Description of patient classes for AD iCAP.

<p>Description of patient classes for AD iCAP.</p

ALS classifier accuracies with 6 Huntington's disease samples included in test set.

<p>ALS classifier accuracies with 6 Huntington's disease samples included in test set.</p

The 134 genes from the ALS gene expression classifier.

<p>Genes that are either transcriptionally responsive to ER stress [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0178608#pone.0178608.ref032" target="_blank">32</a>] or directly targeted by TFs ATF4 (Atf4) and CHOP (Ddit3) during ER stress [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0178608#pone.0178608.ref032" target="_blank">32</a>] are shown as nodes (depicted using Cytoscape [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0178608#pone.0178608.ref033" target="_blank">33</a>]). Node color indicates mean log₂ differential expression level in the indicator cell assay (disease versus normal) ranging from –0.44 (green) to 0.1 (pink). Node shape indicates genes that are transcriptionally responsive to ER stress (oval) or are not (hexagon) [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0178608#pone.0178608.ref032" target="_blank">32</a>]. 133 of the 134 genes are known to have human orthologs and underlined genes co-occur with “amyotrophic lateral sclerosis” in titles or abstracts of articles in PubMed (on 01/15/15).</p

Classifier pipelines.

<p>I. 47 training samples or 12 test samples passed through the pre-processing pipeline, which resulted in normalized gene intensity scores. II. The classifiers were trained. 12 non-carrier (non-car.) samples in the 47 training examples were used to construct a linear model of gene expression based on control genes, which was used to convert all gene intensities into log2 gene expression ratios. These ratios were used to identify differentially expressed gene sets and ultimately select 64 gene sets or 134 genes that could differentiate between non-carrier and pre-symptomatic carrier mice using SVMs. III. The classifiers were tested. Gene intensities from 12 pre-processed but de-identified examples were converted to expression ratios using the linear model trained in II, and data sets composed of the 64 gene sets or 134 genes identified in II were extracted. The de-identified examples described by these data sets were classified as carrier or non-carrier using the SVMs trained in II. The pipeline for Classifier 3 was identical to that of Classifier 1 except that a different GSA score threshold was used (|GSA| ≥ 1 instead of GSA ≤ -1), resulting in selection of 106 gene sets instead of 64. For classifier testing with Huntington’s samples, classifiers were trained with all 59 normal and disease samples (not shown) and tested against 6 Huntington’s samples. For each classifier configuration, the fraction of Huntington’s samples that were correctly predicted as non-carriers of the ALS mutation is shown as ‘Hunt’ tally.</p

Development of an indicator cell assay.

<p>For each disease indication, the global differential gene expression pattern of the indicator cells is measured in response to serum from normal and diseased subjects, and is used to identify a reliable disease classifier using a small number of features. To deploy the assay, the cell-based components can be automated and miniaturized, and the expression of classifier genes can be measured using a targeted, cost-effective and high-throughput readout to classify new subjects.</p

Blind accuracies of ALS classifiers.

<p>Blind accuracies of ALS classifiers.</p

Combining inferred regulatory and reconstructed metabolic networks enhances phenotype prediction in yeast

<div><p>Gene regulatory and metabolic network models have been used successfully in many organisms, but inherent differences between them make networks difficult to integrate. Probabilistic Regulation Of Metabolism (PROM) provides a partial solution, but it does not incorporate network inference and underperforms in eukaryotes. We present an Integrated Deduced And Metabolism (IDREAM) method that combines statistically inferred Environment and Gene Regulatory Influence Network (EGRIN) models with the PROM framework to create enhanced metabolic-regulatory network models. We used IDREAM to predict phenotypes and genetic interactions between transcription factors and genes encoding metabolic activities in the eukaryote, <i>Saccharomyces cerevisiae</i>. IDREAM models contain many fewer interactions than PROM and yet produce significantly more accurate growth predictions. IDREAM consistently outperformed PROM using any of three popular yeast metabolic models and across three experimental growth conditions. Importantly, IDREAM’s enhanced accuracy makes it possible to identify subtle synthetic growth defects. With experimental validation, these novel genetic interactions involving the pyruvate dehydrogenase complex suggested a new role for fatty acid-responsive factor Oaf1 in regulating acetyl-CoA production in glucose grown cells.</p></div

Comparison of PROM and IDREAM predicted growth ratio with experiments under glucose minimal medium.

<p>The ratio of mutant vs. wild-type growth rate was compared with the growth ratio for 119 TF knockouts previously measured by Sauer Lab. There were 51 TFs in common between the two integrative models, so we distinguish PROM by TF90 (the whole YEASTRACT-based model) and TF51 (the portion of the YEASTRACT-based model that overlaps with that from IDREAM).</p

ROC curves for growth defect predictions using IDREAM and PROM on Yeast6 model.

<p>A. Threshold is 0.5 for binarizing a call as “growth defect” or “no growth defect” B. Threshold is 0.2 for binarizing a call as “growth defect” or “no growth defect”.</p

Comparison of mean absolute residuals for IDREAM and PROM aggregating different yeast models.

<p>The first column shows three different yeast metabolic models, aggregate refers to the predictions for all three models taken together. Column 2–4 show the Pearson correlation coefficient, p-value, and mean absolute residuals difference between predicted and actual growth by IDREAM and IDREAM_hybrid model. Column 5–7 show the Pearson correlation coefficient, p-value, and mean absolute residuals difference by PROM_TF51. Column 8 ‘vs.res.pVal’ represents the significance of difference in correlations between the two IDREAM models and the PROM model. P-values were calculated using a Fisher’s Z transform. IDREAM_h means the IDREAM_hybrid model.</p