Deep learning for prediction of population health costs.

Background: Accurate prediction of healthcare costs is important for optimally managing health costs. However, methods leveraging the medical richness from data such as health insurance claims or electronic health records are missing.

Methods: Here, we developed a deep neural network to predict future cost from health insurance claims records.

SLM2 Is A Novel Cardiac Splicing Factor Involved in Heart Failure due to Dilated Cardiomyopathy.

Alternative mRNA splicing is a fundamental process to increase the versatility of the genome. In humans, cardiac mRNA splicing is involved in the pathophysiology of heart failure. Mutations in the splicing factor RNA binding motif protein 20 (RBM20) cause severe forms of cardiomyopathy.

Spatio-temporal mRNA tracking in the early zebrafish embryo.

Early stages of embryogenesis depend on subcellular localization and transport of maternal mRNA. However, systematic analysis of these processes is hindered by a lack of spatio-temporal information in single-cell RNA sequencing. Here, we combine spatially-resolved transcriptomics and single-cell RNA labeling to perform a spatio-temporal analysis of the transcriptome during early zebrafish development.

Using gradient boosting with stability selection on health insurance claims data to identify disease trajectories in chronic obstructive pulmonary disease.

Objective: We propose a data-driven method to detect temporal patterns of disease progression in high-dimensional claims data based on gradient boosting with stability selection.

Materials And Methods: We identified patients with chronic obstructive pulmonary disease in a German health insurance claims database with 6.5 million individuals and divided them into a group of patients with the highest disease severity and a group of control patients with lower severity.

omniCLIP: probabilistic identification of protein-RNA interactions from CLIP-seq data.

CLIP-seq methods allow the generation of genome-wide maps of RNA binding protein - RNA interaction sites. However, due to differences between different CLIP-seq assays, existing computational approaches to analyze the data can only be applied to a subset of assays. Here, we present a probabilistic model called omniCLIP that can detect regulatory elements in RNAs from data of all CLIP-seq assays.

Expanding the map of protein-RNA interaction sites via cell fusion followed by PAR-CLIP.

PAR-CLIP (photoactivatable ribonucleoside-enhanced crosslinking and immunoprecipitation) facilitates the identification and mapping of protein/RNA interactions. So far, it has been limited to select cell-lines as it requires efficient 4SU uptake. To increase transcriptome complexity and thus identify additional RNA-protein interaction sites we fused HEK 293 T-Rex cells (HEK293-Y) that express the RNA binding protein YBX1 with PC12 cells expressing eGFP (PC12-eGFP).

Alternative Splicing Substantially Diversifies the Transcriptome during Early Photomorphogenesis and Correlates with the Energy Availability in Arabidopsis.

Plants use light as source of energy and information to detect diurnal rhythms and seasonal changes. Sensing changing light conditions is critical to adjust plant metabolism and to initiate developmental transitions. Here, we analyzed transcriptome-wide alterations in gene expression and alternative splicing (AS) of etiolated seedlings undergoing photomorphogenesis upon exposure to blue, red, or white light.