Skip to content
2000
Volume 21, Issue 2
  • ISSN: 1574-8936
  • E-ISSN: 2212-392X

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.

Methods

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.

Results

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.

© 2026 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/cbio/10.2174/0115748936309582240907160359
2025-03-12
2026-02-21

  • From This Site

    /content/journals/cbio/10.2174/0115748936309582240907160359
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. WulczynE. SteinerD.F. MoranM. Interpretable survival prediction for colorectal cancer using deep learning.NPJ Digit. Med.2021417110.1038/s41746‑021‑00427‑2 33875798
     [Google Scholar]
  2. Vale-SilvaL.A. RohrK. Long-term cancer survival prediction using multimodal deep learning.Sci. Rep.20211111350510.1038/s41598‑021‑92799‑4 34188098
     [Google Scholar]
  3. LiuP LiL YuC FeiS. 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. 105-11610.5121/csit.2020.100308
     [Google Scholar]
  4. GuptaS KalaivaniS RajasundaramA AmetaGK OleiwiAK DugbakieBN Prediction performance of deep learning for colon cancer survival prediction on SEER data.Biomed Res Int2022202210.1155/2022/1467070
     [Google Scholar]
  5. TanK. HuangW. LiuX. HuJ. DongS. A multi-modal fusion framework based on multi-task correlation learning for cancer prognosis prediction.Artif. Intell. Med.2022126January10226010.1016/j.artmed.2022.102260 35346442
     [Google Scholar]
  6. ChengJ. ZhangJ. HanY. Integrative analysis of histopathological images and genomic data predicts clear cell renal cell carcinoma prognosis.Cancer Res.20177721e91e10010.1158/0008‑5472.CAN‑17‑0313 29092949
     [Google Scholar]
  7. FrancoE.F. RanaP. CruzA. Performance comparison of deep learning autoencoders for cancer subtype detection using multi-omics data. Vol. 13.Cancers2021139201310.3390/cancers13092013
     [Google Scholar]
  8. HaslamA. KimM.S. PrasadV. Overall survival for oncology drugs approved for genomic indications.Eur. J. Cancer202216017517910.1016/j.ejca.2021.10.028 34819251
     [Google Scholar]
  9. SunD. LiA. TangB. WangM. Integrating genomic data and pathological images to effectively predict breast cancer clinical outcome.Comput. Methods Programs Biomed.2018161455310.1016/j.cmpb.2018.04.008 29852967
     [Google Scholar]
  10. IdL.L. IdQ.M. WengC. IdQ.L. WangT. IdY.W. Explainable deep transfer learning model for disease risk prediction using high-dimensional genomic data.PLoS Comput. Biol.2022187e101032810.1371/journal.pcbi.1010328
     [Google Scholar]
  11. SchneiderL. Laiouar-PedariS. KuntzS. Integration of deep learning-based image analysis and genomic data in cancer pathology: A systematic review.Eur. J. Cancer2022160809110.1016/j.ejca.2021.10.007 34810047
     [Google Scholar]
  12. QiaoY. ZhaoL. LuoC. Multi-modality artificial intelligence in digital pathology.Brief. Bioinform.2022236bbac36710.1093/bib/bbac367 36124675
     [Google Scholar]
  13. MüllerD. DNA methylation-based diagnostic, prognostic, and predictive biomarkers in colorectal cancer.Biochim. Biophys. Acta Rev. Cancer202218773188722
     [Google Scholar]
  14. Galvão-LimaL.J. MoraisA.H.F. ValentimR.A.M. BarretoE.J.S.S. miRNAs as biomarkers for early cancer detection and their application in the development of new diagnostic tools.Biomed. Eng. Online20212012110.1186/s12938‑021‑00857‑9 33593374
     [Google Scholar]
  15. FanJ. LiJ. GuoS. TaoC. ZhangH. WangW. Genome-wide DNA methylation profiles of low- and high-grade adenoma reveals potential biomarkers for early detection of colorectal carcinoma.Clin. Epigenetics202012156
     [Google Scholar]
  16. KwakM.S. LeeH.H. YangJ.M. Deep convolutional neural network-based lymph node metastasis prediction for colon cancer using histopathological images.Front. Oncol.20211061980310.3389/fonc.2020.619803 33520727
     [Google Scholar]
