MicroRNA mediated gene regulatory circuits leads to machine learning based preliminary detection of acute myeloid leukemia

Acute Myeloid Leukemia (AML) can be detected based on morphology, cytochemistry, immunological markers, and cytogenetics. MicroRNAs (miRNAs) influence key biological pathways in multiple haematological malig-nancies including AML. In this work, we have analysed the miRNome and the transcriptome of normal and AML samples and have identified the significant set of miRNA-target mRNA pairs present within AML-Peripheral Blood and AML-Bone Marrow samples from both tissue and cell lines. The miRNA target genes are further filtered based on their functional significance in AML system. These filtered genes constitute the set of selected miRNA target features, which have been finally used for developing machine learning based prediction tool, 'TbAMLPred' for preliminary detection of AML. This model implements both unsupervised clustering and supervised classification algorithms that would in-crease the reliability of prediction. Our results show that the selected miRNA target-based features can separate the control and disease samples linearly. Overall, we put forward 'TbAMLPred' for a non-invasive mode of preliminary AML diagnosis in future. Github link for accessing TbAMLPred: https://github.com/zglabDIB/TbAMLPred

Automated summary

Acute Myeloid Leukemia (AML) can be detected by analyzing the miRNome and transcriptome of AML samples, identifying significant miRNA-target mRNA pairs, and developing the machine learning-based prediction tool 'TbAMLPred' for preliminary AML diagnosis. The selected miRNA target features can effectively separate control and disease samples, making 'TbAMLPred' a promising non-invasive tool for AML diagnosis in the future.