatically created that is required for ANN classification analysis (see below)

6. Train and validate the ANN model by the NeuroDeveloper™ software by using the exported spectra files.
Store the network model (*.snt data format). For details please refer to the NeuroDeveloper™ software
manual

== Classification ==

[[File:Ann-main-figure.jpg|335px|right|thumb|Screenshot of the the MicrobeMS - Neurodeveloper interface]]

[[File:Ann-settings.jpg|399px|right|thumb|Modify ANN identification parameters]]

to be continued (Dec 2019)

  

  

  

  

  

  

  

  

  

== Classification reports ==

[[File:Ann-text.jpg|600px|right|thumb|Screenshot of the ANN text report produced by MicrobeMS v.082]]

[[File:Ann-html.jpg|600px|right|thumb|Screenshot of the ANN HTML report produced by MicrobeMS v.082]]

to be continued (Dec 2019)