convert_seq(1) [debian man page]

CONVERT_SEQ(1)							   User Commands						    CONVERT_SEQ(1)

NAME

       convert_seq - conversion of sequence and alignment formats

SYNOPSIS

       convert_seq [OPTION]

DESCRIPTION

       convert_seq is used for sequence and alignment format conversion.  Instead of using command line arguments it starts a conversation with
       the user where the desired function can be invoked by answering questions.

       This program is used by the prof(1) secondary structure, accessibility and transmembrane helix predictor from Burkhard Rost.

OPTIONS

       None.  Answer questions printed on STDOUT by typing your choice into STDIN.

AUTHORS

       Reinhard Schneider	 Mar,	     1991      version 1.0 LION http://www.lion-ag/ Heidelberg
       Ulrike Goebel		 Mar,	     1997      version 1.1 LION http://www.lion-ag/ Heidelberg
       Reinhard Schneider	 Mar,	     1997      version 2.0 LION http://www.lion-ag/ Heidelberg
       Burkhard Rost		 May,	     1998      version 2.1 EMBL/LION Heidelberg
       Burkhard Rost	   Oct,        1998	 version 2.2
       Laszlo Kajan <lkajan@rostlab.org> TU Muenchen, Germany
       Guy Yachdav <gyachdav@rostlab.org> CUBIC (Columbia University, NY, USA), Technical University Munich (Munich, DE), BioSof LLC (USA)

SEE ALSO

       prof(1), filter_hssp(1)

1.0.9								    2011-09-27							    CONVERT_SEQ(1)

PROFNET(1)							   User Commands							PROFNET(1)

NAME

       profnet_* - neural network implementations in Fortran

SYNOPSIS

       profnet_* [OPTION|filePar]

DESCRIPTION

       profnet_* binaries are neural network implementations in Fortran.  Due to the original design of the code, a specific binary is compiled
       for each particular network architecture, changing certain constants in the source code.  Therefore, there is a binary for every network
       architecture used.  Note: certain array structures are intentionally indexed out of bounds in some of the binaries.

       Note:

       This binary should only be used to run with pre-made training data, do not try to use it to train your network as it will produce undesired
       results. It was made to be used only as part of wrapping (dependent) packages and not as a standalone neural network program.

OPTIONS

       This list is not exhaustive.

       filePar
	   file with input parameters (also gives fileIn, fileOut)

       1   "switch"

       2   number of input units

       3   number of hidden units

       4   number of output units

       5   number of samples

       6   bitacc (typically 100)

       7   file with input vectors

       8   file with junctions

       9   file with output of NN ("none" -> no file written)

       10  optional=dbg

       [inter]
	   will bring up dialog

NOTES

       1st MUST be "switch"!

       tested only with 2 layers!

AUTHOR

       Burkhard Rost <rost@rostlab.org>

       Bug fixes and enhancements by Laszlo Kajan <lkajan@rostlab.org> and Guy Yachdav <gyachdav@rostlab.org>

COPYRIGHT AND LICENSE

       Copyright 1998-2011 by Burkhard Rost <rost@rostlab.org> EMBL, CUBIC (Columbia University, NY, USA) and LION Biosciences (Heidelberg, DE)

       Copyright 2009-2011 by Laszlo Kajan <lkajan@rostlab.org> Technical University Munich (Munich, DE)

       Copyright 2009-2011 by Guy Yachdav <gyachdav@rostlab.org> CUBIC (Columbia University, NY, USA) and Technical University Munich (Munich, DE)

1.0.21- 							    2011-06-16								PROFNET(1)