  17. AlzubaidiL. ZhangJ. HumaidiA.J. Al-DujailiA. DuanY. Al-ShammaO. Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions.J. Big Data2021815310.1186/s40537‑021‑00444‑8
     [Google Scholar]
  18. D’AmourA HellerK MoldovanD AdlamB AlipanahiB BeutelA Underspecification presents challenges for credibility in modern machine learning.arxiv2020
     [Google Scholar]
  19. van der VeldenB.H.M. Explainable AI: Current status and future potential.Eur. Radiol.20233421187118910.1007/s00330‑023‑10121‑4 37589904
     [Google Scholar]
  20. XieN RasG van GervenM DoranD. Explainable deep learning: A field guide for the uninitiated.arxiv2020
     [Google Scholar]
  21. TanJ. UngM. ChengC. GreeneC.S. Unsupervised feature construction and knowledge extraction from genome-wide assays of breast cancer with denoising autoencoders.Pac. Symp. Biocomput.201520132143 25592575
     [Google Scholar]
  22. AmorosoN. QuartoS. La RoccaM. TangaroS. MonacoA. BellottiR. An explainability artificial intelligence approach to brain connectivity in Alzheimer’s disease.Front. Aging Neurosci.202315123806510.3389/fnagi.2023.1238065 37719873
     [Google Scholar]
  23. PhillipsPJ HahnCA FontanaPC BroniatowskiDA PrzybockiMA HahnCA Four Principles of Explainable Artificial Intelligence: Draft NISTIR 8312.National Institute of Standards and Technology Interagency or Internal Report202010.6028/NIST.IR.8312‑draft
     [Google Scholar]
  24. AbdelhafizD. YangC. AmmarR. NabaviS. Deep convolutional neural networks for mammography: Advances, challenges and applications.BMC Bioinformatics201920S11)(Suppl. 1128110.1186/s12859‑019‑2823‑4 31167642
     [Google Scholar]
  25. KitaguchiD. TakeshitaN. MatsuzakiH. IgakiT. HasegawaH. ItoM. Development and validation of a 3-dimensional convolutional neural network for automatic surgical skill assessment based on spatiotemporal video analysis.JAMA Netw. Open202148e212078610.1001/jamanetworkopen.2021.20786 34387676
     [Google Scholar]
  26. SchmauchB. RomagnoniA. PronierE. A deep learning model to predict RNA-Seq expression of tumours from whole slide images.Nat. Commun.2020111387710.1038/s41467‑020‑17678‑4 32747659
     [Google Scholar]
  27. ErtosunM.G. RubinD.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.2015201518991908 26958289
     [Google Scholar]
  28. SenaP. FioresiR. FaglioniF. LosiL. FaglioniG. RoncucciL. Deep learning techniques for detecting preneoplastic and neoplastic lesions in human colorectal histological images.Oncol. Lett.20191866101610710.3892/ol.2019.10928 31788084
     [Google Scholar]
  29. ZittM. ZittM. DNA methylation in colorectal cancer—impact on screening and therapy monitoring modalities.Dis. Markers2007231-25171
     [Google Scholar]
  30. BachS. PaulisI. SluiterN.R. Detection of colorectal cancer in urine using DNA methylation analysis.Sci. Rep.2021111236310.1038/s41598‑021‑81900‑6 33504902
     [Google Scholar]
  31. XuW. XuM. WangL. Integrative analysis of DNA methylation and gene expression identified cervical cancer-specific diagnostic biomarkers.Signal Transduct. Target. Ther.2019415510.1038/s41392‑019‑0081‑6 31871774
     [Google Scholar]
  32. LearningD. Prediction of long non-coding RNAs based on deep learning.Genes2019104273
     [Google Scholar]
  33. HeJ. WuF. HanZ. HuM. LinW. LiY. Biomarkers (mRNAs and non-coding RNAs) for the diagnosis and prognosis of colorectal cancer—from the body fluid to tissue level.Front. Oncol.202111632834
     [Google Scholar]
  34. LiuC. PapukashviliD. DongY. WangX. HuX. YangN. Identification of tumor antigens and design of mRNA vaccine for colorectal cancer based on the immune subtype.Front. Cell Dev. Biol.20229783527
     [Google Scholar]
  35. XueV.W. CheungM.T. ChanP.T. Non-invasive potential circulating mRNA markers for colorectal adenoma using targeted sequencing.Sci. Rep.2019911294310.1038/s41598‑019‑49445‑x 31506480
     [Google Scholar]
