推荐系统算法在生物信息学中应用的研究进展
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摘要
推荐系统算法是一种应用非常广泛的智能算法,比如对用户进行商品推荐.应用推荐系统算法,不仅节约时间,还可以节约资源.除了应用在商品推荐方面,推荐系统算法还可应用于生物信息学研究方面,比如预测长非编码RNA与疾病的关系,长非编码RNA与蛋白质的关系等.随着生物技术和医学研究的发展,越来越多的实验结果表明长非编码RNA与蛋白质的关系在疾病的发展过程中愈发重要.另一方面,生物实验研究不仅耗费很多资源,花费的时间也很多,而且准确性不一定高,所以发展节约资源且高效的智能算法是必然趋势.就以智能算法中的推荐系统算法为例,叙述长非编码RNA的概念以及推荐系统算法的概念,推荐系统算法的应用领域,优点与局限性;然后讨论推荐系统算法在生物信息学领域中的应用;接着详述如何用改进的二分投影推荐算法预测长非编码RNA-蛋白质相互作用关系;最后讨论推荐系统算法发展趋势.
Recommended system algorithm is a very wide application of the intelligent algorithm,such as the recommendation of the product to users.The application of recommended systems not only saves time,but also can save resources.In addition,the recommended system algorithm can also be applied to bioinformatics research,such as predicting the relationship between lncRNAs and diseases,lncRNA-protein interactions.With the development of biotechnology and medical research,more and more experimental evidences showthat therelationship between lncRNA and protein is becoming more and more important in the development of diseases.On the other hand,experimental research cost lots of resources,spent a lot of time and not had necessarily high accuracy.Therefore,the development of efficient and resource-saving intelligent algorithms is an inevitable trend.In this paper,we descripted the related concepts of lncRNAs and the recommended system,and discussed application fields,advantages and limitations of the recommended system algorithm.Then,we discussed the application of recommended system algorithm in the biological field.Especially,predicting lncRNA-protein relationship based on the Bipartite Network Projection Recommended Algorithm.Finally,we discussed the tendency of the development of the recommended system algorithm.
Recommended system algorithm is a very wide application of the intelligent algorithm,such as the recommendation of the product to users.The application of recommended systems not only saves time,but also can save resources.In addition,the recommended system algorithm can also be applied to bioinformatics research,such as predicting the relationship between lncRNAs and diseases,lncRNA-protein interactions.With the development of biotechnology and medical research,more and more experimental evidences showthat therelationship between lncRNA and protein is becoming more and more important in the development of diseases.On the other hand,experimental research cost lots of resources,spent a lot of time and not had necessarily high accuracy.Therefore,the development of efficient and resource-saving intelligent algorithms is an inevitable trend.In this paper,we descripted the related concepts of lncRNAs and the recommended system,and discussed application fields,advantages and limitations of the recommended system algorithm.Then,we discussed the application of recommended system algorithm in the biological field.Especially,predicting lncRNA-protein relationship based on the Bipartite Network Projection Recommended Algorithm.Finally,we discussed the tendency of the development of the recommended system algorithm.
引文
[1]Mohanty V,G9kmenpolar Y,Badve S,et al.Role of lncRNAs in health and disease-size and shape matter[J].Briefings in Functional Genomics,2015,14(2):115.
[2]Xing C,Yan C C,Xu Z,et al.Long non-coding RNAs and complex diseases:from experimental results to computational models[J].Briefings in Bioinformatics,2016.
[3]Guttman M,Amit I,Garber M,et al.Chromatin signature reveals over a thousand highly conserved large non-coding RNAs in mammals[J].Nature,2009,458(7235):223.
[4]Wapinski O,Chang HY.Long noncoding RNAs and human disease[J].Trends in Cell Biology,2011,21(6):354.
[5]Wilusz J E,Sunwoo H,Spector D L.Long noncoding RNAs:functional surprises from the RNA world[J].Genes&Development,2009,23(13):1494.
