DMFS pipeline recovery of previously identified motifs.

<p>Here we list motifs identified by Tillo and Hughes <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0027382#pone.0027382-Tillo1" target="_blank">[48]</a> and Lee <i>et al.</i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0027382#pone.0027382-Lee1" target="_blank">[49]</a> and the number of times these motifs were identified by the DMFS pipeline. Structure related features are omitted, as are transcription binding start sites and features with zero weights. We ran the DMFS pipeline 40 times, with random data partitioning, and counted the number of times each previously identified motif occurred. According to Tillo and Hughes the most discriminative motif is the 4-mer AAAA/TTTT, which emerged in almost every run.</p

ROC curves from DMFS and enumerative methods for the nucleosome occupancy datasets.

<p>The red and green curves are from Gupta <i>et al.. </i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0027382#pone.0027382-Gupta1" target="_blank">[4]</a> for the Dennis and Ozsolak data respectively. The black and blue curves are from the DMFS method for the Dennis and Ozsolak data respectively. For both datasets, the DMFS ROC curve is approximately equal to the ROC curve using enumerative feature generation. This figure was created by manipulating <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0027382#pone-0027382-g001" target="_blank">Figure 1</a> of Gupta <i>et al.. </i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0027382#pone.0027382-Gupta1" target="_blank">[4]</a> in GIMP. The DMFS ROC curves are relative stable. As the false positive rate ranges from 10% to 90% the true positive rate standard deviations have range to for the Dennis data and to for the Ozsolak data.</p

Illustrative diagram of data flow through the pipeline.

<p>Data is initially partitioned into discovery and classification sets. The classification set is further partitioned into training and validation sets. After WordSpy elicits motifs using the discovery set, fuzznuc or fuzzpro counts corresponding motif occurrences in the remaining data. The training data counts are used to train a classifier, while the validation data counts are used to determine performance (e.g. AUC) of the learned classifier.</p

Protein solubility data.

<p>Protein solubility data accuracies for default and tuned parameters settings, as well as for reported and eumerative methods. Reported values are from Magnan <i>et al.</i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0027382#pone.0027382-Magnan1" target="_blank">[5]</a>. The DMFS pipeline results are stable with small standard deviations as determined by 20 runs with random data partitioning: (a) default parameter settings: 0.006 (SVM) and 0.0048 (RF), and (b) tuned parameter settings: 0.0052 (SVM) and 0.0049 (RF).</p

Nucleosome occupancy data.

<p>Mean AUCs for the nucleosome occupancy datasets and approaches as described in the text. Reported values are from Gupta <i>et al.</i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0027382#pone.0027382-Gupta1" target="_blank">[4]</a>. The DMFS pipeline results are stable with small standard deviations as determined by 40 runs with random data partitioning: Dennis data with (a) default parameter settings: 0.0055 (SVM) and 0.0036 (RF), and (b) tuned parameter settings: 0.0048 (SVM) and 0.0041 (RF); Ozsolak data with (a) default parameter settings: 0.0084 (SVM) and 0.0078 (RF), and (b) tuned parameter settings: 0.011 (SVM) and 0.0086 (RF).</p

Standardized observation-based behavioral batteries for evaluating recovery after spinal cord injury in the rodents.

<p><b><i>A,</i></b> Grooming scale scoring system and recovery plots color coded by injury conditions. <b><i>B,</i></b> Paw placement task and recovery plots. <b><i>C,</i></b> Basso, Beattie, Bresnahan (BBB) open field hindlimb locomotor scale. <b><i>D,</i></b> Fine motor-focused, BBB subscore. <b><i>E,</i></b> Forelimb open field score. Three different standardized models of SCI were included in the dataset: hemisections, force-driven contusions (kdyns) and weight-drop contusions (mm) centered at cervical vertebra 5 (C5) and delivered to one side of the spinal cord. Data were collected over 10 years at two different SCI centers (The Ohio State University, and University of California, San Francisco) and represent over 159 subjects with complete outcome batteries. Error bars reflect SEM as used by general linear models (e.g., ANOVA). Note that all points have error bars although some are smaller than the points. (see manuscript for references).</p

