基于個性化網(wǎng)絡(luò)標(biāo)志物的藥物推薦方法研究

劉文斌; 吳倩; 杜玉改; 方剛; 石曉龍; 許鵬

doi:10.11999/JEIT190837

基于個性化網(wǎng)絡(luò)標(biāo)志物的藥物推薦方法研究

doi: 10.11999/JEIT190837 cstr: 32379.14.JEIT190837

劉文斌^{1, 2},
吳倩¹,
杜玉改¹,
方剛²,
石曉龍²,
許鵬^{2, 3, ,}

1.
溫州大學(xué)計算機與人工智能學(xué)院溫州 325035
2.
廣州大學(xué)計算科技研究院廣州 510006
3.
黔南民族師范學(xué)院計算機與信息學(xué)院都勻 558000

基金項目: 國家重點研發(fā)計劃(2019YFA0706402)，國家自然科學(xué)基金(61572367, 61573017, 61972107, 61972109)

詳細(xì)信息

作者簡介:
劉文斌：男，1969年生，教授，研究方向為生物信息學(xué)

吳倩：女，1994年生，碩士，研究方向為生物信息學(xué)

杜玉改：女，1993年生，碩士，研究方向為生物信息學(xué)

方剛：男，1969年生，教授，研究方向為生物信息學(xué)

石曉龍：男，1975年生，教授，研究方向為生物信息學(xué)

許鵬：男，1986年生，博士后，研究方向為生物信息學(xué)

通訊作者:
許鵬　gdxupeng@gzhu.edu.cn

中圖分類號: TP301
計量
- 文章訪問數(shù): 2845
- HTML全文瀏覽量: 1157
- PDF下載量: 256
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2019-10-29
- 修回日期: 2020-01-20
- 網(wǎng)絡(luò)出版日期: 2020-02-27
- 刊出日期: 2020-06-22

Drug Recommendation Based on Individual Specific Biomarkers

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)

摘要

摘要: 基于個性化標(biāo)志物的藥物推薦研究，有助于實現(xiàn)個性化用藥及推動精準(zhǔn)醫(yī)療的發(fā)展。該文利用基因表達(dá)譜數(shù)據(jù)及蛋白質(zhì)網(wǎng)絡(luò)信息，基于基因2維高斯分布方法篩選出個性化網(wǎng)絡(luò)標(biāo)志物。進而綜合考慮靶基因的重要性和藥物的副作用，提出了一種計算藥物對個性化標(biāo)志物影響權(quán)重的方法。將該方法應(yīng)用于肺腺癌、腎透明細(xì)胞癌和子宮內(nèi)膜癌數(shù)據(jù)集，通過啟發(fā)式搜索方法，得到每個疾病樣本重要藥物推薦列表。結(jié)果表明，推薦的藥物列表在同種癌癥不同樣本中既存在一致性，也表現(xiàn)出很大的差異性，如藥物種類及藥物排序差異，這說明個性化藥物在疾病治療中的重要性及必要性。通過從藥物數(shù)據(jù)庫中搜索藥物組合對疾病治療的影響作用表明，該文方法篩選得到的許多藥物組合對具體疾病治療具有積極影響，這進一步證明該文基于個性化網(wǎng)絡(luò)標(biāo)志物的藥物推薦方法的準(zhǔn)確性。該文的研究將有效促進精準(zhǔn)化醫(yī)療的發(fā)展。
- 精準(zhǔn)醫(yī)療 /
- 個性化標(biāo)志物 /
- 網(wǎng)絡(luò)標(biāo)志物 /
- 藥物推薦
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

HTML全文

圖 1 癌癥個性化網(wǎng)絡(luò)標(biāo)志物獲取流程

下載: 全尺寸圖片幻燈片

圖 2 3種癌癥中藥物靶基因數(shù)量與藥物副作用數(shù)量之間的散點圖

下載: 全尺寸圖片幻燈片

圖 3 3種癌癥中考慮藥物副作用和不考慮藥物副作用時藥物的排名

下載: 全尺寸圖片幻燈片

圖 4 3類癌癥得到的候選藥物集合在各個樣本中的具體分布

下載: 全尺寸圖片幻燈片

圖 5 DrugBank數(shù)據(jù)庫中具有協(xié)同作用的藥物對在各個樣本中的分布情況

下載: 全尺寸圖片幻燈片

表 1 3種癌癥數(shù)據(jù)集統(tǒng)計信息

癌癥類型	樣本數(shù)量(正常/癌癥)
LUAD	609(95/514)
KIRC	602(72/530)
UCEC	578(35/543)

下載: 導(dǎo)出CSV

表 2 啟發(fā)式搜索的迭代過程

個性化藥物推薦算法
輸入：物集合$D = \{ d_1,d_2, ··· ,{d_n}\} $;
個性化標(biāo)志物集合$T = \{ t_1,t_2, ··· ,t_m\} $;
輸出：個性化藥物推薦列表 (Personalized Drug, PD);
(1) Initialization: Set $k = 1$;
(2) DO
(3) for $i = 1,2, ··· ,n$
(4) Compute $S\left( {{d_i}} \right)$;
(5) EndFor
(6) If S(d_i) is the maximum among all drugs in $D$ then
(7) ${\rm{PD}}\left( k \right) = {d_i}$;
(8) $k = k + 1$;
(9) EndIf
(10) Update $D$: Delete d_i from $D$;
(11) Update $T$: Delete all targets of ${d_i}$ from $T$;
(12) WHILE Max(targets number of each drug in $D$)>=6

下載: 導(dǎo)出CSV

參考文獻(25)

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, CZERWI?SKA 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, 陳邈. 幾乎完備高斯整數(shù)序列構(gòu)造法[J]. 電子與信息學(xué)報, 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

陳曦, 張坤. 一種基于樹增強樸素貝葉斯的分類器學(xué)習(xí)方法[J]. 電子與信息學(xué)報, 2019, 41(8): 2001–2008. doi: 10.11999/JEIT180886

CHEN Xi and ZHANG Kun. A classifier learning method based on tree-augmented na?ve 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

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