Drug Recommendation Based on Individual Specific Biomarkers

Wenbin LIU; Qian WU; Yugai DU; Gang FANG; Xiaolong SHI; Peng XU

doi:10.11999/JEIT190837

Volume 42 Issue 6

Jun. 2020

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2020 > 42(6): 1340-1347

Wenbin LIU, Qian WU, Yugai DU, Gang FANG, Xiaolong SHI, Peng XU. Drug Recommendation Based on Individual Specific Biomarkers[J]. Journal of Electronics & Information Technology, 2020, 42(6): 1340-1347. doi: 10.11999/JEIT190837

Citation:

Wenbin LIU, Qian WU, Yugai DU, Gang FANG, Xiaolong SHI, Peng XU. Drug Recommendation Based on Individual Specific Biomarkers[J]. Journal of Electronics & Information Technology, 2020, 42(6): 1340-1347. doi: 10.11999/JEIT190837

Citation:

PDF( 4041 KB)

Drug Recommendation Based on Individual Specific Biomarkers

doi: 10.11999/JEIT190837

Wenbin LIU^{1, 2},
Qian WU¹,
Yugai DU¹,
Gang FANG²,
Xiaolong SHI²,
Peng XU^{2, 3
,
,}

1.
College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China
2.
Institute of Computing Science and Technology, Guangzhou University, Guangzhou 510006, China
3.
School of Computer Science of Information Technology, Qiannan Normal University for Nationalities, Duyun 558000, China

Funds: The National Key R&D Program of China (2019YFA0706402), The National Natural Science Foundation of China (61572367, 61573017, 61972107, 61972109)

Received Date: 2019-10-29
Rev Recd Date: 2020-01-20

Available Online: 2020-02-27

Publish Date: 2020-06-22

Abstract

Abstract

Drug recommendation research based on personalized markers can help to achieve personalized medicine and promote the development of precision medicine. In this paper, a method for calculating the weight of drugs on personalized markers is proposed, which first uses gene expression profile data and protein network information to filter out personalized network markers based on gene two-dimensional Gaussian distribution and then uses the importance degree of genes and the drugs side effect data to calculate the weight of drugs. This method is applied to lung adenocarcinoma, kidney renal clear cell carcinoma and uterine corpus endometrial carcinoma. Through the iterative process, a list of important drug recommendations for each disease sample is got. The results show that there are some differences in the recommended drug list and the ordering importance of drugs in different cases of the same kind of cancer, which indicates the importance and necessity of personalized drugs in the treatment of diseases. By querying the relationship between drugs from the drug database, many of the drug combinations screened by this method have a positive effect on the treatment of specific diseases, which further proves the accuracy of the drug recommendation methods based on personalized network markers. This study will effectively promote the development of precision medicine.
- Precision medicine,
- Personalized biomarkers,
- Network biomarkers,
- Drug recommendation

FullText(HTML)

References(25)

References

SIEGEL R L, MILLER K D, and JEMAL A. Cancer statistics, 2016[J]. CA: A Cancer Journal for Clinicians, 2016, 66(1): 7–30. doi: 10.3322/caac.21332

TORRE L A, BRAY F, SIEGEL R L, et al. Global cancer statistics, 2012[J]. CA: A Cancer Journal for Clinicians, 2015, 65(2): 87–108. doi: 10.3322/caac.21262

WANG Hongwei, SUN Qiang, ZHAO Wenyuan, et al. Individual-level analysis of differential expression of genes and pathways for personalized medicine[J]. Bioinformatics, 2015, 31(1): 62–68. doi: 10.1093/bioinformatics/btu522

LIU Xiaoping, WANG Yuetong, JI Hongbin, et al. Personalized characterization of diseases using sample-specific networks[J]. Nucleic Acids Research, 2016, 44(22): e164. doi: 10.1093/nar/gkw772

ZHANG Wanwei, ZENG Tao, and CHEN Luonan. EdgeMarker: Identifying differentially correlated molecule pairs as edge-biomarkers[J]. Journal of Theoretical Biology, 2014, 362: 35–43. doi: 10.1016/j.jtbi.2014.05.041

YU Xiangtian, ZHANG Jingsong, SUN Shaoyan, et al. Individual-specific edge-network analysis for disease prediction[J]. Nucleic Acids Research, 2017, 45(20): e170. doi: 10.1093/nar/gkx787

PERLMAN L, GOTTLIEB A, ATIAS N, et al. Combining drug and gene similarity measures for drug-target elucidation[J]. Journal of Computational Biology, 2011, 18(2): 133–145. doi: 10.1089/cmb.2010.0213

WANG Wenhui, YANG Sen, and LI Jing. Drug Target Predictions Based on Heterogeneous Graph Inference[M]. ALTMAN R B, DUNKER A K, HUNTER L, et al. Biocomputing 2013. Hawaii, USA: World Scientific, 2013: 53-64 doi: 10.1142/9789814447973_0006.

