INFLAMeR: a machine learning algorithm based on large-scale perturbation screening identified new lncRNAs regulating differentiation and survival of leukaemia cells

<strong>Abstract</strong> Long non-coding RNAs (lncRNAs) are a diverse group of transcripts with poorly understood<br> functionality. To address this gap, we developed INFLAMeR, an advanced machine learning<br> model trained on CRISPRi screening data, to predict functional lncRNAs using comprehensive<br> genetic features. We experimentally validated the predictions by assessing their impact on cell<br> proliferation and anticancer drug resistance. Among the selected lncRNAs, 85% showed<br> significant effects upon knockdown, while low-scoring lncRNAs had no discernible impact.<br> Notably, our study elucidated the functional role of SNHG6 in hematopoietic differentiation.<br> INFLAMeR greatly enhances the prediction of functional lncRNAs, providing valuable insights<br> into their regulatory landscape. By integrating INFLAMeR with experimental validation, we can<br> identify and characterize functional lncRNAs in a cell-type-specific manner, contributing to a<br> deeper understanding of their involvement in cellular processes. Our findings revealed insights<br> into lncRNA biology and a framework for improving the identification of functional lncRNAs.