Computational Drug Repositioning (CDR) is the task of discovering potential new indications for existing drugs by mining large-scale heterogeneous drug-related data sources. Leveraging the patient-level temporal ordering information between numeric physiological measurements and various drug prescriptions provided in Electronic Health Records (EHRs), we propose a Continuous Self-controlled Case Series (CSCCS) model for CDR. As an initial evaluation, we look for drugs that can control Fasting Blood Glucose (FBG) level in our experiments. Applying CSCCS to the Marshfield Clinic EHR, well-known drugs that are indicated for controlling blood glucose level are rediscovered. Furthermore, some drugs with recent literature support for the potential effect of blood glucose level control are also identified.
There is great interest in methods to improve human insight into trained non-linear models. Leading approaches include producing a ranking of the most relevant features, a non-trivial task for non-linear models. We show theoretically and empirically the benefit of a novel version of recursive feature elimination (RFE) as often used with SVMs; the key idea is a simple twist on the kinds of sensitivity testing employed in computational learning theory with membership queries (e.g., [1]). With membership queries, one can check whether changing the value of a feature in an example changes the label. In the real-world, we usually cannot get answers to such queries, so our approach instead makes these queries to a trained (imperfect) non-linear model. Because SVMs are widely used in bioinformatics, our empirical results use a real-world cancer genomics problem; because ground truth is not known for this task, we discuss the potential insights provided. We also evaluate on synthetic data where ground truth is known.
We present the baseline regularization model for computational drug repurposing using electronic health records (EHRs). In EHRs, drug prescriptions of various drugs are recorded throughout time for various patients. In the same time, numeric physical measurements (e.g. fasting blood sugar level) are also recorded. Baseline regularization uses statistical relationships between the occurrences of prescriptions of some particular drugs and the increase or the decrease in the values of some particular numeric physical measurements to identify potential repurposing opportunities.
In this note, we consider an abstract system of two damped elastic systems. The damping involves the average velocity and a fractional power of the principal operator, with power $\theta$ in $[0,1]$. The damping matrix is degenerate, which makes the the regularity analysis more delicate. First, using a combination of the frequency domain method and multipliers technique, we prove the following regularity for the underlying semigroup:
\begin{itemize}
\item The semigroup is of Gevrey class $\delta$ for every $\delta>1/2\theta$, for each $\theta$ in $(0,1/2)$.
\item The semigroup is analytic for $\theta=1/2$.
\item The semigroup is of Gevrey class $\delta$ for every $\delta>1/2(1-\theta)$, for each $\theta$ in $(1/2,1)$.\end{itemize}
Next, we analyze the point spectrum, and derive the optimality of our regularity results. We also prove that the semigroup is not differentiable for $\theta=0$ or $\theta=1$. Those results strongly improve upon some recent results presented in \cite{ast}.
Mendelian Randomization (MR) is an important causal inference method primarily used in biomedical research. This work applies contemporary techniques in machine learning to improve the robustness and power of traditional MR tools. By denoising and combining candidate genetic variants through techniques from unsupervised probabilistic graphical models, an influential latent instrumental variable is constructed for causal effect estimation. We present results on identifying relationships between biomarkers and the occurrence of coronary artery disease using individual-level real-world data from UK-BioBank via the proposed method. The approach, termed Instrumental Variable sYnthesis (IVY) is proposed as a complement to current methods, and is able to improve results based on allele scoring, particularly at moderate sample sizes. 2016; Kang, Zhang, Cai, & Small, 2016), or modern (deep learning-based techniques (Hartford, Lewis, Leyton-Brown, & Taddy, 2017; IVY seeks to produce instrumental variables that can be used by downstream standard instrumental variable methodology for estimating causal-effects.
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