Phillipe Loher

The paper presents a benchmarking challenge to predict Alzheimer's disease neuropathology from single-nucleus transcriptomics using human and AI models, finding that top methods achieve near-perfect categorical staging predictions.

The paper identifies primate-specific motifs in human rRNAs and 45S spacers that are also found in nervous system genes linked to disorders like autism and schizophrenia.

CorrAdjust improves RNA-RNA correlation analysis by removing hidden confounders, enhancing the detection of biologically relevant interactions in human RNA-seq data.

This study profiles small RNAs in schizophrenia and bipolar disorder, finding altered sncRNA abundance and links to accelerated aging and key biological processes.

The paper investigates the subcellular localization of miRNA isoforms, tRFs, and rRFs across three cell lines, revealing that their localization depends on sequence and cell type, and creates an atlas of their distribution.

The paper uses machine learning to identify spatial informative genes and reconstruct 3-D embryo structure from single-cell transcriptomics data, achieving top rankings in DREAM challenges.

The paper predicts cellular positions in the Drosophila embryo using scRNA-seq data by benchmarking methods against spatially annotated genes.

The paper introduces mirGFF3, a standardized format for miRNA/isomiR analysis, and mirtop, a tool to convert outputs into this format, enabling data sharing and comparative analysis.

The paper analyzes tRNA fragments (tRFs) across 32 cancer types, identifying disease-specific patterns and associations with mRNAs and repeat elements.

The paper shows that binary isomiR profiles can accurately classify 32 TCGA cancer types with high sensitivity and low false discovery rate.