TY - GEN
T1 - GDFM
T2 - 30th ACM International Conference on Information and Knowledge Management
AU - Mansha, S.
AU - Khalid, T.
AU - Kamiran, F.
AU - Hussain, Masroor
AU - Hussain, S.F.
AU - Yin, Hongzhi
PY - 2021/10/30
Y1 - 2021/10/30
N2 - 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.
AB - 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.
KW - Gene Network Graphs
KW - Expression information
KW - Deep Attentional Factorization Machines
KW - Interaction prediction
UR - http://www.scopus.com/inward/record.url?eid=2-s2.0-85119172762&partnerID=MN8TOARS
UR - https://www.scopus.com/pages/publications/85119172762
U2 - 10.1145/3459637.3482110
DO - 10.1145/3459637.3482110
M3 - Conference contribution
SN - 9781450384469
T3 - Proceedings of the ACM International Conference on Information & Knowledge Management
SP - 3323
EP - 3327
BT - CIKM '21
PB - Association for Computing Machinery (ACM)
Y2 - 1 November 2021 through 5 November 2021
ER -