Shaoke Lou

This paper uses wearable data and AI to identify genetic associations with psychiatric disorders by creating intermediate phenotypes from continuous physiological features.

The paper processes single-nuclei multi-omics data from 388 human brains to identify regulatory elements and build cell-type networks, revealing changes in aging and neuropsychiatric disorders.

This study uses PRO-cap to identify drug-responsive enhancers in prostate cancer, finding many not detected by ChIP-seq or ATAC-seq.

This paper constructs a multiple-layer interactome for SARS-CoV-2 by integrating human genes, miRNAs, and co-infecting microbes to identify potential drug targets.

This paper applies Bayesian structural time series to biomedical sensor data to evaluate interventions, showing its effectiveness in assessing treatment impacts with complex covariates.

The paper identifies gene signatures predictive of asthma severity using a denoising autoencoder and random forest classifier applied to sputum gene expression data.

RADAR identifies variants affecting RNA-binding protein function by integrating conservation, RNA structure, and tissue-specific data to prioritize noncoding variants.

The paper develops a custom ENCODE annotation for cancer genomics, highlighting TF-RBP interactions and network rewiring during oncogenesis, and validates findings using experimental assays.

DiNeR identifies significant changes in transcription factor co-regulation networks by analyzing ChIP-seq data from disease and normal samples, revealing key disease-associated regulators.

The paper integrates heterogeneous RNA-seq data from asthmatic patients to uncover gene-microbe interactions and their clinical associations.

TopicNet measures transcriptional regulatory network change by abstracting gene regulatory topics and quantifying rewiring scores between cellular states, revealing TFs with significant network connectivity changes and their impact on cancer survival.

The paper introduces the Cancer LncRNA Census (CLC), a curated resource of lncRNAs with causative roles in cancer, and shows their genomic and evolutionary features.

ActivePathways integrates multivariate omics data to identify enriched pathways, revealing cancer driver genes and prognostic pathways in breast cancer.

GRAM predicts the molecular effect of non-coding variants in a cell-type specific manner using SELEX and expression profiles, achieving high performance on MPRA and luciferase assays.

MrTADFinder identifies topologically associating domains (TADs) from Hi-C data using network modularity, revealing multi-scale structures and boundary features.