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image of Explainable Colon Cancer Stage Prediction with Multimodal Biodata through the Attention-based Transformer and Squeeze-Excitation Framework

Abstract

Introduction

The heterogeneity in tumours poses significant challenges to the accurate prediction of cancer stages, necessitating the expertise of highly trained medical professionals for diagnosis. Over the past decade, the integration of deep learning into medical diagnostics, particularly for predicting cancer stages, has been hindered by the black-box nature of these algorithms, which complicates the interpretation of their decision-making processes.

Method

This study seeks to mitigate these issues by leveraging the complementary attributes found within functional genomics datasets (including mRNA, miRNA, and DNA methylation) and stained histopathology images. We introduced the Extended Squeeze- and-Excitation Multiheaded Attention (ESEMA) model, designed to harness these modalities. This model efficiently integrates and enhances the multimodal features, capturing biologically pertinent patterns that improve both the accuracy and interpretability of cancer stage predictions.

Result

Our findings demonstrate that the explainable classifier utilised the salient features of the multimodal data to achieve an area under the curve (AUC) of 0.9985, significantly surpassing the baseline AUCs of 0.8676 for images and 0.995 for genomic data.

Conclusion

Furthermore, the extracted genomics features were the most relevant for cancer stage prediction, suggesting that these identified genes are promising targets for further clinical investigation.

© 2025 Bentham Science Publishers
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/content/journals/cbio/10.2174/0115748936309582240907160359
2025-03-12
2025-04-21

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References

  1. Wulczyn E. Steiner D.F. Moran M. Plass M. Reihs R. Tan F. Flament-Auvigne I. Brown T. Regitnig P. Chen P.H.C. Hegde N. Sadhwani A. MacDonald R. Ayalew B. Corrado G.S. Peng L.H. Tse D. Müller H. Xu Z. Liu Y. Stumpe M.C. Zatloukal K. Mermel C.H. Interpretable survival prediction for colorectal cancer using deep learning. NPJ Digit. Med. 2021 4 1 71 10.1038/s41746‑021‑00427‑2 33875798
    [Google Scholar]
  2. Vale-Silva L.A. Rohr K. Long-term cancer survival prediction using multimodal deep learning. Sci. Rep. 2021 11 1 13505 10.1038/s41598‑021‑92799‑4 34188098
    [Google Scholar]
  3. Liu P Li L Yu C Fei S. Two staged prediction of gastric cancer patient’s survival via machine learning techniques. IAP, ICCIoT, CNSA, SIGML, IT, ICBB, DMDB, Nanjing, Jiangsu 210000 China, 2020, pp. 105-116. 10.5121/csit.2020.100308
    [Google Scholar]
  4. Gupta S Kalaivani S Rajasundaram A Ameta GK Oleiwi AK Dugbakie BN Prediction performance of deep learning for colon cancer survival prediction on SEER data. Biomed. Res. Int. 2022 2022 10.1155/2022/1467070
    [Google Scholar]
  5. Tan K. Huang W. Liu X. Hu J. Dong S. A multi-modal fusion framework based on multi-task correlation learning for cancer prognosis prediction. Artif. Intell. Med. 2022 126 January 102260 10.1016/j.artmed.2022.102260 35346442
    [Google Scholar]
  6. Cheng J. Zhang J. Han Y. Wang X. Ye X. Meng Y. Parwani A. Han Z. Feng Q. Huang K. Integrative analysis of histopathological images and genomic data predicts clear cell renal cell carcinoma prognosis. Cancer Res. 2017 77 21 e91 e100 10.1158/0008‑5472.CAN‑17‑0313 29092949
    [Google Scholar]
  7. Franco E.F. Rana P. Cruz A. Calderón V.V. Azevedo V. Ramos R.T.J. Ghosh P. Performance comparison of deep learning autoencoders for cancer subtype detection using multi-omics data. Vol. 13. Cancers 2021 13 9 2013 10.3390/cancers13092013
    [Google Scholar]
  8. Haslam A. Kim M.S. Prasad V. Overall survival for oncology drugs approved for genomic indications. Eur. J. Cancer 2022 160 175 179 10.1016/j.ejca.2021.10.028 34819251
    [Google Scholar]
  9. Sun D. Li A. Tang B. Wang M. Integrating genomic data and pathological images to effectively predict breast cancer clinical outcome. Comput. Methods Programs Biomed. 2018 161 45 53 10.1016/j.cmpb.2018.04.008 29852967
    [Google Scholar]