  36. HerringE. TremblayÉ. McfaddenN. KanaokaS. Multitarget stool mRNA test for detecting colorectal cancer lesions including advanced adenomas.Cancers20211361228
     [Google Scholar]
  37. FengL. LiuZ. LiC. 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. Health202241e8e1710.1016/S2589‑7500(21)00215‑6 34952679
     [Google Scholar]
  38. MazakiJ. KatsumataK. OhnoY. A novel predictive model for anastomotic leakage in colorectal cancer using auto-artificial intelligence.Anticancer Res.202141115821582510.21873/anticanres.15400 34732457
     [Google Scholar]
  39. MasumS. HopgoodA. StefanS. FlashmanK. KhanJ. 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.202213110.1007/s12672‑022‑00472‑7
     [Google Scholar]
  40. AbrahamJ.P. MageeD. CremoliniC. Clinical validation of a machine-learning-derived signature predictive of outcomes from first-line oxaliplatin-based chemotherapy in advanced colorectal cancer.Clin. Cancer Res.20212741174118310.1158/1078‑0432.CCR‑20‑3286 33293373
     [Google Scholar]
  41. BilalM. RazaS.E.A. AzamA. 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. Health2021312e763e77210.1016/S2589‑7500(21)00180‑1 34686474
     [Google Scholar]
  42. KangY. KimY.J. ParkS. Development and operation of a digital platform for sharing pathology image data.BMC Med. Inform. Decis. Mak.202121111410.1186/s12911‑021‑01466‑1 33812383
     [Google Scholar]
  43. MisumiY. NonakaK. TakeuchiM. 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.20231214484010.3390/jcm12144840 37510955
     [Google Scholar]
  44. WeiJ.W. SuriawinataA.A. VaickusL.J. Evaluation of a deep neural network for automated classification of colorectal polyps on histopathologic slides.JAMA Netw. Open202034e20339810.1001/jamanetworkopen.2020.3398 32324237
     [Google Scholar]
  45. TheodosiA. OuzounisS. KostopoulosS. 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.20213235810.1007/s00138‑021‑01184‑8
     [Google Scholar]
  46. FilipówS. Blood circulating miRNAs as cancer biomarkers for diagnosis and surgical treatment response.Front. Genet.20191016910.3389/fgene.2019.00169
     [Google Scholar]
  47. AikemuB. XueP. HongH. 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.20211059418210.3389/fonc.2020.594182 33628729
     [Google Scholar]
  48. HuoY. GuoY. WangJ. Integrating multi-modal information to detect spatial domains of spatial transcriptomics by graph attention network.J. Genet. Genomics202350972073310.1016/j.jgg.2023.06.005 37356752
     [Google Scholar]
  49. GongP. ChengL. ZhangZ. Multi-omics integration method based on attention deep learning network for biomedical data classification.Comput. Methods Programs Biomed.202323110737710.1016/j.cmpb.2023.107377 36739624
     [Google Scholar]
  50. ChaudharyK. PoirionO.B. LuL. GarmireL.X. Deep learning-based multi-omics integration robustly predicts survival in liver cancer.Clin. Cancer Res.201924612481259
     [Google Scholar]
  51. JiangA. LiuN. ZhaoR. 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.20212810.1177/10732748211051228 34632799
     [Google Scholar]
  52. WuQ.Y. LiuS.L. SunP. Establishment and clinical application value of an automatic diagnosis platform for rectal cancer T-staging based on a deep neural network.Chin. Med. J.2021134782182810.1097/CM9.0000000000001401 33797468
     [Google Scholar]
  53. DeSilvioT. AntunesJ.T. BeraK. Region-specific deep learning models for accurate segmentation of rectal structures on post-chemoradiation T2w MRI: A multi-institutional, multi-reader study.Front. Med.202310May114905610.3389/fmed.2023.1149056 37250635
     [Google Scholar]
  54. WangH. DaiC. WenY. GADRP: graph convolutional networks and autoencoders for cancer drug response prediction.Brief. Bioinform.2023241bbac50110.1093/bib/bbac501 36460622
     [Google Scholar]
  55. QuraishR.U. HirahataT. QuraishA.U. Ul QuraishS. An overview: Genetic tumor markers for early detection and current gene therapy strategies.Cancer Inform.20232211769351221150772
