updated to latest info

index.html

@@ -6,20 +6,17 @@ <h1>netDx</h1>
 
 netDx is a <b>patient classifier</b> algorithm that can integrate several types of patient data into a single model. It does this by converting each type of data into a view of patient similarity; i.e. by converting the data into a graph in which more similar patients are tightly linked, while less similar patients are not so tightly linked.
 
-<h2>What do you want to do?</h2>
-<ul>
-<li><a href="https://netdx-website.readthedocs.io/en/latest/Introduction.html">Learn how netDx works</a></li>
-<li><a href="https://github.com/BaderLab/netDx">Get the software (link to github repo)</a></li>
-<li><a href="https://netdx-website.readthedocs.io/en/latest/index.html">Learn to design effective predictors and use netDx with the user manual (in active development ; feedback welcome!)</a></li>
-<li>See worked examples (R vignettes): 
-	<ol>
-	<li><a href="examples/BuildPredictor.html">Build a classifier</a></li>
-	<li><a href="examples/plotPredictorResults.html">Plot classifier results</a></li>
-	</ol>
-</ul>
-
-<b>Reference:</b> <a href="https://www.embopress.org/doi/full/10.15252/msb.20188497">Pai S et al. netDx: interpretable patient classification using integrated patient similarity networks. Molecular Systems Biology (2019) 15, e8497</a>
+<a href="http://bioconductor.org/packages/release/bioc/html/netDx.html">netDx is now in BioConductor!</a>
+Go here to see vignettes (worked examples) and install software.
 
+<b>Reference:</b> 
+<b>Methods paper:</b>Pai S, Hui S, Isserlin R, Shah MA, Kaka H, GD Bader (2019) 
+ <a href="https://www.embopress.org/doi/full/10.15252/msb.20188497">
+	netDx: interpretable patient classification using integrated patient similarity networks.</a>
+	<i>Molecular Systems Biology</i> <b>15</b>, e8497</a><br>
+<b>Software update:</b>Pai S, Weber P, Isserlin R, Kaka H, Hui S, Shah MA, Giudice L, Giugno R, Nøhr AK, Baumbach J, GD Bader
+<a href="https://f1000research.com/articles/9-1239">netDx: Software for building interpretable patient classifiers by multi-'omic data integration using patient similarity networks
+</a> <i>F1000Res.</i><b>9</b>:1239.
 <p></p>
 <b>Questions?</b>
 Contact Shraddha Pai, <a href="mailto:shraddha.pai@utoronto.ca">shraddha.pai@utoronto.ca</a>