  10. Id LL Id QM Weng C Id QL Wang T Id YW Explainable deep transfer learning model for disease risk prediction using high-dimensional genomic data. PLoS Comput. Biol. 2022 18 7 e1010328 10.1371/journal.pcbi.1010328
    [Google Scholar]
  11. 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 2022 160 80 91 10.1016/j.ejca.2021.10.007 34810047
    [Google Scholar]
  12. Qiao Y. Zhao L. Luo C. Luo Y. Wu Y. Li S. Bu D. Zhao Y. Multi-modality artificial intelligence in digital pathology. Brief. Bioinform. 2022 23 6 bbac367 10.1093/bib/bbac367 36124675
    [Google Scholar]
  13. Müller D. DNA methylation-based diagnostic, prognostic, and predictive biomarkers in colorectal cancer. Biochim. Biophys. Acta Rev. Cancer 2022 1877 3 188722
    [Google Scholar]
  14. Galvão-Lima L.J. Morais A.H.F. Valentim R.A.M. Barreto E.J.S.S. miRNAs as biomarkers for early cancer detection and their application in the development of new diagnostic tools. Biomed. Eng. Online 2021 20 1 21 10.1186/s12938‑021‑00857‑9 33593374
    [Google Scholar]
  15. Fan J Li J Guo S Tao C Zhang H Wang W Genome-wide DNA methylation profiles of low- and high-grade adenoma reveals potential biomarkers for early detection of colorectal carcinoma. Clin. Epigenetics 2020 12 1 56
    [Google Scholar]
  16. Kwak M.S. Lee H.H. Yang J.M. Cha J.M. Jeon J.W. Yoon J.Y. Kim H.I. Deep convolutional neural network-based lymph node metastasis prediction for colon cancer using histopathological images. Front. Oncol. 2021 10 January 619803 10.3389/fonc.2020.619803 33520727
    [Google Scholar]
  17. Alzubaidi L Zhang J Humaidi AJ Al-Dujaili A Duan Y Al-Shamma O Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. J. Big Data 2021 8 1 53 10.1186/s40537‑021‑00444‑8
    [Google Scholar]
  18. D’Amour A. Heller K. Moldovan D. Adlam B. Alipanahi B. Beutel A. Underspecification presents challenges for credibility in modern machine learning. arxiv 2020
    [Google Scholar]
  19. van der Velden B.H.M. Explainable AI: Current status and future potential. Eur. Radiol. 2023 34 2 1187 1189 10.1007/s00330‑023‑10121‑4 37589904
    [Google Scholar]
  20. Xie N. Ras G. van Gerven M. Doran D. Explainable deep learning: A field guide for the uninitiated. arxiv 2020
    [Google Scholar]
  21. Tan J. Ung M. Cheng C. Greene C.S. Unsupervised feature construction and knowledge extraction from genome-wide assays of breast cancer with denoising autoencoders. Pac. Symp. Biocomput. 2015 20 132 143 25592575
    [Google Scholar]
  22. Amoroso N. Quarto S. La Rocca M. Tangaro S. Monaco A. Bellotti R. An explainability artificial intelligence approach to brain connectivity in Alzheimer’s disease. Front. Aging Neurosci. 2023 15 1238065 10.3389/fnagi.2023.1238065 37719873
    [Google Scholar]
  23. Phillips PJ Hahn CA Fontana PC Broniatowski DA Przybocki MA Hahn CA Four Principles of Explainable Artificial Intelligence: Draft NISTIR 8312 National Institute of Standards and Technology Interagency or Internal Report 2020 10.6028/NIST.IR.8312‑draft
    [Google Scholar]
  24. Abdelhafiz D. Yang C. Ammar R. Nabavi S. Deep convolutional neural networks for mammography: Advances, challenges and applications. BMC Bioinformatics 2019 20 S11 Suppl. 11 281 10.1186/s12859‑019‑2823‑4 31167642
    [Google Scholar]
  25. Kitaguchi D. Takeshita N. Matsuzaki H. Igaki T. Hasegawa H. Ito M. Development and validation of a 3-dimensional convolutional neural network for automatic surgical skill assessment based on spatiotemporal video analysis. JAMA Netw. Open 2021 4 8 e2120786 10.1001/jamanetworkopen.2021.20786 34387676
    [Google Scholar]
  26. Schmauch B. Romagnoni A. Pronier E. Saillard C. Maillé P. Calderaro J. Kamoun A. Sefta M. Toldo S. Zaslavskiy M. Clozel T. Moarii M. Courtiol P. Wainrib G. A deep learning model to predict RNA-Seq expression of tumours from whole slide images. Nat. Commun. 2020 11 1 3877 10.1038/s41467‑020‑17678‑4 32747659
    [Google Scholar]
  27. Ertosun M.G. Rubin D.L. Automated grading of gliomas using deep learning in digital pathology images: A modular approach with ensemble of convolutional neural networks. AMIA Annu. Symp. Proc. 2015 2015 1899 1908 26958289
    [Google Scholar]
  28. Sena P. Fioresi R. Faglioni F. Losi L. Faglioni G. Roncucci L. Deep learning techniques for detecting preneoplastic and neoplastic lesions in human colorectal histological images. Oncol. Lett. 2019 18 6 6101 6107 10.3892/ol.2019.10928 31788084