     [Google Scholar]
  56. AliS. AbuhmedT. El-SappaghS. Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence.Inf. Fusion202399April10180510.1016/j.inffus.2023.101805
     [Google Scholar]
  57. Key facts UNESCO’s the Ethics of Artificial Intelligence.2021Available from: https://www.unesco.org/en/artificial-intelligence/recommendation-ethics
  58. RibeiroMT SinghS GuestrinC Model-agnostic interpretability of machine learning.arXiv2016
     [Google Scholar]
  59. LarasatiR. 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. LiZ. ChenX. ZhangX. JiangR. ChenS. Latent feature extraction with a prior-based self-attention framework for spatial transcriptomics.Genome Res.202333101757177310.1101/gr.277891.123 37903634
     [Google Scholar]
  61. KangM. LeeS. LeeD. KimS. Learning cell-type-specific gene regulation mechanisms by multi-attention based deep learning with regulatory latent space.Front. Genet.202011September86910.3389/fgene.2020.00869 33133123
     [Google Scholar]
  62. YangY. ZhouM. FangQ. ShenH.B. AnnoFly: Annotating Drosophila embryonic images based on an attention-enhanced RNN model.Bioinformatics201935162834284210.1093/bioinformatics/bty1064 30601935
     [Google Scholar]
  63. ParkS. KohY. JeonH. KimH. YeoY. KangJ. Enhancing the interpretability of transcription factor binding site prediction using attention mechanism.Sci. Rep.20201011341310.1038/s41598‑020‑70218‑4 32770026
     [Google Scholar]
  64. YangM. HuangL. HuangH. Integrating convolution and self-attention improves language model of human genome for interpreting non-coding regions at base-resolution.Nucleic Acids Res.20225014e8110.1093/nar/gkac326 35536244
     [Google Scholar]
  65. GanY. HuangX. GuoW. YanC. ZouG. Predicting synergistic anticancer drug combination based on low-rank global attention mechanism and bilinear predictor.Bioinformatics20233910btad60710.1093/bioinformatics/btad607 37812255
     [Google Scholar]
  66. ChoiS.R. LeeM. Transformer architecture and attention mechanisms in genome data analysis: A comprehensive review.Biology2023127103310.3390/biology12071033 37508462
     [Google Scholar]
  67. ComesM.C. ArezzoF. CormioG. An explainable machine learning ensemble model to predict the risk of ovarian cancer in BRCA-mutated patients undergoing risk-reducing salpingo-oophorectomy.Front. Oncol.202313July118179210.3389/fonc.2023.1181792 37519818
     [Google Scholar]
  68. PrelajA. GalliE.G. MiskovicV. Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients.Front. Oncol.202312January107882210.3389/fonc.2022.1078822 36755856
     [Google Scholar]
  69. MassafraR. FanizziA. AmorosoN. Analyzing breast cancer invasive disease event classification through explainable artificial intelligence.Front. Med.202310111635410.3389/fmed.2023.1116354 36817766
     [Google Scholar]
  70. JumperJ. EvansR. PritzelA. Highly accurate protein structure prediction with AlphaFold.Nature2021596787358358910.1038/s41586‑021‑03819‑2 34265844
     [Google Scholar]
  71. PearceR. ZhangY. Deep learning techniques have significantly impacted protein structure prediction and protein design.Curr. Opin. Struct. Biol.20216819420710.1016/j.sbi.2021.01.007
     [Google Scholar]
  72. MitchellA.L. AlmeidaA. BeracocheaM. MGnify: the microbiome analysis resource in 2020.Nucleic Acids Res.202048D1D570D578 31696235
     [Google Scholar]
  73. SeniorA.W. EvansR. JumperJ. Improved protein structure prediction using potentials from deep learning.Nature2020577779270671010.1038/s41586‑019‑1923‑7 31942072
     [Google Scholar]
  74. ZuoC. DaiH. ChenL. Deep cross-omics cycle attention model for joint analysis of single-cell multi-omics data.Bioinformatics202137224091409910.1093/bioinformatics/btab403 34028557
     [Google Scholar]
  75. LiH. MaT. HaoM. Decoding functional cell-cell communication events by multi-view graph learning on spatial transcriptomics.Brief. Bioinform.2023246bbad35910.1093/bib/bbad359 37824741