Derivation of Multivariate Syndromic Outcome Metrics for Consistent Testing across Multiple Models of Cervical Spinal Cord Injury in Rats

<div><p>Spinal cord injury (SCI) and other neurological disorders involve complex biological and functional changes. Well-characterized preclinical models provide a powerful tool for understanding mechanisms of disease; however managing information produced by experimental models represents a significant challenge for translating findings across research projects and presents a substantial hurdle for translation of novel therapies to humans. In the present work we demonstrate a novel ‘syndromic’ information-processing approach for capitalizing on heterogeneous data from diverse preclinical models of SCI to discover translational outcomes for therapeutic testing. We first built a large, detailed repository of preclinical outcome data from 10 years of basic research on cervical SCI in rats, and then applied multivariate pattern detection techniques to extract features that are conserved across different injury models. We then applied this translational knowledge to derive a data-driven multivariate metric that provides a common ‘ruler’ for comparisons of outcomes across different types of injury (NYU/MASCIS weight drop injuries, Infinite Horizons (IH) injuries, and hemisection injuries). The findings revealed that each individual endpoint provides a different view of the SCI syndrome, and that considering any single outcome measure in isolation provides a misleading, incomplete view of the SCI syndrome. This limitation was overcome by taking a novel multivariate integrative approach for leveraging complex data from preclinical models of neurological disease to identify therapies that target multiple outcomes. We suggest that applying this syndromic approach provides a roadmap for translating therapies for SCI and other complex neurological diseases.</p> </div

Histological outcomes.

<p><b><i>A,</i></b> Tissue sparing measures in SCI research are typically taken at the lesion center as determined by the largest extent of the lesion ellipsoid. Although specific methods for quantification may vary across studies, typical measures include lesion size, <b><i>B,</i></b> gray matter (GM) sparing, <b><i>C,</i></b> white matter (WM) sparing, <b><i>D,</i></b> total sparing (GM+WM), <b><i>E,</i></b> total tissue area (GM+WM+debris), <b><i>F,</i></b> motorneuron number. Scale bar, 100 µm. Since the compiled dataset was limited to unilateral injuries (hemisections or hemicontusions), all measures are represented as a percentage of the contralateral, spared hemicord. The quantified area is illustrated in red on a representative example. The representative example was taken from the subject closest to the group mean for lesion size across the study’s 159 subjects.</p

Standardized digital locomotor analysis for evaluating recovery after spinal cord injury in rodents.

<p><b><i>A,</i></b> Digital footprint analysis allows objective quantification of many correlated outcomes including: <b><i>B,</i></b> Stride-length for each limb; <b><i>C,</i></b> Print area for each limb; <b><i>D,</i></b> Distribution of limb use reflected as the absolute deviation from the pre-injury baseline (i.e. deviation from ∼25% recruitment for each limb).</p

Consistency of multivariate syndromic patterns across two different biomechanically controlled cervical spinal contusion models.

<p><b><i>A,</i></b> SCI syndromic space extracted from an NYU/MASCIS injury device dataset (N = 52 rats; 24 outcome variables). <b><i>B,</i></b> SCI syndromic space extracted from an Infinite Horizons injury device dataset (N = 100 rats, 24 outcome variables). Note, normed PC score axes are scaled according to variance within each extraction, resulting in axes with units that are not directly comparable across extractions. However relative relationships among groups (sham vs. injuries) are conserved. <b><i>C,</i></b> Consensus PC loading patterns that are conserved across injury patterns. Loading weights (arrows) reflect average values across the two datasets. <b><i>D,</i></b> Statistical evaluation of PC cross-validation in the PC loading matrices from NYU/MASCIS and IH injury datasets. *p<.05 for n = 24 variables; s>0.63.</p