    [Google Scholar]
  29. Zitt M Zitt M. DNA methylation in colorectal cancer—impact on screening and therapy monitoring modalities. Dis. Markers 2007 23 1-2 51 71
    [Google Scholar]
  30. Bach S. Paulis I. Sluiter N.R. Tibbesma M. Martin I. van de Wiel M.A. Tuynman J.B. Bahce I. Kazemier G. Steenbergen R.D.M. Detection of colorectal cancer in urine using DNA methylation analysis. Sci. Rep. 2021 11 1 2363 10.1038/s41598‑021‑81900‑6 33504902
    [Google Scholar]
  31. Xu W. Xu M. Wang L. Zhou W. Xiang R. Shi Y. Zhang Y. Piao Y. Integrative analysis of DNA methylation and gene expression identified cervical cancer-specific diagnostic biomarkers. Signal Transduct. Target. Ther. 2019 4 1 55 10.1038/s41392‑019‑0081‑6 31871774
    [Google Scholar]
  32. Learning D. Prediction of long non-coding RNAs based on deep learning. Genes 2019 10 4 273
    [Google Scholar]
  33. He J Wu F Han Z Hu M Lin W Li Y. Biomarkers (mRNAs and non-coding RNAs) for the diagnosis and prognosis of colorectal cancer—from the body fluid to tissue level. Front. Oncol. 2021 11 632834
    [Google Scholar]
  34. Liu C Papukashvili D Dong Y Wang X Hu X Yang N Identification of tumor antigens and design of mRNA vaccine for colorectal cancer based on the immune subtype. Front. Cell Dev. Biol. 2022 9 783527
    [Google Scholar]
  35. Xue V.W. Cheung M.T. Chan P.T. Luk L.L.Y. Lee V.H. Au T.C. Yu A.C. Cho W.C.S. Tsang H.F.A. Chan A.K. Wong S.C.C. Non-invasive potential circulating mRNA markers for colorectal adenoma using targeted sequencing. Sci. Rep. 2019 9 1 12943 10.1038/s41598‑019‑49445‑x 31506480
    [Google Scholar]
  36. Herring E Tremblay É Mcfadden N Kanaoka S Multitarget stool mRNA test for detecting colorectal cancer lesions including advanced adenomas. Cancers 2021 13 6 1228
    [Google Scholar]
  37. Feng L. Liu Z. Li C. Li Z. Lou X. Shao L. Wang Y. Huang Y. Chen H. Pang X. Liu S. He F. Zheng J. Meng X. Xie P. Yang G. Ding Y. Wei M. Yun J. Hung M.C. Zhou W. Wahl D.R. Lan P. Tian J. Wan X. Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: A multicentre observational study. Lancet Digit. Health 2022 4 1 e8 e17 10.1016/S2589‑7500(21)00215‑6 34952679
    [Google Scholar]
  38. Mazaki J. Katsumata K. Ohno Y. Udo R. Tago T. Kasahara K. Kuwabara H. Enomoto M. Ishizaki T. Nagakawa Y. Tsuchida A. A novel predictive model for anastomotic leakage in colorectal cancer using auto-artificial intelligence. Anticancer Res. 2021 41 11 5821 5825 10.21873/anticanres.15400 34732457
    [Google Scholar]
  39. Masum S Hopgood A Stefan S Flashman K Khan J Data analytics and artificial intelligence in predicting length of stay, readmission, and mortality: A population-based study of surgical management of colorectal cancer. Discover Oncol. 2022 13 1 10.1007/s12672‑022‑00472‑7
    [Google Scholar]
  40. Abraham J.P. Magee D. Cremolini C. Antoniotti C. Halbert D.D. Xiu J. Stafford P. Berry D.A. Oberley M.J. Shields A.F. Marshall J.L. Salem M.E. Falcone A. Grothey A. Hall M.J. Venook A.P. Lenz H.J. Helmstetter A. Korn W.M. Spetzler D.B. Clinical validation of a machine-learning–derived signature predictive of outcomes from first-line oxaliplatin-based chemotherapy in advanced colorectal cancer. Clin. Cancer Res. 2021 27 4 1174 1183 10.1158/1078‑0432.CCR‑20‑3286 33293373
    [Google Scholar]
  41. Bilal M. Raza S.E.A. Azam A. Graham S. Ilyas M. Cree I.A. Snead D. Minhas F. Rajpoot N.M. Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: A retrospective study. Lancet Digit. Health 2021 3 12 e763 e772 10.1016/S2589‑7500(21)00180‑1 34686474
    [Google Scholar]
  42. Kang Y. Kim Y.J. Park S. Ro G. Hong C. Jang H. Cho S. Hong W.J. Kang D.U. Chun J. Lee K. Kang G.H. Moon K.C. Choe G. Lee K.S. Park J.H. Jeong W.K. Chun S.Y. Park P. Choi J. Development and operation of a digital platform for sharing pathology image data. BMC Med. Inform. Decis. Mak. 2021 21 1 114 10.1186/s12911‑021‑01466‑1 33812383
    [Google Scholar]
  43. Misumi Y. Nonaka K. Takeuchi M. Kamitani Y. Uechi Y. Watanabe M. Kishino M. Omori T. Yonezawa M. Isomoto H. Tokushige K. Comparison of the ability of artificial-intelligence-based computer-aided detection (CAD) systems and endoscopists to detect colorectal neoplastic lesions on endoscopy video. J. Clin. Med. 2023 12 14 4840 10.3390/jcm12144840 37510955
    [Google Scholar]
