Network Elastic Net for Identifying Smoking specific gene expression for lung cancer

利用机器学习识别女性非吸烟者III期非小细胞肺癌的预后生物标志物

Identification of Prognostic Biomarkers for Stage III Non-Small Cell Lung Carcinoma in Female Nonsmokers Using Machine Learning

本研究基于GDS3837基因表达数据，利用XGBoost机器学习算法识别出C/EBP-alpha、LDHA等五个与女性非吸烟者III期非小细胞肺癌相关的预后生物标志物，AUC达0.835，为早期诊断和个性化治疗提供依据。

Analysis of Genomic and Transcriptomic Variations as Prognostic Signature for Lung Adenocarcinoma

Lung cancer is the leading cause of the largest number of deaths worldwide and lung adenocarcinoma (LUAD) is the most common form of lung cancer. In this study, we carried out an integrated meta-analysis of the mutations including single-nucleotide variations (SNVs), the copy number variations (CNVs), RNA-seq and clinical data of patients with LUAD downloaded from The Cancer Genome Atlas (TCGA). We integrated significant SNV and CNV genes, differentially expressed genes (DEGs) and the DEGs in active subnetworks to construct a prognosis signature. Cox proportional hazards model (LOOCV) with Las…

Multi-source Transfer Learning with Convolutional Neural Networks for Lung Pattern Analysis

Early diagnosis of interstitial lung diseases is crucial for their treatment, but even experienced physicians find it difficult, as their clinical manifestations are similar. In order to assist with the diagnosis, computer-aided diagnosis (CAD) systems have been developed. These commonly rely on a fixed scale classifier that scans CT images, recognizes textural lung patterns and generates a map of pathologies. In a previous study, we proposed a method for classifying lung tissue patterns using a deep convolutional neural network (CNN), with an architecture designed for the specific problem. In…