医学⽣信分析，2020路在何⽅？aquarium
以下⽂章来源于科研讲坛，作者猫头鹰博⼠
科研讲坛
期刊，实验，科研⼼得——每天聊点与科研有关的事⼉
⽣信数据挖掘发表SCI，为何拒稿率越来越⾼？因为现在会⽤R等软件，画个热图、⽕⼭图、PPI ⽹络的⼈越来越多了，⼤家⼀窝蜂地每3、5天⼀篇⽂章的质量去刷，可想⽽知数据量和作图都是粗糙的，审稿⼈⾃然开始审美疲劳了。
不过⾼分的⽣信SCI不在少数其实，猫头鹰博⼠给⼤家分析了⼀下2019年⾼分期刊（影响因⼦3.5分~12分）纯⽣信⽂章的统计结果，检索了2019年全年，共发现~750篇，平均62篇/⽉。
我们按照年接收量＞10篇的标准对杂志（>3.4分）统计，如果按照接收数量排序，J Cell Biochem（3.4分）、J Cell Physiol（4.5分）、Front Oncol（4.13分）、Sci Rep（4.011）、Front Genet（3.517分）、Cancers(Bal)（6.16分），如下：
纯⽣信友好期刊2019接收量影响因⼦
J Cell Biochem83 3.40vice
J Cell Physiol68 4.50
Front Oncol60 4.13
Sci Rep58 4.01
Front Genet47 3.52
Cancers (Bal)43 6.16
Cancer Cell Int39 3.44
Aging (Albany NY)37 5.52
Bioinformatics33 4.53
J Transl Med24 4.10 Biomed Pharmacother22 3.74
Int J Mol Sci22 4.18
EBioMedicine18 6.68
J Cell Mol Med18 4.66
Nucleic Acids Res1211.15 Breast Cancer Res Treat11 3.47
Epigenomics11 4.40
Int J Cancer11 4.98
Brief Bioinform109.10
Oncogene10 6.63
如果按照影响因⼦IF⼤⼩排序，如下：
纯⽣信友好期刊影响因⼦2019接收量
Nat Commun11.886
e bookNucleic Acids Res11.1512
Brief Bioinform9.1010
Clin Cancer Res8.917
J Immunother Cancer8.687
Cancer Res8.385
EBioMedicine 6.6818
Oncogene 6.6310
Cancers (Bal) 6.1643
Mol Oncol 5.967
Cell Death Dis 5.966
ggJ Clin Med 5.697
J Exp Clin Cancer Res 5.657
Aging (Albany NY) 5.5237
Clin Epigenetics 5.507
Oncoimmunology
5.336Cancer Epidemiol Biomarkers Prev
5.065Int J Cancer
4.9811Cancer Immunol Immunother
4.906Cancer Sci 4.757Cancer Gene Ther 4.685J Cell Mol Med 4.6618Bioinformatics 4.5333J Cell Physiol 4.5068Mol Cancer Res 4.485PLoS Comput Biol 4.435Epigenomics 4.4011Gynecol Oncol 4.397Int J Mol Sci 4.1822Front Oncol 4.1360J Transl Med 4.1024Sci Rep 4.0158Carcinogenesis 4.007Front Pharmacol 3.856Biomed Pharmacother
3.7422Oral Oncol 3.735Ann Transl Med 3.696Int J Oncol 3.579Front Genet
3.5247Breast Cancer Res Treat
3.4711Life Sci 3.455Cancer Cell Int 3.4439World J Gastroenterolescape
3.417Mol Carcinog 3.415J Cell Biochem 3.4083
其中⼤于5分的杂志⾥Cancers (Bal)、Aging (Albany NY)、EBioMedicine 对纯⽣信类的⽂章最为友好的，好中⼀些。我们举例⼀些⾼分⽂章：
⽂章名
杂志
影响因⼦
elyesA comprehensive PDX gastric cancer collection captures c ancer cell intrinsic transcriptional MSI traits.
Cancer Res 8.378
Identification of Coding and Long Noncoding RNAs Differe ntially Expresd in Tumors and Preferentially Expresd i n Healthy Tissues.(泛癌)
Identifying and targeting cancer-specific metabolism with n etwork-bad drug target prediction.(泛癌)
EBioMedicine 6.68
Pathway-bad biomarker identification with crosstalk anal ysis for robust prognosis prediction in hepatocellular carci noma.
Incread glycolysis correlates with elevated immune activi ty in tumor immune microenvironment.(泛癌)
Incorporation of long non-coding RNA expression profile in the 2017 ELN risk classification can improve prognostic pr ediction of acute myeloid leukemia patients.
Identification of candidate diagnostic and prognostic bioma rkers for pancreatic carcinoma.
Comprehensive characterization of the rRNA metabolism-r elated genes in human cancer.(泛癌)
rOncogene 6.634
Histoepigenetic analysis of HPV- and tobacco-associated head and neck cancer identifies both subtype-specific and common therapeutic targets despite divergent microenviro
nments.
Identification of SERPINE1 as a Regulator of Glioblastoma Cell Dispersal with Transcriptome Profiling
abilityCancers (Bal)
6.16
The YAP1-NMU Axis Is Associated with Pancreatic Cance r Progression and Poor Outcome: Identification of a Novel
Diagnostic Biomarker and Therapeutic Target.
KRAS-Driven Lung Adenocarcinoma and B Cell Infiltration:Novel Insights for Immunotherapy.免疫浸润
Clinical Impact of RANK Signalling in Ovarian Cancer.Identification of microRNAs involved in pathways which ch aracterize the expression subtypes of NSCLC.
Mol Oncol
5.962
Identification of lncRNAs associated with early-stage brea st cancer and their prognostic implications.
Differentially expresd autophagy-related genes are pote ntial prognostic and diagnostic biomarkers in clear-cell ren al cell carcinoma.
Aging (Albany NY) 5.515
TPM2 as a potential predictive biomarker for atherosclerosi s.⾮肿瘤An eight-long non-coding RNA signature as a candidate pr ognostic biomarker for bladder cancer.
Identification and validation of four hub genes involved in t he plaque deterioration of atherosclerosis.saber什么意思
Identification of potential blood biomarkers for Parkinson's
dia （⾮肿瘤）by gene expression and DNA methylati on data integration analysis.
Clin Epigenetics 5.496
我们将⾼分⽣信SCI 模式分为以下9类：
1．泛癌研究：多肿瘤组合分析，找共享基因；
2．单疾病的多组学（转录组、DNA 甲基化、ATAC-q ）联合分析；
3．单细胞测序数据分析：聚类分析、PCA/t-SNE 降维、细胞分群、拟时分析、TCGA 数据验证的创新模式；
4．肿瘤类免疫浸润分析价值分⼦；5．转录因⼦-lncRNA 在肿瘤发⽣中的分析：6．m6A 表观遗传组在肿瘤发病中的⼤数据挖掘；等等
distinction总之，以此类推，分数和⼯作量是成正⽐的，猫头鹰博⼠相信2020年⽣信分析依然⼤有作为。