  44. Wei J.W. Suriawinata A.A. Vaickus L.J. Ren B. Liu X. Lisovsky M. Tomita N. Abdollahi B. Kim A.S. Snover D.C. Baron J.A. Barry E.L. Hassanpour S. Evaluation of a deep neural network for automated classification of colorectal polyps on histopathologic slides. JAMA Netw. Open 2020 3 4 e203398 10.1001/jamanetworkopen.2020.3398 32324237
    [Google Scholar]
  45. Theodosi A. Ouzounis S. Kostopoulos S. Glotsos D. Kalatzis I. Tzelepi V. Ravazoula P. Asvestas P. Cavouras D. Sakellaropoulos G. Design of a hybrid deep learning system for discriminating between low- and high-grade colorectal cancer lesions, using microscopy images of IHC stained for AIB1 expression biopsy material. Mach. Vis. Appl. 2021 32 3 58 10.1007/s00138‑021‑01184‑8
    [Google Scholar]
  46. Filipów S. Blood circulating miRNAs as cancer biomarkers for diagnosis and surgical treatment response. Front. Genet. 2019 10 169 10.3389/fgene.2019.00169
    [Google Scholar]
  47. Aikemu B. Xue P. Hong H. Jia H. Wang C. Li S. Huang L. Ding X. Zhang H. Cai G. Lu A. Xie L. Li H. Zheng M. Sun J. Artificial intelligence in decision-making for colorectal cancer treatment strategy: An observational study of implementing watson for oncology in a 250-case cohort. Front. Oncol. 2021 10 594182 10.3389/fonc.2020.594182 33628729
    [Google Scholar]
  48. Huo Y. Guo Y. Wang J. Xue H. Feng Y. Chen W. Li X. Integrating multi-modal information to detect spatial domains of spatial transcriptomics by graph attention network. J. Genet. Genomics 2023 50 9 720 733 10.1016/j.jgg.2023.06.005 37356752
    [Google Scholar]
  49. Gong P. Cheng L. Zhang Z. Meng A. Li E. Chen J. Zhang L. Multi-omics integration method based on attention deep learning network for biomedical data classification. Comput. Methods Programs Biomed. 2023 231 107377 10.1016/j.cmpb.2023.107377 36739624
    [Google Scholar]
  50. Chaudhary K. Poirion O.B. Lu L. Garmire L.X. Deep learning-based multi-omics integration robustly predicts survival in liver cancer. Clin Cancer Res. 2019 24 6 1248 1259
    [Google Scholar]
  51. Jiang A. Liu N. Zhao R. Liu S. Gao H. Wang J. Zheng X. Ren M. Fu X. Liang X. Tian T. Ruan Z. Yao Y. Construction and validation of a novel nomogram to predict the overall survival of patients with combined small cell lung cancer: A surveillance, epidemiology, and end results population-based study. Cancer Contr. 2021 28 10.1177/10732748211051228 34632799
    [Google Scholar]
  52. Wu Q.Y. Liu S.L. Sun P. Li Y. Liu G.W. Liu S.S. Hu J.L. Niu T.Y. Lu Y. Establishment and clinical application value of an automatic diagnosis platform for rectal cancer T-staging based on a deep neural network. Chin. Med. J. 2021 134 7 821 828 10.1097/CM9.0000000000001401 33797468
    [Google Scholar]
  53. DeSilvio T. Antunes J.T. Bera K. Chirra P. Le H. Liska D. Stein S.L. Marderstein E. Hall W. Paspulati R. Gollamudi J. Purysko A.S. Viswanath S.E. Region-specific deep learning models for accurate segmentation of rectal structures on post-chemoradiation T2w MRI: A multi-institutional, multi-reader study. Front. Med. 2023 10 May 1149056 10.3389/fmed.2023.1149056 37250635
    [Google Scholar]
  54. Wang H. Dai C. Wen Y. Wang X. Liu W. He S. Bo X. Peng S. GADRP: graph convolutional networks and autoencoders for cancer drug response prediction. Brief. Bioinform. 2023 24 1 bbac501 10.1093/bib/bbac501 36460622
    [Google Scholar]
  55. Quraish R ul. An overview: Genetic tumor markers for early detection and current gene therapy strategies. Cancer Inform. 2023 22 11769351221150772
    [Google Scholar]
  56. Ali S. Abuhmed T. El-Sappagh S. Muhammad K. Alonso-Moral J.M. Confalonieri R. Guidotti R. Del Ser J. Díaz-Rodríguez N. Herrera F. Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence. Inf. Fusion 2023 99 April 101805 10.1016/j.inffus.2023.101805
    [Google Scholar]
  57. Key facts UNESCO ’ s the Ethics of Artificial Intelligence. 2021 Available from: https://www.unesco.org/en/artificial-intelligence/recommendation-ethics
  58. Ribeiro MT Singh S Guestrin C Model-agnostic interpretability of machine learning. arXiv 2016
    [Google Scholar]
  59. Larasati R. Explainable AI for breast cancer diagnosis: Application and user’s understandability perception. 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET), Prague, Czech Republic, 2022, pp. 1-6.
