Ash, J. T, Darnell, G., Munro, D., & Engelhardt, B. E. Joint analysis of expression levels and histological images identifies genes associated with tissue morphology. Nature Commun, 12(1), 1–12 (2021).
Blum, C., Graham, A., Yousefzadeh, M., Shrout, J., Benjamin, K., Krishna, M., Hoda, R., Hoda, R., Cole, D. J, & Garrett-Mayer, E. et al. The expression ratio of map7/b2m is prognostic for survival in patients with stage ii colon cancer. Int. J. Oncolo., 33(3), 579–584 (2008).
Catalona, W. J, & Smith, D. S. 5-year tumor recurrence rates after anatomical radical retropubic prostatectomy for prostate cancer. J Urol, 152(5), 1837–1842 (1994).
Chan, T. Y, Partin, A. W, Walsh, P. C, & Epstein, J. I. Prognostic significance of gleason score 3+ 4 versus gleason score 4+ 3 tumor at radical prostatectomy. Urology, 56(5), 823–827 (2000).
Chandran, U. R, Ma, Changqing, Dhir, R., Bisceglia, M., Lyons-Weiler, M., Liang, W., Michalopoulos, G., Becich, M., & Monzon, F. A. Gene expression profiles of prostate cancer reveal involvement of multiple molecular pathways in the metastatic process. BMC cancer, 7(1), 1–21 (2007).
Cheerla, A. & Gevaert, O. Deep learning with multimodal representation for pancancer prognosis prediction. Bioinformatics 35(14), i446–i454 (2019). DOI: 10.1093/bioinformatics/btz342
Chen, J. W, Dhahbi, J. Lung adenocarcinoma and lung squamous cell carcinoma cancer classification, biomarker identification, and gene expression analysis using overlapping feature selection methods. Sci. Rep., 11(1), 1–15 (2021).
Chitale, D., Gong, Y., Taylor, B. S., Broderick, S., Brennan, C., Somwar, R., Golas, B., Wang, Lu, M. N., & Szoke, J. et al. An integrated genomic analysis of lung cancer reveals loss of dusp4 in egfr-mutant tumors. Oncogene, 28(31), 2773–2783 (2009).
Dai, X., Cheng, H., Bai, Z. & Li, J. Breast cancer cell line classification and its relevance with breast tumor subtyping. J. Cancer 8(16), 3131 (2017). DOI: 10.7150/jca.18457
Duan, K., Sathiya Keerthi, S, & Poo, Aun N. Evaluation of simple performance measures for tuning svm hyperparameters. Neurocomputing, 51, 41–59 (2003).
Duroux, D., Van Steen, K. netanova: novel graph clustering technique with significance assessment via hierarchical anova. Briefings in Bioinform, 24(2), bbad029, (2023).
Egevad, Lars, Granfors, T, Karlberg, L, Bergh, A, & Stattin, Per. Prognostic value of the gleason score in prostate cancer. BJU international, 89(6), 538–542 (2002).
Ferwerda, J., Hainmueller, J. & Hazlett, C. J. Kernel-based regularized least squares in R (KRLS) and Stata (krls). J. Stat. Softw. 79(3), 1–26 (2017). DOI: 10.18637/jss.v079.i03
Frandsen, J., Orton, A., Shrieve, D., & Tward, J. Risk of death from prostate cancer with and without definitive local therapy when gleason pattern 5 is present: a surveillance, epidemiology, and end results analysis. Cureus, 9(7), (2017).
Garufi, A., Traversi, G., Cirone, M. & D’Orazi, G. Hipk2 role in the tumor-host interaction: impact on fibroblasts transdifferentiation caf-like. IUBMB Life 71(12), 2055–2061 (2019). DOI: 10.1002/iub.2144
Gregorich, M. et al. Individual-specific networks for prediction modelling-a scoping review of methods. BMC Med. Res. Methodol. 22(1), 1–17 (2022). DOI: 10.1186/s12874-022-01544-6
Guebila, M. B., Wang, T., Lopes-Ramos, C. M., Fanfani, V., Weighill, D., Burkholz, R., Schlauch, D., Paulson, J. N, Altenbuchinger, M., & Sonawane, A.et al. The network zoo: a multilingual package for the inference and analysis of biological networks. bioRxiv, (2022).
Harrell, F. E, Jr. with contributions from Charles Dupont, and many others. Hmisc: Harrell Miscellaneous, (2021). R package version 4.5-0.
He, K., Zhang, X., Ren, S., & Sun, J. Deep residual learning for image recognition. CoRR, abs/1512.03385, (2015).