     [Google Scholar]
  76. BenhammouY. AchchabB. HerreraF. TabikS. BreakHis based breast cancer automatic diagnosis using deep learning: Taxonomy, survey and insights.Neurocomputing2020375October92410.1016/j.neucom.2019.09.044
     [Google Scholar]
  77. GuoH. LvX. LiY. LiM. Attention-based GCN integrates multi-omics data for breast cancer subtype classification and patient-specific gene marker identification.Brief. Funct. Genomics202322546347410.1093/bfgp/elad013 37114942
     [Google Scholar]
  78. WangC. LyeX. KaaliaR. KumarP. RajapakseJ.C. Deep learning and multi-omics approach to predict drug responses in cancer.BMC Bioinformatics202222S10Suppl. 1063210.1186/s12859‑022‑04964‑9 36443676
     [Google Scholar]
  79. PangJ. LiangB. DingR. YanQ. ChenR. XuJ. A denoised multi-omics integration framework for cancer subtype classification and survival prediction.Brief. Bioinform.2023245bbad30410.1093/bib/bbad304 37594302
     [Google Scholar]
  80. SinghA. SenguptaS. LakshminarayananV. Explainable deep learning models in medical image analysis.J. Imaging20206652
     [Google Scholar]
  81. LeeJ. LeeY. KimJ. KosiorekA.R. ChoiS. TehY.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.202345554365447
     [Google Scholar]
  83. ZuoC. ZhangY. CaoC. FengJ. JiaoM. ChenL. Elucidating tumor heterogeneity from spatially resolved transcriptomics data by multi-view graph collaborative learning.Nat. Commun.2022131596210.1038/s41467‑022‑33619‑9 36216831
     [Google Scholar]
  84. KaczmarekE. JamzadA. ImtiazT. NanayakkaraJ. RenwickN. MousaviP. Multi-omic graph transformers for cancer classification and interpretation.Pac. Symp. Biocomput.202227373384 34890164
     [Google Scholar]
  85. HuJ. ShenL. AlbanieS. SunG. WuE. 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. JeongD. KooB. OhM. KimT.B. KimS. GOAT: Gene-level biomarker discovery from multi-Omics data using graph ATtention neural network for eosinophilic asthma subtype.Bioinformatics20233910btad58210.1093/bioinformatics/btad582 37740295
     [Google Scholar]
  87. OuyangD. LiangY. LiL. Integration of multi-omics data using adaptive graph learning and attention mechanism for patient classification and biomarker identification.Comput. Biol. Med.2023164March10730310.1016/j.compbiomed.2023.107303 37586201
     [Google Scholar]
  88. StahlschmidtS.R. UlfenborgB. SynnergrenJ. Multimodal deep learning for biomedical data fusion: A review.Brief. Bioinform.2022232bbab56910.1093/bib/bbab569 35089332
     [Google Scholar]
  89. BoehmK.M. KhosraviP. VanguriR. GaoJ. ShahS.P. Harnessing multimodal data integration to advance precision oncology.Nat. Rev. Cancer202222211412610.1038/s41568‑021‑00408‑3 34663944
     [Google Scholar]
  90. Lopez PinayaW.H. VieiraS. Garcia-DiasR. MechelliA. Autoencoders. In: Machine Learning: Methods and Applications to Brain Disorders.Academic Press2019193208
     [Google Scholar]
  91. ŠkrljB. DaeroskiS. LavračN. PetkovićM. Feature importance estimation with self-attention networks.Front Artif Intell Appl202032514911498
     [Google Scholar]
  92. AryaN SahaS Multi-modal classification for human breast cancer prognosis prediction: Proposal of deep-learning based stacked ensemble model.IEEE/ACM Trans Comput Biol Bioinform2022192103241
     [Google Scholar]
  93. OlatunjiI. CuiF. Multimodal AI for prediction of distant metastasis in carcinoma patients.Front. Bioinform.2023310.3389/fbinf.2023.1131021
     [Google Scholar]
  94. GuoW. LiangW. DengQ. ZouX. A multimodal affinity fusion network for predicting the survival of breast cancer patients.Front. Genet.20211270902710.3389/fgene.2021.709027 34490038
     [Google Scholar]
  95. ZhangY. LiA. HeJ. WangM. A novel MKL method for GBM prognosis prediction by integrating histopathological image and multi-omics data.IEEE J. Biomed. Health Inform.202024117117910.1109/JBHI.2019.2898471 30763249
     [Google Scholar]