    [Google Scholar]
  60. Li Z. Chen X. Zhang X. Jiang R. Chen S. Latent feature extraction with a prior-based self-attention framework for spatial transcriptomics. Genome Res. 2023 33 10 1757 1773 10.1101/gr.277891.123 37903634
    [Google Scholar]
  61. Kang M. Lee S. Lee D. Kim S. Learning cell-type-specific gene regulation mechanisms by multi-attention based deep learning with regulatory latent space. Front. Genet. 2020 11 September 869 10.3389/fgene.2020.00869 33133123
    [Google Scholar]
  62. Yang Y. Zhou M. Fang Q. Shen H.B. AnnoFly: Annotating Drosophila embryonic images based on an attention-enhanced RNN model. Bioinformatics 2019 35 16 2834 2842 10.1093/bioinformatics/bty1064 30601935
    [Google Scholar]
  63. Park S. Koh Y. Jeon H. Kim H. Yeo Y. Kang J. Enhancing the interpretability of transcription factor binding site prediction using attention mechanism. Sci. Rep. 2020 10 1 13413 10.1038/s41598‑020‑70218‑4 32770026
    [Google Scholar]
  64. Yang M. Huang L. Huang H. Tang H. Zhang N. Yang H. Wu J. Mu F. Integrating convolution and self-attention improves language model of human genome for interpreting non-coding regions at base-resolution. Nucleic Acids Res. 2022 50 14 e81 10.1093/nar/gkac326 35536244
    [Google Scholar]
  65. Gan Y. Huang X. Guo W. Yan C. Zou G. Predicting synergistic anticancer drug combination based on low-rank global attention mechanism and bilinear predictor. Bioinformatics 2023 39 10 btad607 10.1093/bioinformatics/btad607 37812255
    [Google Scholar]
  66. Choi S.R. Lee M. Transformer architecture and attention mechanisms in genome data analysis: A comprehensive review. Biology 2023 12 7 1033 10.3390/biology12071033 37508462
    [Google Scholar]
  67. Comes M.C. Arezzo F. Cormio G. Bove S. Calabrese A. Fanizzi A. Kardhashi A. La Forgia D. Legge F. Romagno I. Loizzi V. Massafra R. An explainable machine learning ensemble model to predict the risk of ovarian cancer in BRCA-mutated patients undergoing risk-reducing salpingo-oophorectomy. Front. Oncol. 2023 13 July 1181792 10.3389/fonc.2023.1181792 37519818
    [Google Scholar]
  68. Prelaj A. Galli E.G. Miskovic V. Pesenti M. Viscardi G. Pedica B. Mazzeo L. Bottiglieri A. Provenzano L. Spagnoletti A. Marinacci R. De Toma A. Proto C. Ferrara R. Brambilla M. Occhipinti M. Manglaviti S. Galli G. Signorelli D. Giani C. Beninato T. Pircher C.C. Rametta A. Kosta S. Zanitti M. Di Mauro M.R. Rinaldi A. Di Gregorio S. Antonia M. Garassino M.C. de Braud F.G.M. Restelli M. Lo Russo G. Ganzinelli M. Trovò F. Pedrocchi A.L.G. Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients. Front. Oncol. 2023 12 January 1078822 10.3389/fonc.2022.1078822 36755856
    [Google Scholar]
  69. Massafra R. Fanizzi A. Amoroso N. Bove S. Comes M.C. Pomarico D. Didonna V. Diotaiuti S. Galati L. Giotta F. La Forgia D. Latorre A. Lombardi A. Nardone A. Pastena M.I. Ressa C.M. Rinaldi L. Tamborra P. Zito A. Paradiso A.V. Bellotti R. Lorusso V. Analyzing breast cancer invasive disease event classification through explainable artificial intelligence. Front. Med. 2023 10 1116354 10.3389/fmed.2023.1116354 36817766
    [Google Scholar]
  70. Jumper J. Evans R. Pritzel A. Green T. Figurnov M. Ronneberger O. Tunyasuvunakool K. Bates R. Žídek A. Potapenko A. Bridgland A. Meyer C. Kohl S.A.A. Ballard A.J. Cowie A. Romera-Paredes B. Nikolov S. Jain R. Adler J. Back T. Petersen S. Reiman D. Clancy E. Zielinski M. Steinegger M. Pacholska M. Berghammer T. Bodenstein S. Silver D. Vinyals O. Senior A.W. Kavukcuoglu K. Kohli P. Hassabis D. Highly accurate protein structure prediction with AlphaFold. Nature 2021 596 7873 583 589 10.1038/s41586‑021‑03819‑2 34265844
