PENN: Phase Estimation Neural Network on Gene Expression Data

Aram Ansary Ogholbake, Qiang Cheng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


With the continuous expansion of available transcriptomic data like gene expression, deep learning techniques are becoming more and more valuable in analyzing and interpreting them. The National Center for Biotechnology Information Gene Expression Omnibus (GEO) encompasses approximately 5 million gene expression datasets from animal and human subjects. Unfortunately, the majority of them do not have a recorded timestamps, hindering the exploration of the behavior and patterns of circadian genes. Therefore, predicting the phases of these unordered gene expression measurements can help understand the behavior of the circadian genes, thus providing valuable insights into the physiology, behaviors, and diseases of humans and animals. In this paper, we propose a novel approach to predict the phases of the un-timed samples based on a deep neural network architecture. It incorporates the potential periodic oscillation information of the cyclic genes into the objective function to regulate the phase estimation. To validate our method, we use mouse heart, mouse liver and temporal cortex of human brain dataset. Through our experiments, we demonstrate the effectiveness of our proposed method in predicting phases and uncovering rhythmic pattern in circadian genes.

Original languageEnglish
Title of host publicationThe 4th Joint International Conference on Deep Learning, Big Data and Blockchain (DBB 2023) -
EditorsMuhammad Younas, Irfan Awan, Salima Benbernou, Dana Petcu
Number of pages9
StatePublished - 2023
Event4th Joint International Conference on Deep Learning, Big Data and Blockchain, DBB 2023 - Marrakech, Morocco
Duration: Aug 14 2023Aug 16 2023

Publication series

NameLecture Notes in Networks and Systems
Volume768 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389


Conference4th Joint International Conference on Deep Learning, Big Data and Blockchain, DBB 2023

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications


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