DuReS: An R Package for Denoising Experimental Tandem Mass Spectra and Metabolite Annotation.

Mass spectrometry-based untargeted metabolomics is a powerful technique for profiling small molecules in biological samples, yet accurate metabolite identification remains challenging. The presence of random noise peaks in tandem mass spectra can lead to false annotations and necessitate time-consuming manual verification. A common method for removing noise from mass spectra is intensity thresholding, where low-intensity peaks are discarded by applying a user-defined cutoff.

Machine learning-driven identification of the gene-expression signature associated with a persistent multiple organ dysfunction trajectory in critical illness.

Background: Multiple organ dysfunction syndrome (MODS) disproportionately drives morbidity and mortality among critically ill patients. However, we lack a comprehensive understanding of its pathobiology. Identification of genes associated with a persistent MODS trajectory may shed light on underlying biology and allow for accurate prediction of those at-risk.

iCOMIC: a graphical interface-driven bioinformatics pipeline for analyzing cancer omics data.

Despite the tremendous increase in omics data generated by modern sequencing technologies, their analysis can be tricky and often requires substantial expertise in bioinformatics. To address this concern, we have developed a user-friendly pipeline to analyze (cancer) genomic data that takes in raw sequencing data (FASTQ format) as input and outputs insightful statistics. Our iCOMIC toolkit pipeline featuring many independent workflows is embedded in the popular Snakemake workflow management system.

Sequence Neighborhoods Enable Reliable Prediction of Pathogenic Mutations in Cancer Genomes.

Identifying cancer-causing mutations from sequenced cancer genomes hold much promise for targeted therapy and precision medicine. "Driver" mutations are primarily responsible for cancer progression, while "passengers" are functionally neutral. Although several computational approaches have been developed for distinguishing between driver and passenger mutations, very few have concentrated on using the raw nucleotide sequences surrounding a particular mutation as potential features for building predictive models.

Machine Learning Identifies Complicated Sepsis Course and Subsequent Mortality Based on 20 Genes in Peripheral Blood Immune Cells at 24 H Post-ICU Admission.

A complicated clinical course for critically ill patients admitted to the intensive care unit (ICU) usually includes multiorgan dysfunction and subsequent death. Owing to the heterogeneity, complexity, and unpredictability of the disease progression, ICU patient care is challenging. Identifying the predictors of complicated courses and subsequent mortality at the early stages of the disease and recognizing the trajectory of the disease from the vast array of longitudinal quantitative clinical data is difficult.