GOSemSim是我写的第一个R包，我以前是用perl的，写这个R包，可以说也是我开始使用R的起点，我最初通过不断重写GOSemSim的代码来提高自己的姿势水平，比如学S4的时候，我把它用S4重写，不过后来我又改回去了，因为我觉得不用户不友好，然而那个S4的版本，被我写进了《功能蛋白质研究》一书中，真是太囧了。后来我学Rcpp，又把基于信念含量的几个算法用C++重写了一遍。所以说这个包，我是很有感情的。

在本公众号里，我之前只写过一篇文章《[GOSemSim]: 跨物种计算基因相似性》，然而大家对这个包应该还有点印象，没错！被抄袭的就是这个。

这里有必要再广而告之，这个抄袭系列，见过不要脸的，没见过这么不要脸的，连裤叉都不要了。

• [连载1]：学术期刊的学术不端，你见过吗？

• [连载2]：Editor你的心不会痛吗？

• [连载3]：辣眼睛，一篇抄袭引发的系列血案！

• [连载4]：我像是一颗棋，进退任由你决定

• 从业超过10年，未见过如此厚颜无耻之人

• 活久见，抄袭竟是重用他人代码没有致谢？

• 祝你们缠缠绵绵到天涯

• 大学教授力挺抄袭，强行洗地到裤衩都不要了，到底图什么？

• 关于重用代码与文章抄袭

• 虐狗节来看一下这个裤衩都不要的故事

这是我引用第二大的文章，目前已经超过300篇引用，而且做为一个基础性的工具，被多个R包所依赖，clusterProfiler中去GO冗余的功能也是应用了GOSemSim。

• CRAN packages

• BiSEp: Toolkit to Identify Candidate Synthetic Lethality

• LANDD: Liquid Association for Network Dynamics Detection

• ppiPre: Predict Protein-Protein Interactions Based on Functional and Topological Similarities”

• Bioconductor packages

• clusterProfiler: statistical analysis and visualization of functional profiles for genes and gene clusters

• DOSE: Disease Ontology Semantic and Enrichment analysis

• meshes: MeSH Enrichment and Semantic analyses

• Rcpi: Molecular Informatics Toolkit for Compound-Protein Interaction in Drug Discovery

• tRanslatome: Comparison between multiple levels of gene expression”

同时也被广泛应用于生信分析的方方面面，下面这个列表是部分的引用文章，从中可以看出这个包被应用的广泛性，其实还有很多种应用场景没被挖掘出来的。

Disease or Drug analysis

• Regulatory T Cells Orchestrate Similar Immune Evasion of Fetuses and Tumors in Mice. The Journal of Immunology. 2016, 196(2):678-690.

• DOSE: an R/Bioconductor package for disease ontology semantic and enrichment analysis. Bioinformatics. 2015, 31(4):608-609.

• TFmiR: a web server for constructing and analyzing disease-specific transcription factor and miRNA co-regulatory networks. Nucleic Acids Research. 2015, 43(W1):W283-W288.

• Human Monogenic Disease Genes Have Frequently Functionally Redundant Paralogs. PLoS Computational Biology. 2013, 9(5):e1003073. 48 30915 48 14985 0 0 968 0 0:00:31 0:00:15 0:00:16 3004 48 30915 48 14985 0 0 916 0 0:00:33 0:00:16 0:00:17 3079p>

• Flexible model-based clustering of mixed binary and continuous data: application to genetic regulation and cancer. Nucleic Acids Research. 2017

Gene/Protein functional analysis

• Network-driven plasma proteomics expose molecular changes in the Alzheimer’s brain. Molecular Neurodegeneration. 2016, 11:31.

• Single-Cell Co-expression Analysis Reveals Distinct Functional Modules, Co-regulation Mechanisms and Clinical Outcomes. PLOS Computational Biology. 2016, 12(4):e1004892.

• Crosstalk of dynamic functional modules in lung development of rhesus macaques. Mol. Biosyst.. 2016, 12:1342-1349.

• Comparative transcriptomics reveals the conserved building blocks involved in parallel evolution of diverse phenotypic traits in ants. Genome Biology. 2016, 17:43.

• protr/ProtrWeb: R package and web server for generating various numerical representation schemes of protein sequences. Bioinformatics. 2015, 31(11):1857-1859.

• tRanslatome: an R/Bioconductor package to portray translational control. Bioinformatics. 2014, 30(2):289-291.

• EvoCor: a platform for predicting functionally related genes using phylogenetic and expression profiles. Nucleic Acids Research. 2014, 42(W1):W72-W75.

• Genome-wide activity of unliganded estrogen receptor-α in breast cancer cells. Proc Natl Acad Sci. 2014, 111(13):4892-4897.

