Opportunities for Artificial Intelligence in Advancing Precision Medicine

Abstract

Machine learning (ML), deep learning (DL), and artificial intelligence (AI) are of increasing importance in biomedicine. The goal of this work is to show progress in ML in digital health, to exemplify future needs and trends, and to identify any essential prerequisites of AI and ML for precision health.High-throughput technologies are delivering growing volumes of biomedical data, such as largescale genome-wide sequencing assays, libraries of medical images, or drug perturbation screens of healthy, developing, and diseased tissue. Multi-omics data in biomedicine is deep and complex, offering an opportunity for data-driven insights and automated disease classification. Learning from these data will open our understanding and definition of healthy baselines and disease signatures. State-of-the-art applications of deep neural networks include digital image recognition, single cell clustering, and virtual drug screens, demonstrating breadths and power of ML in biomedicine.Significantly, AI and systems biology have embraced big data challenges and may enable novel biotechnology-derived therapies to facilitate the implementation of precision medicine approaches.

scGCN: a Graph Convolutional Networks Algorithm for Knowledge Transfer in Single Cell Omics

“…In addition to the successful results, there are several aspects in which scGCN can be improved. First, as an artificial intelligence (AI) model, scGCN shows not only the merits of its kind, but also some limitations including the black-box nature of AI models [47][48][49], which can be addressed through downstream analysis such as differential gene identification and enrichment analysis that can ameliorate some of the problems and bring insights into the labeled cells. Second, as a graph model, improving the graph construction can further boost the model performance.…”

“…However, existing datasets and new datasets are often 46 collected from different tissues and species [14,15], under various experimental conditions, 47 generated by different platforms [16,17], and in the form of different omics types [18]. Thus a 48 reliable and accurate knowledge transfer method must overcome the following challenges: 1) the 49 unique technical issues of single-cell data (e.g., dropouts and dispersion) [19][20][21][22]; 2) batch effects 50 arisen from different operators, experimental protocols [23], and technical variation (e.g., mRNA quality, pre-amplification efficiency, technical settings during data generation) [24][25][26];…”

DSTG: Deconvoluting Spatial Transcriptomics Data through Graph-based Artificial Intelligence

“…In addition to the successful results, there are several aspects that DSTG can be improved. First, as an artificial intelligence (AI) model, DSTG shows not only the merits of its kind, but also some limitations including the black-box nature of AI models [34][35][36], which can be addressed through downstream analysis that can ameliorate some of the problems and bring insights into the learned cellular compositions. Second, as a graph model, improving the built graph can further boost the model performance.…”

Isabl Platform, a digital biobank for processing multimodal patient data

“…genomic, imaging) [ 3 ]. Such high-dimensional data science is now embedded across disciplines, raising significant hopes for the development of artificial intelligence (AI) driven innovation in healthcare and research [ 3 , 4 ]. However, for this aspiration to fully materialize there is a clear and unmet need for the development of AI-ready data architectures or digital biobanks.…”