  96. BoraK. BhuyanM.K. KasugaiK. MallikS. ZhaoZ. Computational learning of features for automated colonic polyp classification.Sci. Rep.2021111434710.1038/s41598‑021‑83788‑8 33623086
     [Google Scholar]
  97. TougeronD. FauquembergueE. RouquetteA. Tumor-infiltrating lymphocytes in colorectal cancers with microsatellite instability are correlated with the number and spectrum of frameshift mutations.Mod. Pathol.20092291186119510.1038/modpathol.2009.80 19503063
     [Google Scholar]
  98. NatrajanR. LittleS.E. Reis-FilhoJ.S. Amplification and overexpression of CACNA1E correlates with relapse in favorable histology Wilms’ tumors.Clin. Cancer Res.200612247284729310.1158/1078‑0432.CCR‑06‑1567 17189400
     [Google Scholar]
  99. HeX.S. YeW.L. ZhangY.J. Oncogenic potential of BEST4 in colorectal cancer via activation of PI3K/Akt signaling.Oncogene20224181166117710.1038/s41388‑021‑02160‑2 35058597
     [Google Scholar]
  100. PanX. WangQ. XuC. YanL. PangS. GanJ. Prognostic value of chloride channel accessory mRNA expression in colon cancer.Oncol. Lett.20191832967297610.3892/ol.2019.10615 31404307
     [Google Scholar]
  101. YangG.Z. HuL. CaiJ. Prognostic value of carbonic anhydrase VII expression in colorectal carcinoma.BMC Cancer201515120910.1186/s12885‑015‑1216‑y 25885898
     [Google Scholar]
  102. ZhangL. GongY. WangS. GaoF. Anti-colorectal cancer mechanisms of formononetin identified by network pharmacological approach.Med. Sci. Monit.2019257709771410.12659/MSM.919935 31608899
     [Google Scholar]
  103. ZhangQ. NieH. PanJ. XuH. ZhanQ. FMNL3 is overexpressed in tumor tissues and predicts an immuno-hot phenotype in pancreatic cancer.Int. J. Gen. Med.2022158285829810.2147/IJGM.S384195 36444244
     [Google Scholar]
  104. BrodskyA.S. KhuranaJ. GuoK.S. Somatic mutations in collagens are associated with a distinct tumor environment and overall survival in gastric cancer.BMC Cancer202222113910.1186/s12885‑021‑09136‑1 35120467
     [Google Scholar]
  105. YuH. QinX.K. YinK.W. EphB6 deficiency in intestinal neurons promotes tumor growth in colorectal cancer by neurotransmitter GABA signaling.Carcinogenesis2023448-968269410.1093/carcin/bgad041 37294054
     [Google Scholar]
  106. BildikG. LiangX. SuttonM.N. BastR.C. LuZ. DIRAS3: An imprinted tumor suppressor gene that regulates RAS and PI3K-driven cancer growth, motility, autophagy, and tumor dormancy.Mol. Cancer Ther.20222112537
     [Google Scholar]
  107. ZhouL. YuY. WenR. Development and validation of an 8-gene signature to improve survival prediction of colorectal cancer.Front. Oncol.20221286309410.3389/fonc.2022.863094 35619909
     [Google Scholar]
  108. HintonG VinyalsO DeanJ Distilling the knowledge in a neural network.arxiv2015
     [Google Scholar]
  109. BannerR. HubaraI. SoudryD. 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. ChmielB Ben-UriL ShkolnikM HofferE BannerR SoudryD Neural gradients are near-lognormal: Improved quantized and sparse training.arxiv2020
     [Google Scholar]
  111. GholamiA KimS DongZ YaoZ MahoneyMW KeutzerK A survey of quantization methods for efficient neural network inference.arxiv2021
     [Google Scholar]
  112. O’ NeillJ. Ver SteegG. GalstyanA. Compressing deep neural networks via layer fusion.Available from: www.aaai.org
/content/journals/cbio/10.2174/0115748936309582240907160359
Loading
Explainable Colon Cancer Stage Prediction with Multimodal Biodata through the Attention-based Transformer and Squeeze-Excitation Framework
Curr Bioinform 21, 122 (2026); https://doi.org/10.2174/0115748936309582240907160359
/content/journals/cbio/10.2174/0115748936309582240907160359
/content/journals/cbio/10.2174/0115748936309582240907160359
Loading

Data & Media loading...


  • Article Type:     Research Article
Keyword(s): attention model; explainable; features-relevance; genomics; H&E Images; Multimodal

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test