9 papers · ranked by Valyu relevance
Amrita Nagasuri, Umair Khan, Parker Grosjean, Adi Siddharth + 15 more
Endometriosis is a chronic inflammatory disease associated with pelvic pain, infertility, and delayed diagnosis. Growing evidence suggests that altered DNA methylation contributes to disease development and could serve as a biomarker for disease. We developed a leakage-safe machine learning pipeline to classify…
Huihai Wu, Ashleigh Lister, Iain Macaulay, Katie Long + 10 more
Single-cell and spatial transcriptomics are transforming our understanding of cellular heterogeneity and tissue organization, yet their analytical complexity remains a major bottleneck. Here, we present EISCA and EISTA, two standardized, end-to-end pipelines for single-cell RNA- seq and imaging-based spatial…
Li Peng, Weisi Lai, Jian Huang, Yan Zhong
Early detection of preeclampsia with noninvasive and reliable biomarkers is the foremost step for minimizing adverse effects during pregnancy. However, none maternal serum analyte evaluated so far is sufficiently accurate to allow recommending their routine use. Microarray was used to first identify differentially…
Maria B. Walter Costa, Rose Brouns, Maria Schreiber, Aristeidis Litos + 5 more
Understanding the adaptations of microorganisms to their environment is key to predicting the stability and dynamics of microbial communities. To uncover molecular mechanisms of environmental response, we extracted genomic features from 13,554 prokaryotic isolates, and trained machine learning models to identify which…
Lena Trnovec, Miha Štajdohar, Gad Shaulsky, Blaž Zupan
dictyExpress 3.0 is a React/TypeScript reimplementation of the Dictyostelium discoideum transcriptomics web server, first introduced in 2009, that unifies bulk and single-cell exploration. A long-standing gene-expression resource for the Dictyostelium community, dictyExpress is also accessible through dictyBase, the…
Alejandro Espinoza, Bruno J. de Andrade Silva, Matteo Pellegrini
Single-cell RNA sequencing technologies provide insights into gene expression at the cellular level, enabling detailed analysis of cellular heterogeneity. In this study, we systematically compared two scRNA-seq platforms,10X Genomics and Parse Biosciences, using human peripheral blood mononuclear cells (PBMCs) and…
Jackie Rao, Muntadher Jihad, Giulia Biffi, Paul D.W. Kirk
Identifying cell types from single-cell RNA sequencing (scRNA-seq) data typically requires several separate and often uninterpretable steps: dimensionality reduction, batch-correction, clustering, marker-gene identification and the discovery of finer-grained structure. Here we introduce scFLAME (single-cell Factor…
Yuqiao Liu, Siyu Yi, Hengchuang Yin, Wei Ju
Single-cell RNA sequencing profiles cellular heterogeneity at atlas scale, making automated annotation essential. However, target datasets often contain novel cell types missing from incomplete references. We present scOLAR, an ontology-guided open-set framework that learns prototypes over the Cell Ontology and uses…
Hayden Johnson, Boris A. Vinatzer, Reza Mazloom, Kassaye Belay + 2 more
Rapid and accurate microbial identification is critical for interpreting biological data in basic research and when making applied decisions on how to effectively treat patients and control human, animal, and plant diseases. Advancements in high-throughput sequencing have the potential to expedite fungal species…