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Research map / Probabilistic and scientific modeling / Molecules and proteins

Disease Trajectory Maps

NeurIPS 2016 · Probabilistic and scientific modeling › Molecules and proteins · approach: Spatiotemporal forecasting and imputation · 2 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use DTM representations instead of LMMscleroderma trajectory representation · PFVC, pulmonary arterial hypertensionp-value: 0.013 -> 0.002Helped
use DTM representations instead of LMM/FPCArepresentation association test · TSS, ulcers and gangrenep-value: 0.573 -> 0.009Helped
use DTM instead of FPCArepresentation association test · PFVC, pulmonary hypertensionp-value: 0.000 -> 0.000No clear effect
use DTM instead of LMMrepresentation association test · PFVC, pulmonary hypertensionp-value: 0.270 -> 0.000Helped
use DTM instead of LMMrepresentation association test · PFVC, myositisp-value: 0.337 -> 0.004Helped
use DTM instead of LMMrepresentation association test · TSS, gastrointestinal complicationsp-value: 0.026 -> 0.011Helped
use DTM instead of LMMrepresentation association test · PFVC, congestive heart failurep-value: 0.170 -> 0.013Helped
use DTM instead of LMMrepresentation association test · PFVC, gastrointestinal complicationsp-value: 0.328 -> 0.347Hurt
use DTM instead of LMMheld-out likelihood · PFVCSubject log-likelihood: -17.59 -> -17.74No clear effect
use DTM instead of LMMheld-out likelihood · TSSObservation log-likelihood: -3.47 -> -3.32Helped
use DTM instead of FPCAheld-out likelihood · PFVCObservation log-likelihood: -4.03 -> -3.98Helped
use DTM instead of LMMinterstitial lung disease association · PFVC and TSSp-value: 0.000 -> 0.000No clear effect

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