Hengel, H. et al. Gpt2 mutations cause developmental encephalopathy with microcephaly and features of complicated hereditary spastic paraplegia. Clin. Genet. 94(3–4), 356–361 (2018). DOI: 10.1111/cge.13390
Holzinger, A., Malle, B., Saranti, A. & Pfeifer, B. Towards multi-modal causability with graph neural networks enabling information fusion for explainable ai. Inform. Fusion 71, 28–37 (2021). DOI: 10.1016/j.inffus.2021.01.008
Hu, J., Li, X., Coleman, K., Schroeder, A., Ma, N., Irwin, D. J., Lee, E. B, Shinohara, R. T., & Li, M. S. Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network. Nature methods, 18(11), 1342–1351 (2021).
Huang, H.-C., Chuang, Y.-Y., & Chen, C.-S. Affinity aggregation for spectral clustering. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, pages 773–780. IEEE, (2012).
Ilse, M., Tomczak, J., Welling, M. Attention-based deep multiple instance learning. In Jennifer Dy and Andreas Krause, editors, Proceedings of the 35th International Conference on Machine Learning, volume 80 of Proceedings of Machine Learning Research, pages 2127–2136. PMLR, 10–15 (Jul 2018).
Karatzoglou, A., Smola, A., Hornik, K. & Zeileis, A. kernlab-an s4 package for kernel methods in r. J. Stat. Softw. 11(9), 1–20 (2004). DOI: 10.18637/jss.v011.i09
Karim, Md R., Wicaksono, G., Costa, I. G., Decker, S., Beyan, O. Prognostically relevant subtypes and survival prediction for breast cancer based on multimodal genomics data. IEEE Access, 7, 133850–133864 (2019).
Kaymakcalan, H., Yarman, Y., Goc, N., Toy, F., Meral, C., Ercan-Sencicek, A. G., & Gunel, M. Novel compound heterozygous mutations in gpt2 linked to microcephaly, and intellectual developmental disability with or without spastic paraplegia. Am. J. Med. Gen. Part A, 176(2), 421–425 (2018).
Khan, Md T., Prajapati, B., Lakhina, S., Sharma, M., Prajapati, S., Chosdol, K., & Sinha, S. Identification of gender-specific molecular differences in glioblastoma (gbm) and low-grade glioma (lgg) by the analysis of large transcriptomic and epigenomic datasets. Front. Oncol., 11, (2021).
Koh, H.W.L., Fermin, D., Vogel, C., Choi, K. P., Ewing, R. M., & Choi, H. iomicspass: network-based integration of multiomics data for predictive subnetwork discovery. NPJ systems biology and applications, 5(1), 1–10 (2019).
Kuijjer, M. L, Hsieh, P.-H., Quackenbush, J., & Glass, K. lionessr: single sample network inference in r. BMC cancer, 19(1), 1–6 (2019).
Kuijjer, M. L. lionessR: Modeling networks for individual samples using LIONESS, (2022). R package version 1.0.
Marieke Lydia Kuijjer. Matthew George Tung, GuoCheng Yuan, John Quackenbush, and Kimberly Glass. Estimating sample-specific regulatory networks. Iscience 14, 226–240 (2019).
Lavery, Hugh J, & Droller, Michael J. Do gleason patterns 3 and 4 prostate cancer represent separate disease states? J. Urol., 188(5), 1667–1675 (2012).
Liberzon, A. et al. The molecular signatures database hallmark gene set collection. Cell Syst. 1(6), 417–425 (2015). DOI: 10.1016/j.cels.2015.12.004
Liu, P., Ramachandran, S., Seyed, M. A., Scharer, C. D., Laycock, N., Dalton, W. B., Williams, H., Karanam, S., Datta, M. W, Jaye, D. L et al. Sex-determining region y box 4 is a transforming oncogene in human prostate cancer cells. Cancer Res., 66(8), 4011–4019 (2006).
Liu, T. et al. A hybrid deep learning model for predicting molecular subtypes of human breast cancer using multimodal data. Irbm 43(1), 62–74 (2022). DOI: 10.1016/j.irbm.2020.12.002
Liu, X., Wang, Y., Ji, H., Aihara, K. & Chen, L. Personalized characterization of diseases using sample-specific networks. Nucleic Acids Res. 44(22), e164–e164 (2016). DOI: 10.1093/nar/gkw772
McInnes, L., Healy, J., & Melville, J. Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426, (2018).
Menche, Jörg, G., Emre, S., Amitabh, B., Patrick J, L., Matthew J., Baribaud, Frédéric, D. R, & Barabási, A. Integrating personalized gene expression profiles into predictive disease-associated gene pools. NPJ Syst. Biol. Appl., 3(1), 1–10 (2017).
