@inproceedings{d3cb06d8797143c989cc68f1c237ca93,
title = "GDFM: gene vectors embodied deep attentional factorization machines for interaction prediction",
abstract = "Gene Network Graphs (GNGs) are comprised of biomedical data. Deriving structural information from these graphs remains a prime area of research in the domain of biomedical and health informatics. In this paper, we propose Gene Vectors Embodied Deep Attentional Factorization Machines (GDFMs) for the gene to gene interaction prediction. We first initialize GDFM with vector embeddings learned from gene locality configuration and an expression equivalence criterion that preserves their innate similar traits. GDFM uses an attention-based mechanism that manipulates different positions, to learn the representation of sequence, before calculating the pairwise factorized interactions. We further use hidden layers, batch normalization, and dropout to stabilize the performance of our deep structured architecture. An extensive comparison with several state-of-the-art approaches, using Ecoli and Yeast datasets for gene-gene interaction prediction shows the significance of our proposed framework.",
keywords = "Gene Network Graphs, Expression information, Deep Attentional Factorization Machines, Interaction prediction",
author = "S. Mansha and T. Khalid and F. Kamiran and Masroor Hussain and S.F. Hussain and Hongzhi Yin",
year = "2021",
month = oct,
day = "30",
doi = "10.1145/3459637.3482110",
language = "English",
isbn = "9781450384469",
series = "Proceedings of the ACM International Conference on Information & Knowledge Management",
publisher = "Association for Computing Machinery (ACM)",
pages = "3323–3327",
booktitle = "CIKM '21",
address = "United States",
note = "30th ACM International Conference on Information and Knowledge Management, CIKM2021 ; Conference date: 01-11-2021 Through 05-11-2021",
}