    [Google Scholar]
  71. Pearce R. Zhang Y. Deep learning techniques have significantly impacted protein structure prediction and protein design. Curr. Opin. Struct. Biol. 2021 68 194 207 10.1016/j.sbi.2021.01.007
    [Google Scholar]
  72. Mitchell A.L. Almeida A. Beracochea M. Boland M. Burgin J. Cochrane G. Crusoe M.R. Kale V. Potter S.C. Richardson L.J. Sakharova E. Scheremetjew M. Korobeynikov A. Shlemov A. Kunyavskaya O. Lapidus A. Finn R.D. MGnify: the microbiome analysis resource in 2020. Nucleic Acids Res. 2020 48 D1 D570 D578 31696235
    [Google Scholar]
  73. Senior A.W. Evans R. Jumper J. Kirkpatrick J. Sifre L. Green T. Qin C. Žídek A. Nelson A.W.R. Bridgland A. Penedones H. Petersen S. Simonyan K. Crossan S. Kohli P. Jones D.T. Silver D. Kavukcuoglu K. Hassabis D. Improved protein structure prediction using potentials from deep learning. Nature 2020 577 7792 706 710 10.1038/s41586‑019‑1923‑7 31942072
    [Google Scholar]
  74. Zuo C. Dai H. Chen L. Deep cross-omics cycle attention model for joint analysis of single-cell multi-omics data. Bioinformatics 2021 37 22 4091 4099 10.1093/bioinformatics/btab403 34028557
    [Google Scholar]
  75. Li H. Ma T. Hao M. Guo W. Gu J. Zhang X. Wei L. Decoding functional cell–cell communication events by multi-view graph learning on spatial transcriptomics. Brief. Bioinform. 2023 24 6 bbad359 10.1093/bib/bbad359 37824741
    [Google Scholar]
  76. Benhammou Y. Achchab B. Herrera F. Tabik S. BreakHis based breast cancer automatic diagnosis using deep learning: Taxonomy, survey and insights. Neurocomputing 2020 375 October 9 24 10.1016/j.neucom.2019.09.044
    [Google Scholar]
  77. Guo H. Lv X. Li Y. Li M. Attention-based GCN integrates multi-omics data for breast cancer subtype classification and patient-specific gene marker identification. Brief. Funct. Genomics 2023 22 5 463 474 10.1093/bfgp/elad013 37114942
    [Google Scholar]
  78. Wang C. Lye X. Kaalia R. Kumar P. Rajapakse J.C. Deep learning and multi-omics approach to predict drug responses in cancer. BMC Bioinformatics 2022 22 S10 Suppl. 10 632 10.1186/s12859‑022‑04964‑9 36443676
    [Google Scholar]
  79. Pang J. Liang B. Ding R. Yan Q. Chen R. Xu J. A denoised multi-omics integration framework for cancer subtype classification and survival prediction. Brief. Bioinform. 2023 24 5 bbad304 10.1093/bib/bbad304 37594302
    [Google Scholar]
  80. Singh A Sengupta S Lakshminarayanan V Explainable deep learning models in medical image analysis. J. Imaging 2020 6 6 52
    [Google Scholar]
  81. Lee J. Lee Y. Kim J. Kosiorek A.R. Choi S. Teh Y.W. Set transformer: A framework for attention-based permutation-invariant. Neural Netw. 2018
    [Google Scholar]
  82. Guo M hao. Beyond self-attention: External attention using two linear layers for visual tasks. IEEE Trans. Pattern Anal. Mach. Intell. 2023 45 5 5436 5447
    [Google Scholar]
  83. Zuo C. Zhang Y. Cao C. Feng J. Jiao M. Chen L. Elucidating tumor heterogeneity from spatially resolved transcriptomics data by multi-view graph collaborative learning. Nat. Commun. 2022 13 1 5962 10.1038/s41467‑022‑33619‑9 36216831
    [Google Scholar]
  84. Kaczmarek E. Jamzad A. Imtiaz T. Nanayakkara J. Renwick N. Mousavi P. Multi-omic graph transformers for cancer classification and interpretation. Pac. Symp. Biocomput. 2022 27 373 384 34890164
    [Google Scholar]
  85. Hu J Shen L Albanie S Sun G Wu E. Squeeze-and-excitation networks. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, pp. 7132-7141.
