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Fig. 1 | Journal of Hematology & Oncology

Fig. 1

From: Multi-dimensional fragmentomic assay for ultrasensitive early detection of colorectal advanced adenoma and adenocarcinoma

Fig. 1

Schematic illustration of study design. Plasma samples were collected from patients with advanced colorectal adenoma (advCRA) or early-stage (stage 0/I) adenocarcinoma (CRC), as well as healthy controls. The cfDNA was then extracted from the participant’s plasma sample and subject to whole-genome sequencing. Five different feature types, including Fragment Size Ratio (FSR), Fragment Size Distribution (FSD), EnD Motif (EDM), BreakPoint Motif (BPM) and Copy Number Variation (CNV), were calculated using mapped sequencing reads. For each feature type, a base model was constructed based on the ensemble learning of five algorithm, GLM, GBM, random forest, deep learning and Xgboost. The base model predictions were then ensembled into a large matrix, which was subsequently used by a GLM algorithm to train the final ensemble stack model

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