References
- Machlowska J , BajJ. Gastric cancer: epidemiology, risk factors, classification, genomic characteristics and treatment strategies. Int. J. Mol. Sci.21(11), 1–20 (2020).
- Qiu J , SunM, WangY, ChenB. Identification of hub genes and pathways in gastric adenocarcinoma based on bioinformatics analysis. Med. Sci. Monit.26, e920261-1–e920262-12 (2020).
- Johnston FM , BeckmanM. Updates on management of gastric cancer. Curr. Oncol. Rep.21(8), 67 (2019).
- Huang S , ChaudharyK, GarmireLX. More is better: recent progress in multi-omics data integration methods. Front. Genet.8, 84 (2017).
- Subramanian I , VermaS, KumarS, JereA, AnamikaK. Multi-omics data integration, interpretation, and its application. Bioinform. Biol. Insights14, 1177932219899051 (2020).
- Canzler S , SchorJ, BuschWet al. Prospects and challenges of multi-omics data integration in toxicology. Arch. Toxicol.94(2), 371–388 (2020).
- Chakraborty S , HosenMI, AhmedM, ShekharHU. Onco-multi-omics approach: a new frontier in cancer research. Biomed. Res. Int.2018, 9836256–9836256 (2018).
- Das T , AndrieuxG, AhmedM, ChakrabortyS. Integration of online omics-data resources for cancer research. Front. Genet.11, 578345 (2020).
- Shi XJ , WeiY, JiB. Systems biology of gastric cancer: perspectives on the omics-based diagnosis and treatment. Front. Mol. Biosci.7, 203 (2020).
- Liang X , PengP, YanshengL. A semantic model for cross-modal and multi-modal retrieval. In: Proceedings of the 3rd ACM International Conference on Multimedia Retrieval (ICMR ’13).Association for Computing Machinery, NY, USA, 175–182 (2013).
- Guo LY , WuAH, WangYX, ZhangLP, ChaiH, LiangXF. Deep learning-based ovarian cancer subtypes identification using multi-omics data. BioData Min.13(1), 10 (2020).
- Chaudhary K , PoirionOB, LuL, GarmireLX. Deep learning-based multi-omics integration robustly predicts survival in liver cancer. Clin. Cancer Res.24(6), 1248–1259 (2018).
- Lv J , WangJ, ShangX, LiuF, GuoS. Survival prediction in patients with colon adenocarcinoma via multi-omics data integration using a deep learning algorithm. Biosci. Rep.40(12), 1–12 (2020).
- Huang X , PengY, YuanM. MHTN: modal-adversarial hybrid transfer network for cross-modal retrieval. IEEE Trans. Cybern.50(3), 1047–1059 (2020).
- Zhang Y , ZhouW, WangM, TianQ, LiH. Deep relation embedding for cross-modal retrieval. IEEE Trans. Image Process.30, 617–627 (2021).
- Mandal D , ChaudhuryKN, BiswasS. Generalized semantic preserving hashing for cross-modal retrieval. IEEE Trans. Image Process.28(1), 102–112 (2019).
- Feng F , WangX, LinR. Cross-modal retrieval with correspondence autoencoder. In: Proceedings of the 22nd ACM International Conference on Multimedia (MM ’14).Association for Computing Machinery, NY, USA, 7–16 (2014).
- Vukotić V , RaymondC, GravierG. Multimodal and crossmodal representation learning from textual and visual features with bidirectional deep neural networks for video hyperlinking. In: Proceedings of the 2016 ACM Workshop on Vision and Language Integration Meets Multimedia Fusion (iV&L-MM ’16).Association for Computing Machinery, NY, USA, 37–44 (2016).
- Gao L , LiX, LiuD, WangL, YuZ. A bidirectional deep neural network for accurate silicon color design. Adv. Mater.31(51), 1905467 (2019).
- Becker N , WeftW, ToedtG, LichterP, BennerA. penalizedSVM: a R-package for feature selection SVM classification. Bioinformatics25(13), 1711–1712 (2009).
- Love MI , HuberW, AndersS. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol.15(12), 550 (2014).
- Shen L , LiuM, LiuW, CuiJ, LiC. Bioinformatics analysis of RNA sequencing data reveals multiple key genes in uterine corpus endometrial carcinoma. Oncol. Lett.15(1), 205–212 (2018).
- Grassano L , PaganaG, DapernoM, BibbonaE, GaspariniM. Asymptotic distributions of kappa statistics and their differences with many raters, many rating categories and two conditions. Biom. J.60(1), 146–154 (2018).
- Goldstein E , YeghiazaryanK, AhmadA, GiordanoFA, FrohlichH, GolubnitschajaO. Optimal multiparametric set-up modelled for best survival outcomes in palliative treatment of liver malignancies: unsupervised machine learning and 3 PM recommendations. EPMA J.11(3), 505–515 (2020).
- Zhou C , WangY, JiMH, TongJ, YangJJ, XiaH. Predicting peritoneal metastasis of gastric cancer patients based on machine learning. Cancer Control27(1), 1–8 (2020).
