AI-based Cancer Stem Cell profiler and Neoplasia Deconvoluter (ACSCeND)

ACSCeND is a cutting-edge computational tool that integrates machine learning (ML) and deep learning (DL) methodologies to analyze stem cell data. The tool comprises two key components:

1. ML-based Classifier: This module is designed to classify single-cell transcriptomics data of stem cells into three categories—Pluripotent, Multipotent, and Unipotent—offering insights into stem cell states.

2. DL-based Deconvoluter: This component is used for deconvoluting bulk RNA-seq data to estimate and quantify the cellular compositions within a mixed sample, providing valuable information on the distribution of different cell types.

ACSCeND is the result of a collaborative effort between research groups led by Prof. Shubhasis Haldar at the SN Bose National Centre for Basic Sciences and Prof. Debayan Gupta at Ashoka University. This tool is aimed at advancing research in stem cell biology by leveraging modern computational approaches for more precise and scalable analysis.

For more information about ACSCeND, read the related article:
Comprehensive Enumeration of Cancer Stem-like Cell Heterogeneity Using Deep Neural Network
Available at bioRxiv.