Generative Prediction of Causal Gene Sets Responsible for Complex Traits

Information Science, Artificial Intelligence, Life Sciences, Cell Biology, Biochemistry, Bioengineering, Genetics,
gene regulatory networks   complex traits   generative deep learning   variational autoencoder   causal gene sets   phenotypic variation   polygenic diseases  

Generative Deep Learning for Predicting Causal Gene Sets of Complex Traits: PNAS Landmark New Method Explained I. Academic Background and Research Motivation The Dilemma of Complex Traits The relationship between genotype and phenotype has long been one of the core issues in biology and genetics. This challenge is especially prominent in the study ...

SIRCLE Model Reveals Mechanisms of Phenotype Regulation in Renal Cancer

Medicine, Urology, Nephrology, Oncology,
multi-omics integration   phenotype regulation   machine learning   variational autoencoder   clear cell renal cell carcinoma   pancan   metabolism  

Executive Report on the Study of ccRCC Regulatory Mechanisms Using the SIRCLE Model Background Overview Clear Cell Renal Cell Carcinoma (ccRCC) is the most prevalent form of kidney cancer, accounting for 70% of renal malignancies. The development and progression of ccRCC involve intricate remodeling of the kidney epigenome, transcriptome, proteome,...

Graph Neural Network for Representation Learning of Lung Cancer

Graph Neural Network for Representation Learning of Lung Cancer

Information Science, Computer Science, Artificial Intelligence, Medicine, Respiratory System, Medical Imaging, Oncology,
multiple instance learning   graph   whole slide images   graph neural networks   lung cancer   variational autoencoder  

Representation Learning of Lung Cancer Based on Graph Neural Networks Background Introduction With the rapid development of digital pathology, image-based diagnostic systems are becoming increasingly important in precise pathological diagnosis. These systems rely on Multiple Instance Learning (MIL) technology for Whole Slide Images (WSIs). However,...