Syed Abu Bakar Hannan Shakir Ahmed

Artificial Intelligence Specialization · Cohort 2026

Cardiac Maturation Clock Based on MYH7/MYH6 and Isoform-Switch Ratios in iPSC-CMs

Human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) are valuable for disease modeling and regenerative medicine but remain limited by functional immaturity. We developed a deep learning framework using a Deep Count Autoencoder with a Zero-Inflated Negative Binomial loss to extract robust transcriptomic maturation signatures from scRNA-seq data without log normalization. By minimizing batch effects between in vitro iPSC-CMs and adult GTEx heart tissue through relative scaling and bridging datasets, the model generates a continuous maturity score based on adult-like gene expression. The approach improves upon previous XGBoost models and identifies key maturation drivers, including CASQ2, providing a standardized tool for evaluating cardiomyocyte maturity

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