CELL CYCLE STATE PREDICTION USING GRAPH NEURAL NETWORKS

bioRxiv (Cold Spring Harbor Laboratory)

Abstract

ABSTRACT Mitosis is a crucial process ensuring the faithful transmission of the genetic information stored in the cell nucleus. Aberrations in this intricate process pose a significant threat to an organism’s health, leading to conditions like cancer and various diseases. Hence, the study of mitosis holds paramount importance. Recent investigations have involved manual and semiautomated analyses of time-lapse microscopy images to understand mitosis better. This paper introduces an approach for predicting mitosis stages, employing a Convolutional Neural Network (CNN) as the initial feature extractor, followed by a Graph Neural Network (GNN) for predicting cell cycle states. A distinctive timestamp is incorporated into the feature vectors, treating this information as a graph to leverage internal interactions for predicting the subsequent cell state. To assess performance, experiments were conducted on three datasets, demonstrating that our method exhibits comparable efficacy to state-of-the-art techniques.

Authors 4

1. Sayan Acharya

   Jadavpur University

   Affiliation as printed

   Computer Science and Engineering Department, Jadavpur University, Kolkata, India

2. Aditya Ganguly

   Jadavpur University

   Affiliation as printed

   Computer Science and Engineering Department, Jadavpur University, Kolkata, India

3. Ram Sarkar

   Jadavpur University

   Affiliation as printed

   Computer Science and Engineering Department, Jadavpur University, Kolkata, India

4. Abin Jose corresponding Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

   Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany