BNW using Octave instead of Matlab.

This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning.

I am calling this BNW_1.02. It can be accessed at:
compbio.uthsc.edu/BNW_1.02

1 files changed, 3 insertions, 3 deletions

diff --git a/sourcecodes/BNW_workflow_net1.htm b/sourcecodes/BNW_workflow_net1.htm
index 2b81a60e..ae00ad80 100644
--- a/sourcecodes/BNW_workflow_net1.htm
+++ b/sourcecodes/BNW_workflow_net1.htm
@@ -292,7 +292,7 @@ ul
 
 <p class=MsoNormal style='margin-right:107.5pt'><span style='font-size:12.0pt;
 line-height:115%;font-family:"Arial","sans-serif"'>This tutorial provides
-an overview of using BNW to build a Bayesian network model from a dataset and use the network to make predictions. The dataset used in this tutorial is a synthetic example of a genetic dataset that has a total of 8 variables. Two of the variables are genotypes labeled Geno1 and Geno2, and the remaining 6 variables are gene expression levels or other quantitative traits that are labeled Trait1 to Trait6. The dataset is available <a href="example_datasets/example_data_8nodes.txt">here</a>.<br><br>The data file is formatted according to the guidelines on the <a href=http://compbio.uthsc.edu/BNW_1.01/sourcecodes/help.php#file_format>BNW help page</a>. The first row of the file contains the names of the variables and the remaining rows contain the data for each sample of the dataset. The genotypes (Geno1 and Geno2), which are the only discrete variables in the network, are the leftmost variables in the input file and are integer values (1 and 2) for all of the samples. The quantitative traits are continuous variables, and, therefore, all contain a "." in at least one of the samples.<o:p></o:p></span></p>
+an overview of using BNW to build a Bayesian network model from a dataset and use the network to make predictions. The dataset used in this tutorial is a synthetic example of a genetic dataset that has a total of 8 variables. Two of the variables are genotypes labeled Geno1 and Geno2, and the remaining 6 variables are gene expression levels or other quantitative traits that are labeled Trait1 to Trait6. The dataset is available <a href="example_datasets/example_data_8nodes.txt">here</a>.<br><br>The data file is formatted according to the guidelines on the <a href=http://compbio.uthsc.edu/BNW_1.02/sourcecodes/help.php#file_format>BNW help page</a>. The first row of the file contains the names of the variables and the remaining rows contain the data for each sample of the dataset. The genotypes (Geno1 and Geno2), which are the only discrete variables in the network, are the leftmost variables in the input file and are integer values (1 and 2) for all of the samples. The quantitative traits are continuous variables, and, therefore, all contain a "." in at least one of the samples.<o:p></o:p></span></p>
 
 <p class=MsoNormal style='margin-right:107.5pt'><b style='mso-bidi-font-weight:
 normal'><span style='font-size:14.0pt;line-height:115%;font-family:"Arial","sans-serif"'>1. Structure learning using default options<o:p></o:p></span></b></p>
@@ -322,7 +322,7 @@ normal'><span style='font-size:14.0pt;line-height:115%;font-family:"Arial","sans
 
 <p class=MsoNormal style='margin-top:0in;margin-right:107.5pt;margin-bottom:
 0in;margin-left:0in;margin-bottom:.0001pt'><span style='font-size:12.0pt;
-line-height:115%;font-family:"Arial","sans-serif";mso-no-proof:yes'>In order to test if edges present the single best scoring network are conserved across high scoring networks. We can modify the structure learning settings to get identify the structures of many high scoring networks and perform <a href=http://compbio.uthsc.edu/BNW_1.01/sourcecodes/help.php#learn_details>model averaging</a> over these structures. To do this, return to the BNW home page, select <u>Learn a network model from data</u>, and upload the datafile. Instead of using the default settings, select <u>Go to structure learning settings and the BNW structural constraint interface</u>. A more detailed overview of use of the structural constraint interface is provided in <a href=http://compbio.uthsc.edu/BNW_1.01/sourcecodes/BNW_workflow_2.htm>another tutorial</a>, but, here, we will investigate the impact of modifying some of the structure learning settings shown below:<br><o:p></o:p></span></p><br>
+line-height:115%;font-family:"Arial","sans-serif";mso-no-proof:yes'>In order to test if edges present the single best scoring network are conserved across high scoring networks. We can modify the structure learning settings to get identify the structures of many high scoring networks and perform <a href=http://compbio.uthsc.edu/BNW_1.02/sourcecodes/help.php#learn_details>model averaging</a> over these structures. To do this, return to the BNW home page, select <u>Learn a network model from data</u>, and upload the datafile. Instead of using the default settings, select <u>Go to structure learning settings and the BNW structural constraint interface</u>. A more detailed overview of use of the structural constraint interface is provided in <a href=http://compbio.uthsc.edu/BNW_1.02/sourcecodes/BNW_workflow_2.htm>another tutorial</a>, but, here, we will investigate the impact of modifying some of the structure learning settings shown below:<br><o:p></o:p></span></p><br>
 
 <p class=MsoNormal style='margin-right:107.5pt'><span style='font-size:12.0pt;
 line-height:115%;font-family:"Arial","sans-serif";mso-no-proof:yes'>
@@ -356,7 +356,7 @@ normal'><span style='font-size:14.0pt;line-height:115%;font-family:"Arial","sans
 
 <p class=MsoNormal style='margin-top:0in;margin-right:107.5pt;margin-bottom:
 0in;margin-left:0in;margin-bottom:.0001pt'><span style='font-size:12.0pt;
-line-height:115%;font-family:"Arial","sans-serif";mso-no-proof:yes'><br>To make predictions with the network, we will use the structure learned after model averaging of the top 100 highest scoring networks. First, we will use the model to compare the expected values for nodes in the network based on observed genotypes. For these predictions, we will keep the prediction in evidence mode. The difference between evidence and intervention modes is discussed in the <a href=http://compbio.uthsc.edu/BNW_1.01/sourcecodes/faq.php#evid_inter>BNW FAQ page</a>. To use the model to make predictions based on Geno1, click on one of the blue bars in the Geno1 node and enter 1 or 2 to indicate which genotype value should be used to predict the values of the other network nodes. In the figure below, Geno1 is outlined in red and state 2 has a 100% probability, indicating that the value of this node has been entered as evidence. The red lines in the figure show the predicted distributions of the nodes after this evidence is known and can be compared with the blue lines which show the distributions for variables using the original data.<o:p></o:p></span></p><br>
+line-height:115%;font-family:"Arial","sans-serif";mso-no-proof:yes'><br>To make predictions with the network, we will use the structure learned after model averaging of the top 100 highest scoring networks. First, we will use the model to compare the expected values for nodes in the network based on observed genotypes. For these predictions, we will keep the prediction in evidence mode. The difference between evidence and intervention modes is discussed in the <a href=http://compbio.uthsc.edu/BNW_1.02/sourcecodes/faq.php#evid_inter>BNW FAQ page</a>. To use the model to make predictions based on Geno1, click on one of the blue bars in the Geno1 node and enter 1 or 2 to indicate which genotype value should be used to predict the values of the other network nodes. In the figure below, Geno1 is outlined in red and state 2 has a 100% probability, indicating that the value of this node has been entered as evidence. The red lines in the figure show the predicted distributions of the nodes after this evidence is known and can be compared with the blue lines which show the distributions for variables using the original data.<o:p></o:p></span></p><br>
 
 <p class=MsoNormal style='margin-right:107.5pt'><span style='font-size:12.0pt;
 line-height:115%;font-family:"Arial","sans-serif";mso-no-proof:yes'>