From c4f926438dcb8abe805e910399940f79ff643c4b Mon Sep 17 00:00:00 2001 From: ziejd2 Date: Thu, 14 Sep 2017 15:39:41 -0500 Subject: Add files via upload --- sourcecodes/examples/cardiagnosis.php | 83 +++++++++++++ sourcecodes/examples/chl_example.php | 32 +++++ sourcecodes/examples/my_new_style.css | 155 +++++++++++++++++++++++++ sourcecodes/examples/spleen_immune_example.php | 33 ++++++ sourcecodes/examples/synthetic_example2.php | 32 +++++ sourcecodes/examples/synthetic_example3.php | 34 ++++++ 6 files changed, 369 insertions(+) create mode 100644 sourcecodes/examples/cardiagnosis.php create mode 100644 sourcecodes/examples/chl_example.php create mode 100644 sourcecodes/examples/my_new_style.css create mode 100644 sourcecodes/examples/spleen_immune_example.php create mode 100644 sourcecodes/examples/synthetic_example2.php create mode 100644 sourcecodes/examples/synthetic_example3.php (limited to 'sourcecodes/examples') diff --git a/sourcecodes/examples/cardiagnosis.php b/sourcecodes/examples/cardiagnosis.php new file mode 100644 index 00000000..1c2b14e4 --- /dev/null +++ b/sourcecodes/examples/cardiagnosis.php @@ -0,0 +1,83 @@ + + + + +
+ +
+ +

Car Diagnosis [View network]

+
+

+Car Diagnosis is an example of Bayesian Network structure originally designed by Norsys Software Corp. The example structure shows the relationships between different parts of a car. This network includes 15 discrete variables. Label values of the variables are annotated in the following table.
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
VariablesLabel 1Label 2Label 3
Voltage_at_plug1=none2=weak3=strong
Car_cranks1=False2=True
Spark_plugs1=fouled2=too wide3=okay
Spark_quality1=very bad2=bad3=good
Distributer1=faulty2=okay
Spark_timing1=very bad2=bad3=good
Car_starts1=False2=True
Headlights1=off2=dim3=bright
Alternator1=faulty2=okay
Charging_system1=faulty2=okay
Battery_voltage1=dead2=weak3=strong
Battery_age1=very old2=old3=new
Main_fuse1=blown2=okay
Starter_system1=faulty2=okay
Starter_motor1=faulty2=okay
+
+

+ +
+ + diff --git a/sourcecodes/examples/chl_example.php b/sourcecodes/examples/chl_example.php new file mode 100644 index 00000000..f695ad5c --- /dev/null +++ b/sourcecodes/examples/chl_example.php @@ -0,0 +1,32 @@ + + + + +
+ +
+ +

Genetic network of immure response to infection with Chlamydia psittaci [View network]

+
+

+ This network models the differential susceptibility to infection with Chlamydia psittaci observed in B6 and D2 mice, as well as in the BXD recombinant inbred strains that have been developed from these parental strains. The model predicts that disease status, as quantified by the weight loss after infection (Weight), is directly influenced by the level of neutrophils and the genotype at the Ctrq3 locus on Chr 11. Macrophage activation status (MAS) is also predicted to influence the level of neutrophils and the pathogen load. This network has been fully described in a previous paper [PubMed link], in which several predictions made by the model were experimentally validated.
+
+

+
+ + diff --git a/sourcecodes/examples/my_new_style.css b/sourcecodes/examples/my_new_style.css new file mode 100644 index 00000000..20db184f --- /dev/null +++ b/sourcecodes/examples/my_new_style.css @@ -0,0 +1,155 @@ +* { +padding: 0; +margin: 0; +} +html, body { +height: 100%; +} + +body { + margin:0px; + font-family: Georgia, "Times New Roman", + Times, serif; + color: #33339F; + background-color: white; + padding-bottom:0px; + +} + + + +.td_title +{ + padding: 0; + margin: 0; + font-family:Arial; + font-size:24pt; + HRIZONTAL-ALIGN: center; + color:#FFFFFF; + font-weight:bold; + //FONT-STYLE: italic; + background-color: #33339F; + text-align:center + +} + + + +#outer { + padding: 0; + margin: 0; + position: absolute; + top: 9em; + left: 12em; + text-align:left; + background:white; + min-width:50%; + min-hieght:50%; + max-width:70%; + +} + +#outernew { + padding: 0; + margin: 0; + position: absolute; + top: 9em; + left: 12em; + text-align:left; + background:white; + min-width:50%; + min-hieght:50%; + max-width:60%; + +} + +#outernewnew { + padding: 0; + margin: 0; + position: absolute; + top: 12em; + left: 25em; + text-align:left; + background:white; + min-width:50%; + min-hieght:50%; + max-width:60%; + +} + + + +ul.navbar { + list-style-type: none; + padding: 0; + margin: 0; + /*position: relative;*/ + float: top; + /*margin-top: 20px;*/ + top: 9em; + left: 1em; + width: 9em } + +ul.navbar2 { + list-style-type: none; + padding: 0; + margin: 0; + /*position: absolute;*/ + float:top; + top: 9em; + left: 1em; + width: 9em } + +ul.listbar { + list-style-type: none; + padding: 0; + margin: 0; + position: relative; + } + + + +h1 { + font-family: Helvetica, Geneva, Arial, + SunSans-Regular, sans-serif } +ul.navbar li { +/* background: #D0A9F5; */ + background: #33339F; + margin: 0.5em 0; + padding: 0.3em; + border-right: 1em solid black } +ul.navbar a { + text-decoration: none +} +ul.navbar a:link { + color: white; } +ul.navbar a:visited { + color: white; } + +ul.navbar2 li { +/* background: #D0A9F5; */ + background: black; + margin: 0.5em 0; + padding: 0.3em; + border-right: 1em solid #33339F; + color: white } +ul.navbar2 a { + text-decoration: none +} +ul.navbar2 a:link { + color: white; } +ul.navbar2 a:visited { + color: white; } + +a:link { + color: black; } +a:hover { + color: black; + } +a:visited { + color: black; } +address { + margin-top: 1em; + padding-top: 1em; + border-top: thin dotted } + diff --git a/sourcecodes/examples/spleen_immune_example.php b/sourcecodes/examples/spleen_immune_example.php new file mode 100644 index 00000000..89df2c7b --- /dev/null +++ b/sourcecodes/examples/spleen_immune_example.php @@ -0,0 +1,33 @@ + + + + +
+ +
+ +