[6]Yu F,Zheng J,Mao Y,et al.Long non-coding RNA APTR promotes the activation of hepatic stellate cells and the progression of liver fibrosis[J].Biochemical&Biophysical Research Communications,2015,463(4):679-685.
[7]Prasanth K V,Spector D L.Eukaryotic regulatory RNAs:an answer to the‘genome complexity'conundrum[J].Genes Dev,2007,21(1):11-42.
[8]Paul S M,Mytelka D S,Dunwiddie C T,et al.How to improve R&D productivity:the pharmaceutical industry's grand challenge[J].Nature Review s Drug Discovery,2010,9(3):203.
[9]Lin C,Yang H K,Shan Z G.Application of Petri nets to bioinformatics[J].Chinese Journal of Computers,2007.
[10]Wang W,Yang S,Zhang X,et al.Drug repositioning by integrating target information through a heterogeneous netw ork model[J].Bioinformatics,2014,30(20):2923.
[11]Deng A L,Zhu Y Y,Shi B L.A Collaborative Filtering Recommendation Algorithm Based on Item Rating Prediction[J].Journal of Softw are,2003,14(9):54-65.
[12]Bellucci M,Agostini F,Masin M,et al.Predicting protein associations with long noncoding RNAs[J].Nature M ethods,2011,8(6):444-445.
[13]朱扬勇,孙婧.Recommender System:Up to Now[J].计算机科学与探索,2015,9(5):513-25.
[14]Resnick P,Iacovou N,Suchak M,et al.Group Lens:an open architecture for collaborative filtering of netnews[J].ACM Conference on Computer Supported Cooperative Work,1994.
[15]Resnick P,Varian H R.Recommender systems[J].Communications of the Acm,1997,40(3):56-58.
[16]Wu M X,Dong L S,Jie Z Y,et al.Research on Social Recommender Systems[J].Journal of Software,2015:26.
[17]Ge M,Wang M.A Bipartite Network-based Method for Prediction of Long Non-coding RNA–protein Interactions[J].基因组蛋白质组与生物信息学报,2016,14(1):62-71.
[18]Yin F,Zhao X,Zhang X,et al.Improving Accuracy and Scalability of Personal Recommendation Based on Bipartite Netw ork Projection[J].M athematical Problems in Engineering,2014,(2014-9-25).2014,2014(3).
[19]Huang Y A,Chen X,You ZH,et al.ILNCSIM:improved lncRNA functional similarity calculation model[J].Oncotarget,2016,7(18):25902-25914.
[20]Chen X,Huang Y A,Wang X S,et al.FMLNCSIM:fuzzy measure-based lncRNA functional similarity calculation model[J].Oncotarget,2016,7(29):45948-45958.
[21]Yuan J,Wu W,Xie C,et al.NPInter v2.0:an updated database of ncRNA interactions[J].Nucleic Acids Research.2014;42(Database issue):D104.
[22]Xie C,Yuan J,Li H,et al.NONCODEv4:exploring the world of long non-coding RNA genes[J].Nucleic Acids Research.2014;42(Database issue):D98.
[23]NONCODEv4:Annotation of Noncoding RNAs with Emphasis on Long Noncoding RNAs[J]:Springer New York,2016.
[24]Consortium UP.Uni Prot:a hub for protein information[J].Nucleic Acids Research,2015,43(Database issue):204-212.
[25]Pundir S,Martin MJ,O'Donovan C,et al.Uni Prot Tools[J].Current Protocols in Bioinformatics,2016,53:1.29.1.
[26]Huang Y A,You ZH,Chen X,et al.Sequence-based prediction of protein-protein interactions using weighted sparse representation model combined w ith global encoding[J].Bmc Bioinformatics,2016,17(1):184.
[2]Xing C,Yan C C,Xu Z,et al.Long non-coding RNAs and complex diseases:from experimental results to computational models[J].Briefings in Bioinformatics,2016.
[3]Guttman M,Amit I,Garber M,et al.Chromatin signature reveals over a thousand highly conserved large non-coding RNAs in mammals[J].Nature,2009,458(7235):223.