ZONG Nansu, KIM H, NGO V, et al. Deep mining heterogeneous networks of biomedical linked data to predict novel drug-target associations[J]. Bioinformatics, 2017, 33(15): 2337–2344. doi: 10.1093/bioinformatics/btx160

SU Junjie, YOON B J, and DOUGHERTY E R. Accurate and reliable cancer classification based on probabilistic inference of pathway activity[J]. PLoS One, 2009, 4(12): e8161. doi: 10.1371/journal.pone.0008161

TOMCZAK K, CZERWIŃSKA P, and WIZNEROWICZ M. The Cancer Genome Atlas (TCGA): An immeasurable source of knowledge[J]. Contemporary Oncology (Poznan, Poland) , 2015, 19(1A): A68–A77. doi: 10.5114/wo.2014.47136

SZKLARCZYK D, MORRIS J H, COOK H, et al. The STRING database in 2017: Quality-controlled protein-protein association networks, made broadly accessible[J]. Nucleic Acids Research, 2017, 45(D1): D362–D368. doi: 10.1093/nar/gkw937

WISHART D S, FEUNANG Y D, GUO A C, et al. DrugBank 5.0: A major update to the DrugBank database for 2018[J]. Nucleic Acids Research, 2018, 46(D1): D1074–D1082. doi: 10.1093/nar/gkx1037

GRIFFITH M, GRIFFITH O L, COFFMAN A C, et al. DGIdb: Mining the druggable genome[J]. Nature Methods, 2013, 10(12): 1209–1210. doi: 10.1038/nmeth.2689

李玉博, 陈邈. 几乎完备高斯整数序列构造法[J]. 电子与信息学报, 2018, 40(7): 1752–1758. doi: 10.11999/JEIT170844

LI Yubo and CHEN Miao. Construction of nearly perfect gaussian integer sequences[J]. Journal of Electronics &Information Technology, 2018, 40(7): 1752–1758. doi: 10.11999/JEIT170844

陈曦, 张坤. 一种基于树增强朴素贝叶斯的分类器学习方法[J]. 电子与信息学报, 2019, 41(8): 2001–2008. doi: 10.11999/JEIT180886

CHEN Xi and ZHANG Kun. A classifier learning method based on tree-augmented naïve bayes[J]. Journal of Electronics &Information Technology, 2019, 41(8): 2001–2008. doi: 10.11999/JEIT180886

FANUCCHI M P, FOSSELLA F V, BELT R, et al. Randomized phase ii study of bortezomib alone and bortezomib in combination with docetaxel in previously treated advanced non-small-cell lung cancer[J]. Journal of Clinical Oncology, 2006, 24(31): 5025–5033. doi: 10.1200/JCO.2006.06.1853

DAVIES A M, RUEL C, LARA P N, et al. The proteasome inhibitor bortezomib in combination with gemcitabine and carboplatin in advanced non-small cell lung cancer: A california cancer consortium phase I study[J]. Journal of Thoracic Oncology, 2008, 3(1): 68–74. doi: 10.1097/JTO.0b013e31815e8b88

DAVIES A M, CHANSKY K, LARA Jr P N, et al. Bortezomib plus gemcitabine/carboplatin as first-line treatment of advanced non-small cell lung cancer: A phase II southwest oncology group study (S0339)[J]. Journal of Thoracic Oncology, 2009, 4(1): 87–92. doi: 10.1097/JTO.0b013e3181915052

KELLY K, CROWLEY J, BUNN PA, et al. Randomized phase III trial of paclitaxel plus carboplatin versus vinorelbine plus cisplatin in the treatment of patients with advanced non-small-cell lung cancer: A southwest oncology group trial[J]. Journal of Clinical Oncology, 2001, 19(13): 3210–3218. doi: 10.1200/JCO.2001.19.13.3210

SANDLER A, GRAY R, PERRY M C, et al. Paclitaxel-carboplatin alone or with bevacizumab for non-small-cell lung cancer[J]. New England Journal of Medicine, 2006, 355(24): 2542–2550. doi: 10.1056/NEJMoa061884

MCNAMARA M, SWEENEY C, ANTONARAKIS E S, et al. The evolving landscape of metastatic hormone-sensitive prostate cancer: A critical review of the evidence for adding docetaxel or abiraterone to androgen deprivation[J]. Prostate Cancer and Prostatic Diseases, 2018, 21(3): 306–318. doi: 10.1038/s41391-017-0014-9

ROBINET G, BARLESI F, FOURNEL P, et al. Second-line therapy with gefitinib in combination with docetaxel for advanced non-small cell lung cancer: A phase II randomized study[J]. Targeted Oncology, 2007, 2(2): 63–71. doi: 10.1007/s11523-007-0042-9

ANGELOPOULOU A, KOLOKITHAS-NTOUKAS A, FYTAS C, et al. Folic acid-functionalized, condensed magnetic nanoparticles for targeted delivery of doxorubicin to tumor cancer cells overexpressing the folate receptor[J]. ACS Omega, 2019, 4(26): 22214–22227. doi: 10.1021/acsomega.9b03594

TEDESCO-SILVA H, PASCUAL J, VIKLICKY O, et al. Safety of everolimus with reduced calcineurin inhibitor exposure in de novo kidney transplants: An analysis from the randomized TRANSFORM study[J]. Transplantation, 2019, 103(9): 1953–1963. doi: 10.1097/TP.0000000000002626

Relative Articles

Supplements(0)

Cited By

Proportional views