Mobadersany, P., Yousefi, S., Amgad, M., Gutman, D. A, Barnholtz-Sloan, J. S, Vega, J. E., Velázquez, B., Daniel, J., & Cooper, L.A.D. Predicting cancer outcomes from histology and genomics using convolutional networks. Proceedings of the National Academy of Sciences, 115(13), E2970–E2979 (2018).
Ouyang, Q., Nakayama, T., Baytas, O., Davidson, S. M, Yang, C., Schmidt, M., Lizarraga, S. B, Mishra, S., Malak, EI-Quessny, N. S.et al. Mutations in mitochondrial enzyme gpt2 cause metabolic dysfunction and neurological disease with developmental and progressive features. Proceedings of the National Academy of Sciences, 113(38), E5598–E5607 (2016).
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. limma powers differential expression analyses for rna-sequencing and microarray studies. Nucleic acids Res., 43(7), e47–e47 (2015).
Schmauch, B. et al. A deep learning model to predict rna-seq expression of tumours from whole slide images. Nat. Commun. 11(1), 1–15 (2020). DOI: 10.1038/s41467-020-17678-4
Schneider, L., Laiouar-Pedari, S., Kuntz, S., Krieghoff-Henning, E., Hekler, A., Kather, J. N., Gaiser, T., Fröhling, S, & Brinker, T. J. Integration of deep learning-based image analysis and genomic data in cancer pathology: a systematic review. Eur J Cancer, 160, 80–91 (2022).
Sergushichev, A. An algorithm for fast preranked gene set enrichment analysis using cumulative statistic calculation. bioRxiv, (2016).
Shedden, K., Taylor, J.M.G., Enkemann, S. A, Tsao, M. S, Yeatman, T. J., Gerald, W. L., Eschrich, S., Jurisica, I., Venkatraman, S. E, Meyerson, M. et al. Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study: Director’s challenge consortium for the molecular classification of lung adenocarcinoma. Nature medicine, 14(8), 822 (2008).
Shen, R, Mo, Q., Schultz, N., Seshan, V. E., Olshen, A. B., Huse, J., Ladanyi, M, & Sander, C. Integrative subtype discovery in glioblastoma using icluster. PloS one, 7(4), e35236 (2012).
Shen, R., Olshen, A. B, & Ladanyi, M. Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis. Bioinformatics, 25(22), 2906–2912 (2009).
Speicher, N. K., Pfeifer, N. Integrating different data types by regularized unsupervised multiple kernel learning with application to cancer subtype discovery. Bioinformatics, 31(12), i268–i275 (2015).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. 102(43), 15545–15550 (2005). DOI: 10.1073/pnas.0506580102
Tran, D., Nguyen, H., Le, U., Bebis, G., Luu, H. N., & Nguyen, T. A novel method for cancer subtyping and risk prediction using consensus factor analysis. Front. Oncol., page 1052 (2020).
Wang, B., Mezlini, A., Demir, F., Fiume, M., Tu, Z., Brudno, M., Haibe-Kains, B., & Goldenberg, A. SNFtool: Similarity Network Fusion, (2021). R package version 2.3.1.
Wang, B., Mezlini, A. M., Demir, F., Fiume, M., Tu, Z., Brudno, M., Haibe-Kains, B., & Goldenberg, A. Similarity network fusion for aggregating data types on a genomic scale. Nature methods, 11(3), 333–337 (2014).
Weinstein, J. N., Collisson, E. A., Mills, G. B., Shaw, K. R., Ozenberger, B. A., Ellrott, K., Shmulevich, I., Sander, C., & Stuart, J. M. The cancer genome atlas pan-cancer analysis project. Nature Gen., 45(10), 1113–1120 (2013).
Zagars, G. K., Ayala, A. G., von Eschenbach, A. C., & Pollack, A. The prognostic importance of gleason grade in prostatic adenocarcinoma: a long-term follow-up study of 648 patients treated with radiation therapy. Int. J. Radiation Oncol. Biol. Phys., 31(2), 237–245 (1995).
Zhang, Z. et al. Hipk2 inhibits cell metastasis and improves chemosensitivity in esophageal squamous cell carcinoma. Exp. Ther. Med. 15(1), 1113–1118 (2018).
Zhong, T., Mengyun, W. & Ma, S. Examination of independent prognostic power of gene expressions and histopathological imaging features in cancer. Cancers 11(3), 361 (2019). DOI: 10.3390/cancers11030361