    [Google Scholar]
  86. Jeong D. Koo B. Oh M. Kim T.B. Kim S. GOAT: Gene-level biomarker discovery from multi-Omics data using graph ATtention neural network for eosinophilic asthma subtype. Bioinformatics 2023 39 10 btad582 10.1093/bioinformatics/btad582 37740295
    [Google Scholar]
  87. Ouyang D. Liang Y. Li L. Ai N. Lu S. Yu M. Liu X. Xie S. Integration of multi-omics data using adaptive graph learning and attention mechanism for patient classification and biomarker identification. Comput. Biol. Med. 2023 164 March 107303 10.1016/j.compbiomed.2023.107303 37586201
    [Google Scholar]
  88. Stahlschmidt S.R. Ulfenborg B. Synnergren J. Multimodal deep learning for biomedical data fusion: A review. Brief. Bioinform. 2022 23 2 bbab569 10.1093/bib/bbab569 35089332
    [Google Scholar]
  89. Boehm K.M. Khosravi P. Vanguri R. Gao J. Shah S.P. Harnessing multimodal data integration to advance precision oncology. Nat. Rev. Cancer 2022 22 2 114 126 10.1038/s41568‑021‑00408‑3 34663944
    [Google Scholar]
  90. Lopez Pinaya WH Vieira S Garcia-Dias R Mechelli A Autoencoders. Machine Learning: Methods and Applications to Brain Disorders Academic Press 2019 193 208
    [Google Scholar]
  91. Škrlj B. Daeroski S. Lavrač N. Petković M. Feature importance estimation with self-attention networks. Front. Artif. Intell. Appl 2020 325 1491 1498
    [Google Scholar]
  92. Arya N Saha S. Multi-modal classification for human breast cancer prognosis prediction: Proposal of deep-learning based stacked ensemble model. IEEE/ACM Trans. Comput. Biol. Bioinform. 2022 19 2 1032 1041
    [Google Scholar]
  93. Olatunji I Cui F. Multimodal AI for prediction of distant metastasis in carcinoma patients. Front. Bioinformatics 2023 3 10.3389/fbinf.2023.1131021
    [Google Scholar]
  94. Guo W. Liang W. Deng Q. Zou X. A multimodal affinity fusion network for predicting the survival of breast cancer patients. Front. Genet. 2021 12 709027 10.3389/fgene.2021.709027 34490038
    [Google Scholar]
  95. Zhang Y. Li A. He J. Wang M. A novel MKL method for GBM prognosis prediction by integrating histopathological image and multi-omics data. IEEE J. Biomed. Health Inform. 2020 24 1 171 179 10.1109/JBHI.2019.2898471 30763249
    [Google Scholar]
  96. Bora K. Bhuyan M.K. Kasugai K. Mallik S. Zhao Z. Computational learning of features for automated colonic polyp classification. Sci. Rep. 2021 11 1 4347 10.1038/s41598‑021‑83788‑8 33623086
    [Google Scholar]
  97. Tougeron D. Fauquembergue E. Rouquette A. Le Pessot F. Sesboüé R. Laurent M. Berthet P. Mauillon J. Di Fiore F. Sabourin J.C. Michel P. Tosi M. Frébourg T. Latouche J.B. Tumor-infiltrating lymphocytes in colorectal cancers with microsatellite instability are correlated with the number and spectrum of frameshift mutations. Mod. Pathol. 2009 22 9 1186 1195 10.1038/modpathol.2009.80 19503063
    [Google Scholar]
  98. Natrajan R. Little S.E. Reis-Filho J.S. Hing L. Messahel B. Grundy P.E. Dome J.S. Schneider T. Vujanic G.M. Pritchard-Jones K. Jones C. Amplification and overexpression of CACNA1E correlates with relapse in favorable histology Wilms’ tumors. Clin. Cancer Res. 2006 12 24 7284 7293 10.1158/1078‑0432.CCR‑06‑1567 17189400
    [Google Scholar]
  99. He X.S. Ye W.L. Zhang Y.J. Yang X.Q. Liu F. Wang J.R. Ding X.L. Yang Y. Zhang R.N. Zhao Y.Y. Bi H.X. Guo L.C. Gan W.J. Wu H. Oncogenic potential of BEST4 in colorectal cancer via activation of PI3K/Akt signaling. Oncogene 2022 41 8 1166 1177 10.1038/s41388‑021‑02160‑2 35058597
    [Google Scholar]
