Methodologies in Predictive Visual Analytics

abstract: Predictive analytics embraces an extensive area of techniques from statistical modeling to machine learning to data mining and is applied in business intelligence, public health, disaster management and response, and many other fields. To date, visualization has been broadly used to support tasks in the predictive analytics pipeline under the underlying assumption that a human-in-the-loop can aid the analysis by integrating domain knowledge that might not be broadly captured by the system. Primary uses of visualization in the predictive analytics pipeline have focused on data cleaning, exploratory analysis, and diagnostics. More recently, numerous visual analytics systems for feature selection, incremental learning, and various prediction tasks have been proposed to support the growing use of complex models, agent-specific optimization, and comprehensive model comparison and result exploration. Such work is being driven by advances in interactive machine learning and the desire of end-users to understand and engage with the modeling process. However, despite the numerous and promising applications of visual analytics to predictive analytics tasks, work to assess the effectiveness of predictive visual analytics is lacking. This thesis studies the current methodologies in predictive visual analytics. It first defines the scope of predictive analytics and presents a predictive visual analytics (PVA) pipeline. Following the proposed pipeline, a predictive visual analytics framework is developed to be used to explore under what circumstances a human-in-the-loop prediction process is most effective. This framework combines sentiment analysis, feature selection mechanisms, similarity comparisons and model cross-validation through a variety of interactive visualizations to support analysts in model building and prediction. To test the proposed framework, an instantiation for movie box-office prediction is developed and evaluated. Results from small-scale user studies are presented and discussed, and a generalized user study is carried out to assess the role of predictive visual analytics under a movie box-office prediction scenario.Dissertation/ThesisDoctoral Dissertation Engineering 201

Comparison of proinflammatory mediator concentrations between US (<i>white bars</i>) and LPS–stimulated (<i>grey bars</i>).

<p>BMMs were grown on soft <i>(0</i>.<i>3 kPa</i>, <i>left graphs)</i> and stiff <i>(230 kPa</i>, <i>right graphs)</i> substrates. Data were assessed using Mann–Whitney U test. N_wells≥7.</p

TLR4 signal transduction on PA gels.

<p><i>(A)</i> Representative TLR4 immunoblot (<i>top</i>) and densitometry (<i>bottom</i>). Lysates from US and stimulated BMMs grown on 0.3, 47, or 230 kPa gels were subjected to Western blotting. Blots were probed with (<i>from top to bottom</i>) rabbit anti–TLR4 or anti–GAPDH antibodies. For densitometry, data are a percentage of 230 kPa band intensities. Data were assessed using one–way ANOVA followed by Tukey’s multiple comparisons test. <i>(B)</i> Representative IκBα immunoblot (<i>top</i>) and densitometry (<i>bottom</i>). Blots were probed with (<i>from top to bottom</i>) anti–p–IκBα, anti–IκBα, or anti–GAPDH antibodies. For densitometry, data are a ratio of p–IκBα/IκBα. Data were assessed using unpaired t test with Welch’s correction. <i>(C)</i> Representative NF–κB (<i>top</i>) immunoblot and densitometry (<i>bottom</i>). Blots were probed with (<i>from top to bottom</i>) anti–p–NF–κB, anti–NF–κB and anti–GAPDH antibodies. For densitometry, data are a ratio of p–NF–κB/NF–κB. Data were assessed using unpaired t test with Welch’s correction. <i>(D)</i> Representative p–NF–κB immunoblot for cytosolic (C) and nuclear (N) fractions (<i>top</i>) and densitometry (<i>bottom</i>). Blots were probed with anti–p–NF–κB antibody. For densitometry, data are a percentage of 230 kPa band intensities. Data were assessed using unpaired t test with Welch’s correction. Three or more samples were subjected to Western blotting for each antibody.</p

Comparison of proinflammatory mediator concentrations in media between US (<i>white bars</i>) and stimulated (<i>grey bars</i>) BMMs.

<p>BMMs were grown on collagen–or laminin–functionalized soft (<i>left graphs)</i> and stiff <i>(right graphs)</i> gels. Data were assessed using Mann–Whitney U test. N_wells≥ 4.</p

Proinflammatory mediator expression.

<p>Lysates from US and stimulated BMMs grown on PA gels were subjected to Western blotting. <i>(A)</i> Representative immunoblots. Blots were probed with (<i>from top to bottom</i>) rabbit anti–TNF–α, anti–IL–1β, anti–IL–6, anti–iNOS, or GAPDH antibodies. <i>(B)</i> Densitometry for anti–TNF–α. Data were assessed using one–way ANOVA followed by Tukey’s multiple comparisons test. Densitometry was not performed for blots probed for IL–1β, IL–6, iNOS because only stimulated BMMs on 230 kPa show protein expression. Three or more samples were subjected to Western blotting for each antibody.</p

Proinflammatory mediator concentrations in media from TNF–α–stimulated BMMs.

<p>Media from TNF–α–stimulated BMMs grown on PA gels were subjected to ELISAs or Griess assays. Graphs show concentrations of (<i>from top to bottom</i>) IL–1β, IL–6, and NO. Data were assessed using one–way ANOVA followed by Tukey’s multiple comparisons test. N_wells≥ 4.</p

Comparison of proinflammatory mediator concentrations in media between US (<i>white bars</i>) and TNF–α–stimulated (<i>grey bars</i>) BMM.

<p>BMMs were grown on soft <i>(left graphs)</i> and stiff <i>(right graphs)</i> substrates. N_wells≥7. Data were assessed using Mann–Whitney U test. N_wells≥ 4.</p

MyD88-dependent TLR4 signal transduction.

<p><i>(A)</i> TLR4–deficient BMMs grown on gels. Media from US (<i>white bars</i>) and stimulated (<i>grey bars</i>) TLR4–deficient (<i>TLR4</i>^–/–) and WT BMMs grown on 230 kPa gels were subjected to ELISAs and Griess assays. Graphs show concentrations of (<i>from top to bottom</i>) IL–1β, IL–6, and NO. Data from ELISAs were assessed using Kruskal–Wallis followed by Dunn’s multiple comparisons test and data from Griess assays were assessed using one–way ANOVA followed by Tukey’s multiple comparisons test. N_wells≥ 21. <i>(B)</i> Representative MyD88 (<i>top</i>) and IFN–β (<i>middle</i>) immunoblots. Lysates from US and stimulated BMM grown on 0.3, 47, and 230 kPa gels were subjected to Western blotting. Blots were probed with (<i>from top to bottom</i>) anti–MyD88, anti–IFN–β, and anti–GAPDH antibodies. <i>(C)</i> Densitometry. Data are a percentage of 230 kPa band intensities. Data were assessed using one–way ANOVA followed by Tukey’s multiple comparisons test. Densitometry for IFN–β was not performed because no visual changes were observed. Three or more samples were subjected to Western blotting.</p