医学生信分析，2020路在何方？

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(Basel)（6.16分），如下：

纯生信友好期刊	2019接收量	影响因子
J Cell Biochem	83	3.40
J Cell Physiol	68	4.50
Front Oncol	60	4.13
Sci Rep	58	4.01
Front Genet	47	3.52
Cancers (Basel)	43	6.16
Cancer Cell Int	39	3.44
Aging (Albany NY)	37	5.52
Bioinformatics	33	4.53
J Transl Med	24	4.10
Biomed Pharmacother	22	3.74
Int J Mol Sci	22	4.18
EBioMedicine	18	6.68
J Cell Mol Med	18	4.66
Nucleic Acids Res	12	11.15
Breast Cancer Res Treat	11	3.47
Epigenomics	11	4.40
Int J Cancer	11	4.98
Brief Bioinform	10	9.10
Oncogene	10	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 Commun	11.88	6
Nucleic Acids Res	11.15	12
Brief Bioinform	9.10	10
Clin Cancer Res	8.91	7
J Immunother Cancer	8.68	7
Cancer Res	8.38	5
EBioMedicine	6.68	18
Oncogene	6.63	10
Cancers (Basel)	6.16	43
Mol Oncol	5.96	7
Cell Death Dis	5.96	6
J Clin Med	5.69	7
J Exp Clin Cancer Res	5.65	7
Aging (Albany NY)	5.52	37
Clin Epigenetics	5.50	7
Oncoimmunology	5.33	6
Cancer Epidemiol Biomarkers Prev	5.06	5
Int J Cancer	4.98	11
Cancer Immunol Immunother	4.90	6
Cancer Sci	4.75	7
Cancer Gene Ther	4.68	5
J Cell Mol Med	4.66	18
Bioinformatics	4.53	33
J Cell Physiol	4.50	68
Mol Cancer Res	4.48	5
PLoS Comput Biol	4.43	5
Epigenomics	4.40	11
Gynecol Oncol	4.39	7
Int J Mol Sci	4.18	22
Front Oncol	4.13	60
J Transl Med	4.10	24
Sci Rep	4.01	58
Carcinogenesis	4.00	7
Front Pharmacol	3.85	6
Biomed Pharmacother	3.74	22
Oral Oncol	3.73	5
Ann Transl Med	3.69	6
Int J Oncol	3.57	9
Front Genet	3.52	47
Breast Cancer Res Treat	3.47	11
Life Sci	3.45	5
Cancer Cell Int	3.44	39
World J Gastroenterol	3.41	7
Mol Carcinog	3.41	5
J Cell Biochem	3.40	83

其中大于5分的杂志里Cancers (Basel)、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
A comprehensive PDX gastric cancer collection captures  cancer cell intrinsic transcriptional MSI traits.	Cancer Res	8.378
Identification of Coding and Long Noncoding RNAs Differentially  Expressed in Tumors and Preferentially Expressed in Healthy Tissues.(泛癌)
Identifying and targeting cancer-specific metabolism with network-based  drug target prediction.(泛癌)	EBioMedicine	6.68
Pathway-based biomarker identification with crosstalk analysis for robust prognosis prediction in  hepatocellular carcinoma.
Increased glycolysis correlates with elevated immune activity in tumor immune  microenvironment.(泛癌)
Incorporation of long non-coding RNA expression profile in the 2017 ELN risk  classification can improve prognostic prediction  of acute myeloid leukemia patients.
Identification of candidate diagnostic and prognostic biomarkers for pancreatic carcinoma.
Comprehensive characterization of the rRNA metabolism-related genes in human cancer.(泛癌)	Oncogene	6.634
Histoepigenetic analysis of HPV- and tobacco-associated head and neck cancer identifies both subtype-specific and common therapeutic targets despite  divergent microenvironments.
Identification of SERPINE1 as a Regulator of Glioblastoma Cell Dispersal  with Transcriptome Profiling	Cancers (Basel)	6.16
The YAP1-NMU Axis Is Associated with  Pancreatic Cancer 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 characterize the expression subtypes of NSCLC.	Mol Oncol	5.962
Identification of lncRNAs associated with early-stage  breast cancer and their prognostic implications.
Differentially expressed autophagy-related genes are potential  prognostic and diagnostic biomarkers in clear-cell renal cell carcinoma.	Aging (Albany NY)	5.515
TPM2 as a potential predictive biomarker for atherosclerosis.非肿瘤
An eight-long non-coding RNA signature as a candidate prognostic biomarker for bladder cancer.
Identification and validation of four hub genes involved in the plaque  deterioration of atherosclerosis.
Identification of potential blood biomarkers for Parkinson's disease（非肿瘤）by gene expression and DNA  methylation 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-seq）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

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。

欢迎咨询，我们会给您提供个性化的分析方案，免费的哦。

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