  100. Pan X. Wang Q. Xu C. Yan L. Pang S. Gan J. Prognostic value of chloride channel accessory mRNA expression in colon cancer. Oncol. Lett. 2019 18 3 2967 2976 10.3892/ol.2019.10615 31404307
    [Google Scholar]
  101. Yang G.Z. Hu L. Cai J. Chen H.Y. Zhang Y. Feng D. Qi C.Y. Zhai Y.X. Gong H. Fu H. Cai Q.P. Gao C.F. Prognostic value of carbonic anhydrase VII expression in colorectal carcinoma. BMC Cancer 2015 15 1 209 10.1186/s12885‑015‑1216‑y 25885898
    [Google Scholar]
  102. Zhang L. Gong Y. Wang S. Gao F. Anti-colorectal cancer mechanisms of formononetin identified by network pharmacological approach. Med. Sci. Monit. 2019 25 7709 7714 10.12659/MSM.919935 31608899
    [Google Scholar]
  103. Zhang Q. Nie H. Pan J. Xu H. Zhan Q. FMNL3 is overexpressed in tumor tissues and predicts an immuno-hot phenotype in pancreatic cancer. Int. J. Gen. Med. 2022 15 8285 8298 10.2147/IJGM.S384195 36444244
    [Google Scholar]
  104. Brodsky A.S. Khurana J. Guo K.S. Wu E.Y. Yang D. Siddique A.S. Wong I.Y. Gamsiz Uzun E.D. Resnick M.B. Somatic mutations in collagens are associated with a distinct tumor environment and overall survival in gastric cancer. BMC Cancer 2022 22 1 139 10.1186/s12885‑021‑09136‑1 35120467
    [Google Scholar]
  105. Yu H. Qin X.K. Yin K.W. Li Z.M. Ni E.D. Yang J.M. Liu X.H. Zhou A.J. Li S.J. Gao T.M. Li Y. Li J.M. EphB6 deficiency in intestinal neurons promotes tumor growth in colorectal cancer by neurotransmitter GABA signaling. Carcinogenesis 2023 44 8-9 682 694 10.1093/carcin/bgad041 37294054
    [Google Scholar]
  106. Bildik G. Liang X. Sutton M.N. Bast R.C. Lu Z. DIRAS3: An imprinted tumor suppressor gene that regulates RAS and PI3K-driven cancer growth, motility, autophagy, and tumor dormancy. Mol. Cancer Ther. 2022 21 1 25 37
    [Google Scholar]
  107. Zhou L. Yu Y. Wen R. Zheng K. Jiang S. Zhu X. Sui J. Gong H. Lou Z. Hao L. Yu G. Zhang W. Development and validation of an 8-gene signature to improve survival prediction of colorectal cancer. Front. Oncol. 2022 12 863094 10.3389/fonc.2022.863094 35619909
    [Google Scholar]
  108. Hinton G. Vinyals O. Dean J. Distilling the knowledge in a neural network. arxiv 2015
    [Google Scholar]
  109. Banner R Hubara I Soudry D. Scalable methods for 8-bit training of neural networks. Proceedings of the 32nd International Conference on Neural Information Processing Systems, Red Hook, NY, USA, 2018, pp. 5151–5159.
    [Google Scholar]
  110. Chmiel B. Ben-Uri L. Shkolnik M. Hoffer E. Banner R. Soudry D. Neural gradients are near-lognormal: Improved quantized and sparse training. arxiv 2020
    [Google Scholar]
  111. Gholami A. Kim S. Dong Z. Yao Z. Mahoney M.W. Keutzer K. A survey of quantization methods for efficient neural network inference. arxiv 2021
    [Google Scholar]
  112. O’ Neill J. Ver Steeg G. Galstyan A. Compressing deep neural networks via layer fusion. Available from: www.aaai.org
/content/journals/cbio/10.2174/0115748936309582240907160359
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Explainable Colon Cancer Stage Prediction with Multimodal Biodata through the Attention-based Transformer and Squeeze-Excitation Framework
Bentham Science Publishers ; https://doi.org/10.2174/0115748936309582240907160359
/content/journals/cbio/10.2174/0115748936309582240907160359
/content/journals/cbio/10.2174/0115748936309582240907160359
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  • Article Type:     Research Article
Keywords: genomics ; Explainable ; Multimodal ; Attention model ; features-relevance ; H&E Images

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