Lana Xia Garmire

HOMED enables hierarchical and multimodal optimization of DNA methylation deconvolution across tissues

HOMED improves DNA methylation deconvolution by integrating cell-lineage hierarchies and single-cell data, achieving higher accuracy across tissues.

Rescue Blood Contamination in scRNA-Seq Data by Originator, a Computational Deciphering Tool using Genetic and Contextual Information

Originator identifies and removes blood contamination in scRNA-seq data using genetic and contextual information, improving accuracy in downstream analysis.

Landscape of Differentiation Potentials as a "Hallmark" in Oral-derived MSCs

The paper characterizes oral-derived mesenchymal stem cells (MSCs) by analyzing their differentiation potential and identifying differentially expressed genes across three MSC subtypes.

Identification and Validation of New Molecular Subtypes within the Early and Late Mild Cognitive Impairment Stages of Alzheimer's Disease

This study identifies and validates two molecular subtypes within early and late mild cognitive impairment stages of Alzheimer's disease using integrated metabolomics and transcriptomics data.

Maternal plasma lipids are involved in the pathogenesis of preterm birth

This study identifies maternal plasma lipid metabolites associated with preterm birth using metabolomic analysis and machine learning in the Puerto Rico PROTECT cohort.

BML: a versatile web server for bipartite motif discovery

BML is a web server for bipartite motif discovery that uses PWM and DWM to improve accuracy, offering automatic motif identification without requiring expert input.

Intercellular signaling dynamics from a single cell atlas of the biomaterials response

The paper creates a single cell atlas of biomaterials response using scRNA-seq and develops a tool to analyze intercellular signaling patterns in the context of biomaterials.

GranatumX: A community engaging, modularized and flexible webtool for single-cell analysis

GranatumX provides a web-based platform for single-cell RNA sequencing analysis, enabling biologists to use bioinformatics tools through a graphical interface and allowing developers to integrate their own tools into customizable pipelines.

Two-stage biologically interpretable neural-network models for liver cancer prognosis prediction using histopathology and transcriptomic data

The paper develops a two-stage neural network model for liver cancer prognosis using histopathology and transcriptomic data, showing improved accuracy compared to existing methods.

Lilikoi V2.0: a deep-learning enabled, personalized pathway-based R package for diagnosis and prognosis predictions using metabolomics data

Lilikoi V2.0 is a new R package that uses deep learning and machine learning for disease classification and prognosis prediction using metabolomics data.

Evaluation of Cell Type Annotation R Packages on Single Cell RNA-seq Data

The paper evaluates ten cell-type annotation methods on scRNA-seq data, finding Seurat performs best for major cell types but struggles with rare populations and similar cell types.

Maternal blood lipidomics analyses link critical metabolic pathways associated with severe preeclampsia

This study identifies lipidomic changes in maternal blood linked to severe preeclampsia, showing altered lipid species and their potential as biomarkers.

Metabolomics profile of umbilical cord blood is associated with maternal pre-pregnant obesity in a prospective multi-ethnic cohort displaying health disparities

The paper identifies metabolites in umbilical cord blood linked to maternal pre-pregnant obesity using metabolomics in a multi-ethnic cohort.

DeepImpute: an accurate, fast and scalable deep neural network method to impute single-cell RNA-Seq data

DeepImpute is a deep neural network method for imputing single-cell RNA-seq data, showing better accuracy and scalability than existing methods.

Lilikoi: an R package for personalized pathway-based classification modeling using metabolomics data

Lilikoi is an R package that enables personalized pathway-based classification using metabolomics data through four modules for mapping, transformation, selection, and classification.

Multi-modal meta-analysis of 1494 hepatocellular carcinoma samples reveals vast impacts of consensus driver genes on phenotypes

This study identifies 10 consensus driver genes in HCC across six cohorts and shows their associations with mRNA transcriptomes, patient survival, and demographic factors.

Granatum: a graphical single-cell RNA-Seq analysis pipeline for genomics scientists

Granatum provides a web-based, graphical pipeline for scRNA-Seq analysis, enabling non-programmers to perform various analysis steps without coding.

Deep learning accurately predicts estrogen receptor status in breast cancer metabolomics data

This study compares deep learning and machine learning methods for classifying estrogen receptor status in breast cancer metabolomics data, finding deep learning achieves higher accuracy.