- Zhang L , DongD, ZhangWet al. A deep learning risk prediction model for overall survival in patients with gastric cancer: a multicenter study. Radiother. Oncol.150, 73–80 (2020).
- Zhou C , HuJ, WangYet al. A machine learning-based predictor for the identification of the recurrence of patients with gastric cancer after operation. Sci. Rep.11(1), 1571 (2021).
- Peng Y , HuangX, QiJ. Cross-media shared representation by hierarchical learning with multiple deep networks. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI ’16).AAAI Press, 3846–3853 (2016).
- Chen T , ZhangC, LiuYet al. A gastric cancer lncRNAs model for MSI and survival prediction based on support vector machine. BMC Genomics20(1), 846 (2019).
- Zhou L , LiSH, WuY, XinL. Establishment of a prognostic model of four genes in gastric cancer based on multiple data sets. Cancer Med.10(10), 3309–3322 (2021).
- Sano T , CoitDG, KimHHet al. Proposal of a new stage grouping of gastric cancer for TNM classification: International Gastric Cancer Association staging project. Gastric Cancer20(2), 217–225 (2017).
- Gao F , LiM, XiangR, ZhouX, ZhuL, ZhaiY. Expression of CLDN6 in tissues of gastric cancer patients: association with clinical pathology and prognosis. Oncol. Lett.17(5), 4621–4625 (2019).
- Kohmoto T , MasudaK, ShodaKet al. Claudin-6 is a single prognostic marker and functions as a tumor-promoting gene in a subgroup of intestinal type gastric cancer. Gastric Cancer23(3), 403–417 (2020).
- Imai A , MochizukiD, MisawaY. SALL2 is a novel prognostic methylation marker in patients with oral squamous carcinomas: associations with SALL1 and SALL3 methylation status. DNA Cell Biol.38(7), 678–687 (2019).
- Geng HT , ChengZW, CaoRJet al. Low expression of BEX1 predicts poor prognosis in patients with esophageal squamous cell cancer. Oncol. Rep.40(5), 2778–2787 (2018).
- Xu Y , SunQ, YuanFet al. RND2 attenuates apoptosis and autophagy in glioblastoma cells by targeting the p38 MAPK signalling pathway. J. Exp. Clin. Cancer Res.39(1), 174 (2020).
- Chen GY , ZhengHC. The clinicopathological and prognostic significances of DKK3 expression in cancers: a bioinformatics analysis. Cancer Biomark.23(3), 323–331 (2018).
- Park JM , KimMK, ChiCK, KimJH, LeeSH, LeeEJ. Aberrant loss of dickkopf-3 in gastric cancer: can it predict lymph node metastasis preoperatively?World J. Surg.39(4), 1018–1025 (2015).
- Zeng WJ , YangYL, LiuZZet al. Integrative analysis of DNA methylation and gene expression identify a three-gene signature for predicting prognosis in lower-grade gliomas. Cell. Physiol. Biochem.47(1), 428–439 (2018).
- Chen X , FuY, XuHet al. SOX5 predicts poor prognosis in lung adenocarcinoma and promotes tumor metastasis through epithelial–mesenchymal transition. Oncotarget9(13), 10891–10904 (2018).
- You J , ZhaoQ, FanX, WangJ. SOX5 promotes cell invasion and metastasis via activation of Twist-mediated epithelial–mesenchymal transition in gastric cancer. Onco Targets Ther.12, 2465–2476 (2019).
- Liu XP , YinXH, MengXY, YanXH, WangF, HeL. Development and validation of a 9-gene prognostic signature in patients with multiple myeloma. Front. Oncol.8, 615 (2018).
- Jeong HY , KimHJ, KimCEet al. High expression of RFX4 is associated with tumor progression and poor prognosis in patients with glioblastoma. Int. J. Neurosci.131(1), 7–14 (2021).
- Hu S , YinX, ZhangG, MengF. Identification of DNA methylation signature to predict prognosis in gastric adenocarcinoma. J. Cell. Biochem.120(7), 11708–11715 (2019).
- Liu B , ZhangY, FanYet al. Leucine-rich repeat neuronal protein-1 suppresses apoptosis of gastric cancer cells through regulation of Fas/FasL. Cancer Sci.110(7), 2145–2155 (2019).
- Weijiao Y , FuchunL, MengjieCet al. Immune infiltration and a ferroptosis-associated gene signature for predicting the prognosis of patients with endometrial cancer. Aging (Albany, NY).13(12), 16713–16732 (2021).
- Zhou H , HeY, LiL, WuC, HuG. Identification novel prognostic signatures for head and neck squamous cell carcinoma based on ceRNA network construction and immune infiltration analysis. Int. J. Med. Sci.18(5), 1297–1311 (2021).
- Lu WC , XieH, YuanC, LiJJ, LiZY, WuAH. Identification of potential biomarkers and candidate small molecule drugs in glioblastoma. Cancer Cell Int.20(1), 419 (2020).
- öjlert ÅK , HalvorsenAR, NebdalDet al. The immune microenvironment in non-small-cell lung cancer is predictive of prognosis after surgery. Mol. Oncol.13(5), 1166–1179 (2019).