Functional and embedding feature analysis for pan-cancer classification

Abstract

With the increasing number of people suffering from cancer, this illness has become a major health problem worldwide. Exploring the biological functions and signaling pathways of carcinogenesis is essential for cancer detection and research. In this study, a mutation dataset for eleven cancer types was first obtained from a web-based resource called cBioPortal for Cancer Genomics, followed by extracting 21,049 features from three aspects: relationship to GO and KEGG (enrichment features), mutated genes learned by word2vec (text features), and protein-protein interaction network analyzed by node2vec (network features). Irrelevant features were then excluded using the Boruta feature filtering method, and the retained relevant features were ranked by four feature selection methods (least absolute shrinkage and selection operator, minimum redundancy maximum relevance, Monte Carlo feature selection and light gradient boosting machine) to generate four feature-ranked lists. Incremental feature selection was used to determine the optimal number of features based on these feature lists to build the optimal classifiers and derive interpretable classification rules. The results of four feature-ranking methods were integrated to identify key functional pathways, such as olfactory transduction (hsa04740) and colorectal cancer (hsa05210), and the roles of these functional pathways in cancers were discussed in reference to literature. Overall, this machine learning-based study revealed the altered biological functions of cancers and provided a reference for the mechanisms of different cancers.

DNA methylation-based classifier differentiates intrahepatic pancreato-biliary tumours

“…Secondly, in order to check if the methylation signals used for classifier development are specific for iCCA, PAAD and normal bile duct or pan-cancer specific, 67 the classifier should be tested on samples other than those to be classified, ideally on liver metastases. Moreover, other signal selection methods 51 , 68 , 69 could be considered for classifier development that could improve pan-cancer performance. Our data regarding this scenario are preliminary, and we do not have a full understanding on how the neural network model will classify other entities.…”

Immune responses of different COVID-19 vaccination strategies by analyzing single-cell RNA sequencing data from multiple tissues using machine learning methods

“…These algorithms included LASSO (Ranstam and Cook, 2018), LightGBM (Ke et al, 2017), MCFS (Dramiński and Koronacki, 2018), mRMR (Peng et al, 2005), and PFI (Fisher et al, 2019). These methods have been widely practiced in solving life science problems (Zhao et al, 2018;Li et al, 2022a;Li et al, 2022b;Li Z. et al, 2022;Lu et al, 2022;Huang et al, 2023a;Huang et al, 2023b).…”