Genetic network of immune-related genes in the spleens of BXD mice [View network]

+
+

+ This network models how a SNP on Chr 2 impacts the expression of several immune-related genes in BXD mice. To create the network, we selected expression values for genes with an immune-related GO annotation from a microarray dataset that is available in the GeneNetwork. The dataset contains gene expression in the spleens of 81 BXD strains and is further described here. Next, we performed QTL mapping of these immune-related gene expression traits and found that many genes mapped near rs3664317, a SNP at 63.69 Mb on Chr 2.

+ To make a Bayesian network model of the interactions among the locus and the genes, we created an input file, following the BNW file format, containing the genotype at rs3664317 and the expression of the genes with QTLs at this locus. In the file, which can be downloaded here, each row is a BXD strain and each column is a variable (a gene expression trait or the genotype). We coded the genotype to fit the BNW input file format, giving strains with a D genotype a value of 1 for the rs3664317 variable and strains with a B genotype a value of a 2.

When performing structure learning for this example, we adjusted settings and added constraints using the BNW structural constraint interface. First, we performed model averaging over the 100 highest scoring network structures and set the model averaging selection threshold to 0.9, so that only edges that were in a large majority of high scoring network structures were included. Next, we separated the network variables into 3 tiers: Tier1 contained the genotype at rs3664317, Tier2 contained cis-regulated genes, and Tier3 contained trans-regulated genes. Using the default settings for within and between tier interactions, this method of tier construction prevents trans-regulated genes from being the parents of cis-regulated genes. In this case, only one gene, Ifih1, was cis-regulated by this locus.
+
+

+
+ + diff --git a/sourcecodes/examples/synthetic_example2.php b/sourcecodes/examples/synthetic_example2.php new file mode 100644 index 00000000..aed9d357 --- /dev/null +++ b/sourcecodes/examples/synthetic_example2.php @@ -0,0 +1,32 @@ + + + + +
+ +
+ +

Synthetic genetic network [View network]

+
+

+ This network was created using simulated genetic data. The input data file, which is available here, contains 2 genotypes and 6 traits. However, one of the traits (Trait5), is not connected in the network, indicating that it is not influenced by other variables in the dataset and that it does not influence other variables in the network. A complete workflow describing the use of BNW to learn the network structure and make predictions of the resulting model with this dataset is provided in this tutorial.
+
+

+
+ + diff --git a/sourcecodes/examples/synthetic_example3.php b/sourcecodes/examples/synthetic_example3.php new file mode 100644 index 00000000..167132e2 --- /dev/null +++ b/sourcecodes/examples/synthetic_example3.php @@ -0,0 +1,34 @@ + + + + +
+ +
+ +

Synthetic genetic network with time-series data [View network]

+
+

+This network was created using a simulated time-series genetic dataset that is available here. The dataset contains a genotype and the three quantitative traits (TraitA, TraitB, and TraitC) that were each measured at 3 time points (T1, T2, and T3). In this example, T1 might represent the baseline measurement the traits at the time of or shortly before an experimental intervention, while T2 and T3 could represent measurements of the traits in response to the experiment.

+When performing structure learning of this dataset, we used the BNW structural constraint interface to provide constraints on the structure to incorporate the prior knowledge provided by the time the data was collected. Specifically, we separated the data into 4 tiers: Tier 1 contained the genotype, Tier2 containted the three traits at time T1, Tier3 containted the traits at time T2, and Tier4 contained the traits at time T3. We maintined the default options for allowing interactions within or between tiers, preventing any traits from being the parent of the genotype and any trait at a later time point from being the parent of an earlier time point. The network shown was learned using a model average of the 100 highest scoring structures.

+The network model shows that the genotype consistently influenced TraitA at each of the time points, but that other interactions in the network were dependent on time. The variation in TraitB caused by the genotype could only be observed at time T2, after the experimental intervention, and this influence on TraitB decreased by time T3. There were also time-dependent interactions between the quantitative traits, as TraitB at T2 could be predicted by TraitA at T1 and TraitC at T3 could be predicted by TraitA at T1 and TraitB at T2.
+
+

+
+ + -- cgit 1.4.1