Gene-based Collaborative Filtering using recommender system

Jinyu Hu*, Sugam Sharma, Zhiwei Gao, Victor Chang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


The recommender system (RS) has achieved substantial evolution in this information age of the twenty-first century, with no exception to biological domain. While RS has been effectively exploited in analysis of biological data for gene prediction, it has raised interesting research challenges such as how to explore the gene interest (Gi) and recommend the genes for individual patients. To meet these research challenges, we propose a novel TOP-N Gene-based Collaborative Filtering (GeneCF) algorithm based on Gi of patients. The GeneCF algorithm is aimed for matching more accurate recommendations about genes to the patients, with exceptional precision and coverage achieved. The GeneCF algorithm has been tested and evaluated on a hepatocellular carcinoma (HCC) gene expression database. We found that six genes could be the cause of liver cancer: AMP, SAA1, S100P, SPP1 and CY2A7 and AFP. The GeneCF algorithm contributes to help doctors provide smarter, customized care for cancer patients.

Original languageEnglish
Pages (from-to)332-341
Number of pages10
JournalComputers and Electrical Engineering
Publication statusPublished - Jan 2018


  • Big Data
  • Collaborative filtering
  • GPC
  • GeneCF
  • HCC
  • Recommender systems


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