Machine Learning-Based Prediction of Blood Stream Infection in Pediatric Febrile Neutropenia.

Objectives: This study aimed to develop machine learning (ML) prediction models for identifying bloodstream infection (BSI) and septic shock (SS) in pediatric patients with cancer who presenting febrile neutropenia (FN) at emergency department (ED) visit.

Materials And Methods: A retrospective study was conducted on patients, aged younger than 18 years, who visited a tertiary university-affiliated hospital ED due to FN between January 2004 and August 2022. ML models, based on XGBoost, were developed for BSI and SS prediction.

Proteogenomic Analysis to Identify Missing Proteins from Haploid Cell Lines.

Chromosome-centric Human Proteome Project aims at identifying and characterizing protein products encoded from all human protein-coding genes. As of early 2017, 19 837 protein-coding genes have been annotated in the neXtProt database including 2691 missing proteins that have never been identified by mass spectrometry. Missing proteins may be low abundant in many cell types or expressed only in a few cell types in human body such as sperms in testis.