BASAL: a universal mapping algorithm for nucleotide base-conversion sequencing.

Utilizing base-conversion (BC) techniques, single-base resolution profiling of RNA and DNA modifications has significantly advanced. BC strategies range from one-way conversions (e.g.

Multi-omics analysis of adamantinomatous craniopharyngiomas reveals distinct molecular subgroups with prognostic and treatment response significance.

Background: Adamantinomatous craniopharyngioma (ACP) is the commonest pediatric sellar tumor. No effective drug is available and interpatient heterogeneity is prominent. This study aimed to identify distinct molecular subgroups of ACP based on the multi-omics profiles, imaging findings, and histological features, in order to predict the response to anti-inflammatory treatment and immunotherapies.

Development of a Machine Learning Classifier for Brain Tumors Diagnosis Based on DNA Methylation Profile.

More than 150 types of brain tumors have been documented. Accurate diagnosis is important for making appropriate therapeutic decisions in treating the diseases. The goal of this study is to develop a DNA methylation profile-based classifier to accurately identify various kinds of brain tumors.

MRI-Based Grading of Clear Cell Renal Cell Carcinoma Using a Machine Learning Classifier.

Objective: To develop a machine learning (ML)-based classifier for discriminating between low-grade (ISUP I-II) and high-grade (ISUP III-IV) clear cell renal cell carcinomas (ccRCCs) using MRI textures.

Materials And Methods: We retrospectively evaluated a total of 99 patients (with 61 low-grade and 38 high-grade ccRCCs), who were randomly divided into a training set ( = 70) and a validation set ( = 29). Regions of interest (ROIs) of all tumors were manually drawn three times by a radiologist at the maximum lesion level of the cross-sectional CMP sequence images.