Establishing a nomogram for predicting the risk factors for delayed bleeding after endoscopic submucosal dissection for colorectal tumors.

Background: The aim of this study is to establish a nomogram that can predict the risk factors for delayed bleeding after endoscopic submucosal dissection (ESD). This model can be used to assess the probability of delayed bleeding before ESD surgery, thereby avoiding wasting medical resources and improving patient satisfaction.

Methods: This was a retrospective study in which all patients underwent ESD surgery for colorectal tumors between August 2021 and February 2024.

Dual-view jointly learning improves personalized drug synergy prediction.

Motivation: Accurate and robust estimation of the synergistic drug combination is important for medicine precision. Although some computational methods have been developed, some predictions are still unreliable especially for the cross-dataset predictions, due to the complex mechanism of drug combinations and heterogeneity of cancer samples.

Results: We have proposed JointSyn that utilizes dual-view jointly learning to predict sample-specific effects of drug combination from drug and cell features.

A systematic assessment of deep learning methods for drug response prediction: from in vitro to clinical applications.

Drug response prediction is an important problem in personalized cancer therapy. Among various newly developed models, significant improvement in prediction performance has been reported using deep learning methods. However, systematic comparisons of deep learning methods, especially of the transferability from preclinical models to clinical cohorts, are currently lacking.