An Artificial Intelligence-Based Stacked Ensemble Approach for Prediction of Protein Subcellular Localization in Confocal Microscopy Images

Sonam Aggarwal (), Sheifali Gupta, Deepali Gupta, Yonis Gulzar (), Sapna Juneja, Ali A. Alwan and Ali Nauman
Additional contact information
Sonam Aggarwal: Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab 140401, India
Sheifali Gupta: Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab 140401, India
Deepali Gupta: Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab 140401, India
Yonis Gulzar: Department of Management Information Systems, College of Business Administration, King Faisal University, Al-Ahsa 31982, Saudi Arabia
Sapna Juneja: KIET Group of Institutions, Delhi NCR, Ghaziabad 201206, India
Ali A. Alwan: School of Theoretical and Applied Science, Ramapo College of New Jersey, Mahwah, NJ 07430, USA
Ali Nauman: Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea

Sustainability, 2023, vol. 15, issue 2, 1-20

Abstract: Predicting subcellular protein localization has become a popular topic due to its utility in understanding disease mechanisms and developing innovative drugs. With the rapid advancement of automated microscopic imaging technology, approaches using bio-images for protein subcellular localization have gained a lot of interest. The Human Protein Atlas (HPA) project is a macro-initiative that aims to map the human proteome utilizing antibody-based proteomics and related c. Millions of images have been tagged with single or multiple labels in the HPA database. However, fewer techniques for predicting the location of proteins have been devised, with the majority of them relying on automatic single-label classification. As a result, there is a need for an automatic and sustainable system capable of multi-label classification of the HPA database. Deep learning presents a potential option for automatic labeling of protein’s subcellular localization, given the vast image number generated by high-content microscopy and the fact that manual labeling is both time-consuming and error-prone. Hence, this research aims to use an ensemble technique for the improvement in the performance of existing state-of-art convolutional neural networks and pretrained models were applied; finally, a stacked ensemble-based deep learning model was presented, which delivers a more reliable and robust classifier. The F1-score, precision, and recall have been used for the evaluation of the proposed model’s efficiency. In addition, a comparison of existing deep learning approaches has been conducted with respect to the proposed method. The results show the proposed ensemble strategy performed exponentially well on the multi-label classification of Human Protein Atlas images, with recall, precision, and F1-score of 0.70, 0.72, and 0.71, respectively.

Keywords: deep learning; sustainable healthcare; biomedical image analysis; image classification; artificial intelligence; protein subcellular localization prediction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/2/1695/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/2/1695/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:2:p:1695-:d:1037400

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().