• Constitutively Elevated Salicylic Acid Levels Alter Photosynthesis and Oxidative State but Not Growth in Transgenic Populus. The Plant Cell. 2013, 25(7):2714-2730.

Expression data processing

• Robust Detection of Outlier Samples and Genes in Expression Datasets. Journal of Proteomics & Bioinformatics. 2016, 9:38-48.

• Missing value imputation for microRNA expression data by using a GO-based similarity measure. BMC Bioinformatics. 2016, 17:10.

Interactions

Genetic interaction

• Inferring modulators of genetic interactions with epistatic nested effects models.  PLoS Comput Biol.. 2017, 13(4):e1005496

Protein-Protein Interaction

• Critical assessment and performance improvement of plant–pathogen protein–protein interaction prediction methods.
  Briefings in Bioinformatics. 2017.

• Analyzing and interpreting genome data at the network level with ConsensusPathDB. Nature Protocols. 2016, 11:1889-1907.

• Integration of multiple biological features yields high confidence human protein interactome. Journal of Theoretical Biology. 2016, 403:85-96.

• Computational prediction of virus–human protein–protein interactions using embedding kernelized heterogeneous data. Mol. BioSyst. 2016, 12:1976-1986.

• Computational probing protein–protein interactions targeting small molecules. Bioinformatics. 2016, 32(2):226-234.

• An integrative C. elegans protein–protein interaction network with reliability assessment based on a probabilistic graphical model. Mol. Biosyst. 2016, 12:85-92.

• A Highly Efficient Approach to Protein Interactome Mapping Based on Collaborative Filtering Framework. Scientific Reports. 5:7702.

• Deciphering Signaling Pathway Networks to Understand the Molecular Mechanisms of Metformin Action. PLoS Comput Biol. 2015, 11(6):e1004202.

• A novel link prediction algorithm for reconstructing protein–protein interaction networks by topological similarity. Bioinformatics. 2013, 29(3):355-364.

• Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding. Bioinformatics. 2013, 29(13):i199-i209.

• IntScore: a web tool for confidence scoring of biological interactions. Nucl. Acids Res.. 2012, 40(W1):W140-W146.

miRNA-mRNA Interaction

• miR-17∼92 family clusters control iNKT cell ontogenesis via modulation of TGF-β signaling. PNAS. 2016.

• Identifying Functional cancer-specific miRNA-mRNA interactions in testicular germ cell tumor. Journal of Theoretical Biology. 2016, 404:82-96.

• miR2GO: comparative functional analysis for microRNAs. Bioinformatics. 2015, 31(14):2403-2405.

• Uncovering MicroRNA and Transcription Factor Mediated Regulatory Networks in Glioblastoma. PLoS Comput Biol. 2012, 8(7):e1002488.

• myMIR: a genome-wide microRNA targets identification and annotation tool. Briefings in Bioinformatics. 2011, 12(6):588-600.

• Functional similarity analysis of human virus-encoded miRNAs. Journal of Clinical Bioinformatics. 2011, 1:15.

Cellular localization

• Hum-mPLoc 3.0: Prediction enhancement of human protein subcellular localization through modeling the hidden correlations of gene ontology and functional domain features. Bioinformatics. 2017.

Motif analysis

• Comparative pan-cancer DNA methylation analysis reveals cancer common and specific patterns. Brief Bioinform, 2016

non-coding RNA

• Global and cell-type specific properties of lincRNAs with ribosome occupancy. Nucl. Acids Res. 2016.

• Advantages of mixing bioinformatics and visualization approaches for analyzing sRNA-mediated regulatory bacterial networks. Briefings In Bioinformatics. 2015, 16(5):795-805.

Semantic Similarity analysis

• A-DaGO-Fun: an adaptable Gene Ontology semantic similarity-based functional analysis tool. Bioinformatics. 2016, 32(3):477-479.

• The semantic measures library and toolkit: fast computation of semantic similarity and relatedness using biomedical ontologies. Bioinformatics. 2014, 30(5):740-742.

• Semantic similarity analysis of protein data: assessment with biological features and issues. Briefings in Bioinformatics. 2012, 13(5):569-585.

Reducing GO term redundancy

• EGFR feedback-inhibition by Ran-binding protein 6 is disrupted in cancer
  Nature Communications.
  +
  Intracerebroventricular delivery of hematopoietic progenitors results in rapid and robust engraftment of microglia-like cells.
  Science Advances. 2017, 3(12):e1701211.

Evolution

• Venus flytrap carnivorous lifestyle builds on herbivore defense strategies. Cold Spring Harbor Laboratory Press. 2016, 26:812-825.

• FunTree: advances in a resource for exploring and contextualising protein function evolution, Nucl. Acids Res.. 2016, 44(D1):D317-D323.

• Evolutionary rate covariation reveals shared functionality and coexpression of genes. Genome Research. 2012, 22:714-720.