Abstract Machine learning (ML) and deep learning (DL) have enabled algorithms to
autonomously acquire knowledge from data, facilitating predictive and decision-making …

The advent of human induced pluripotent stem cells (hiPSCs) and their capacity to be
differentiated into beating human cardiomyocytes (CMs) in vitro has revolutionized human …

Induced pluripotent stem cells (iPSCs) can be differentiated into mesenchymal stem cells
(iPSC-MSCs), retinal ganglion cells (iPSC-RGCs), and retinal pigmental epithelium cells …

In the present research we tackled the classification of seven genetic cardiac diseases and
control subjects by using an extensive set of machine learning algorithms with their …

Cancer stem cells (CSCs), also known as tumor-initiating cells (TICs), are a subset of tumor
cells that persist within tumors as a distinct population. They drive tumor initiation, relapse …

Human induced pluripotent stem cells (hiPSCs) can be used to generate various cell types
in the human body. Hence, hiPSC-derived cardiomyocytes (hiPSC-CMs) represent a …

Functional changes to cardiomyocytes are undesirable during drug discovery and
identifying the inotropic effects of compounds is hence necessary to decrease the risk of …

Artificial neural networks (ANN), machine learning (ML), deep learning (DL), and ensemble
learning (EL) are four outstanding approaches that enable algorithms to extract information …

Background: Orthobiologics have shown promise in repair, restoration and regeneration of
damaged and degenerated spine, joint and musculoskeletal tissues. The role of MSCs is to …

Induced pluripotent stem cells (iPSCs) hold great promise in regenerative medicine;
however, few algorithms of quality control at the earliest stages of differentiation have been …