Li Liu

The paper identifies multiple genetic variants associated with childhood apraxia of speech through whole-exome sequencing and functional analysis, proposing four novel candidate genes.

This study links germline variation to tumor evolutionary dynamics in multiple myeloma, identifying TBKBP1 as a mediator of subclonal expansion and clinical outcomes.

This study identifies protein domains under positive selection in tumor genomes, revealing convergent evolution in core cellular processes and regulatory functions.

The paper introduces ceQTL, a method to detect eQTLs that affect transcription factor binding and target gene regulation using differential co-expression analysis.

TEATIME estimates evolutionary parameters and fitness diversity from bulk sequencing data, revealing patterns of intratumor heterogeneity across 33 cancer types.

INCLOSE identifies condition-specific cell populations by integrating single-cell omics data and metadata, revealing immune suppression in acute myeloid leukemia.

This study introduces MPET, a computational framework to analyze how intracellular transport affects surface protein expression using CITE-seq data from COVID-19 patients.

This study profiles the transcriptional responses to three mRNA vaccine doses, showing sustained polyfunctional immunity after a nine-month interval.

DeepCORE identifies co-operative regulatory elements by modeling genetic, epigenetic, and transcriptional patterns across 25 cell lines, outperforming existing methods and providing interpretable insights.

The paper adapts a human cancer driver gene detection model to mice using transfer learning, identifying 24 driver genes in mouse breast cancer exomes.

This study evaluates ChatGPT's ability to interpret bioinformatics illustrations, finding it effective for explaining plots and applying biological knowledge but lacking in quantitative analysis.

This study explores using ChatGPT to assist beginners in bioinformatics by fine-tuning it to generate code for data analysis tasks and discusses its educational applications and limitations.

The paper identifies molecular risk factors associated with increased COVID-19 mortality by linking gene expression changes in pre-existing conditions to hazard ratios of mortality.

TreeMap identifies causal eQTL variants by accounting for multisite effects and linkage, showing improved accuracy and enabling genome-wide analysis of long-range eQTL.

This study introduces a computational method to classify cancer driver genes as oncogenes or tumor suppressors based on mutation patterns across 33 cancer types, identifying novel genes and revealing context-specific functions.

MAGOS improves subclone detection in tumors sequenced at standard depths by correcting sequencing errors and outperforms existing methods in accuracy and efficiency.

The paper shows that unbalanced sample sizes in genome-wide heterozygosity analyses can create spurious correlations with disease status, advocating for balanced sample sizes to improve reproducibility.