Deep learning (DL) has emerged as a powerful subset of machine learning (ML) and
artificial intelligence (AI), outperforming traditional ML methods, especially in handling …

Simple Summary The ongoing advancements in deep learning, notably its use in predicting
cancer survival through genomic data analysis, calls for an up-to-date review. This paper …

Cells respond divergently to drugs due to the heterogeneity among cell populations. Thus, it
is crucial to identify drug-responsive cell populations in order to accurately elucidate the …

Multiple Myeloma is an incurable plasma cell malignancy with a poor survival rate that is
usually treated with immunomodulatory drugs (iMiDs) and proteosome inhibitors (PIs). The …

Large-scale studies of single-cell sequencing and biological experiments have successfully
revealed expression patterns that distinguish different cell types in tissues, emphasizing the …

Intratumoral cellular heterogeneity necessitates multi-targeting therapies for improved
clinical benefits in advanced malignancies. However, systematic identification of patient …

Although single-cell sequencing has provided a powerful tool to deconvolute cellular
heterogeneity of diseases like cancer, extrapolating clinical significance or identifying …

Background Single-cell RNA sequencing (scRNA-seq) has revolutionized the
transcriptomics field by advancing analyses from tissue-level to cell-level resolution. Despite …

Tumour heterogeneity is one of the main characteristics of cancer and can be categorised
into inter‐or intratumour heterogeneity. This heterogeneity has been revealed as one of the …

Single-cell RNA sequencing has revealed cellular heterogeneity in complex tissues, notably
benefiting research on diseases such as cancer. However, the integration of single-cell data …