Integration of artificial intelligence and multi-omics in kidney diseases

A machine learning and deep learning-based integrated multi-omics technique for leukemia prediction

“…Previous research has utilized machine learning (ML) and deep learning (DL) to predict leukemia cancer using single data [ 83 , 84 ]. To analyze data related to breast cancer and other types, prior investigations have preferred DL algorithms such as CNN, RNN, ANN, and VAE (Variational Autoencoder) with relu activation function and BSE as loss function [ [85] , [86] , [87] , [88] , [89] ]. Feature selection methods such as PCA, RF Recursive selection, and Chi-square have been widely used in earlier research.…”

AI-enabled organoids: Construction, analysis, and application

“…Therefore, the analysis and processing of massive multi-omics sequencing data present significant challenges to traditional analysis methods in organoids research. AI can simplify this process by using machine learning to handle these complex datasets and extract meaningful biological insights [ 171 ]. In recent years, the rapid development of machine learning has provided a unique means for multi-omics data analysis to explore complex relationships between different omics and phenotypic targets.…”

Missing data in multi-omics integration: Recent advances through artificial intelligence

“…Included in these are articles discussing examples of method application (e.g., Holzinger et al, 2019 ; Li et al, 2021 ) and challenges in applying AI and ML techniques to multi-omics data (e.g., Kang et al, 2021 ; Termine et al, 2021 ). Many are focused on specific application domains such as metabolic engineering (Helmy et al, 2020 ), precision medicine (Hamamoto et al, 2019 ), amongst others (e.g., Mann et al, 2021 ; Lin et al, 2022 ; Zhou et al, 2022 ) and a majority focused on cancer research (e.g., Wang and Gu, 2016 ; Biswas and Chakrabarti, 2020 ; Nicora et al, 2020 ; Cai et al, 2022 ). Finally, a handful of reviews are dedicated to discussion of subsets of general AI and ML approaches to multi-omics data integration (e.g., Li et al, 2016 ; Huang et al, 2017 ; Kim and Tagkopoulos, 2018 ; Picard et al, 2021 ; Reel et al, 2021 ; Lee and Kim, 2022 ) with some attention given to limitations of methods, such as interpretability of models.…”