[4]Wapinski O,Chang HY.Long noncoding RNAs and human disease[J].Trends in Cell Biology,2011,21(6):354.
[5]Wilusz J E,Sunwoo H,Spector D L.Long noncoding RNAs:functional surprises from the RNA world[J].Genes&Development,2009,23(13):1494.
[6]Yu F,Zheng J,Mao Y,et al.Long non-coding RNA APTR promotes the activation of hepatic stellate cells and the progression of liver fibrosis[J].Biochemical&Biophysical Research Communications,2015,463(4):679-685.
[7]Prasanth K V,Spector D L.Eukaryotic regulatory RNAs:an answer to the‘genome complexity'conundrum[J].Genes Dev,2007,21(1):11-42.
[8]Paul S M,Mytelka D S,Dunwiddie C T,et al.How to improve R&D productivity:the pharmaceutical industry's grand challenge[J].Nature Review s Drug Discovery,2010,9(3):203.
[9]Lin C,Yang H K,Shan Z G.Application of Petri nets to bioinformatics[J].Chinese Journal of Computers,2007.
[10]Wang W,Yang S,Zhang X,et al.Drug repositioning by integrating target information through a heterogeneous netw ork model[J].Bioinformatics,2014,30(20):2923.
[11]Deng A L,Zhu Y Y,Shi B L.A Collaborative Filtering Recommendation Algorithm Based on Item Rating Prediction[J].Journal of Softw are,2003,14(9):54-65.
[12]Bellucci M,Agostini F,Masin M,et al.Predicting protein associations with long noncoding RNAs[J].Nature M ethods,2011,8(6):444-445.
[13]朱扬勇,孙婧.Recommender System:Up to Now[J].计算机科学与探索,2015,9(5):513-25.
[14]Resnick P,Iacovou N,Suchak M,et al.Group Lens:an open architecture for collaborative filtering of netnews[J].ACM Conference on Computer Supported Cooperative Work,1994.
[15]Resnick P,Varian H R.Recommender systems[J].Communications of the Acm,1997,40(3):56-58.
[16]Wu M X,Dong L S,Jie Z Y,et al.Research on Social Recommender Systems[J].Journal of Software,2015:26.
[17]Ge M,Wang M.A Bipartite Network-based Method for Prediction of Long Non-coding RNA–protein Interactions[J].基因组蛋白质组与生物信息学报,2016,14(1):62-71.
[18]Yin F,Zhao X,Zhang X,et al.Improving Accuracy and Scalability of Personal Recommendation Based on Bipartite Netw ork Projection[J].M athematical Problems in Engineering,2014,(2014-9-25).2014,2014(3).
[19]Huang Y A,Chen X,You ZH,et al.ILNCSIM:improved lncRNA functional similarity calculation model[J].Oncotarget,2016,7(18):25902-25914.
[20]Chen X,Huang Y A,Wang X S,et al.FMLNCSIM:fuzzy measure-based lncRNA functional similarity calculation model[J].Oncotarget,2016,7(29):45948-45958.
[21]Yuan J,Wu W,Xie C,et al.NPInter v2.0:an updated database of ncRNA interactions[J].Nucleic Acids Research.2014;42(Database issue):D104.
[22]Xie C,Yuan J,Li H,et al.NONCODEv4:exploring the world of long non-coding RNA genes[J].Nucleic Acids Research.2014;42(Database issue):D98.
[23]NONCODEv4:Annotation of Noncoding RNAs with Emphasis on Long Noncoding RNAs[J]:Springer New York,2016.
[24]Consortium UP.Uni Prot:a hub for protein information[J].Nucleic Acids Research,2015,43(Database issue):204-212.
[25]Pundir S,Martin MJ,O'Donovan C,et al.Uni Prot Tools[J].Current Protocols in Bioinformatics,2016,53:1.29.1.
[26]Huang Y A,You ZH,Chen X,et al.Sequence-based prediction of protein-protein interactions using weighted sparse representation model combined w ith global encoding[J].Bmc Bioinformatics,2016,17(1):184.