Dimitriou, N., Arandjelović, O., Harrison, D. J. & Caie, P. D. A principled machine learning framework improves accuracy of stage II colorectal cancer prognosis. NPJ Digit. Med. 1(1), 52 (2018).
Xu, Y., Ju, L., Tong, J., Zhou, C.-M. & Yang, J.-J. Machine learning algorithms for predicting the recurrence of stage IV colorectal cancer after tumor resection. Sci. Rep. 10(1), 2519 (2020).
Xi, Y. & Xu, P. International colorectal cancer burden in 2020 and projections to 2040. Transl. Oncol. 14(10), 101174 (2021).
Rawla, P., Sunkara, T. & Barsouk, A. Epidemiology of colorectal cancer: incidence, mortality, survival, and threat elements. Gastroenterol. Rev./Prz. Gastroenterol.. 14(2), 89–103 (2019).
Wan, N. et al. Machine learning permits detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA. BMC Cancer. 19, 1–10 (2019).
Takamatsu, M. et al. Prediction of early colorectal cancer metastasis by machine learning utilizing digital slide photographs. Comput. Strategies Progr. Biomed. 178, 155–161 (2019).
Prosnitz, R. G. et al. High quality measures for the use of adjuvant chemotherapy and radiation remedy in sufferers with colorectal cancer: a scientific overview. Cancer 107(10), 2352–2360 (2006).
Siegel, R. et al. Preoperative short-course radiotherapy versus mixed radiochemotherapy in regionally superior rectal cancer: A multi-centre prospectively randomised examine of the Berlin Cancer Society. BMC Cancer 9, 1–6 (2009).
Almarzouki, H. Z. Deep-learning-based cancer profiles classification utilizing gene expression knowledge profile. J. Healthc. Eng. 2022(1), 4715998 (2022).
Mostavi, M., Chiu, Y.-C., Huang, Y. & Chen, Y. Convolutional neural community fashions for cancer kind prediction primarily based on gene expression. BMC Med. Genom. 13, 1–13 (2020).
Du, M. et al. Built-in multi-omics approach to distinct molecular characterization and classification of early-onset colorectal cancer. Cell Rep. Med. 4(3), 100974 (2023).
Haj-Hassan, H. et al. Classifications of multispectral colorectal cancer tissues utilizing convolution neural community. J. Pathol. Inform. 8(1), 1 (2017).
Pal, A., Garain, U., Chandra, A., Chatterjee, R. & Senapati, S. Psoriasis pores and skin biopsy picture segmentation utilizing deep convolutional neural community. Comput. Strategies Progr. Biomed. 159, 59–69 (2018).
George, Okay., Faziludeen, S. & Sankaran, P. Breast cancer detection from biopsy photographs utilizing nucleus guided switch learning and perception primarily based fusion. Comput. Biol. Med. 124, 103954 (2020).
Srivastava, G., Chauhan, A. & Pradhan, N. Cjt-deo: Condorcet’s jury theorem and differential evolution optimization primarily based ensemble of deep neural networks for pulmonary and colorectal cancer classification. Appl. Mushy Comput. 132, 109872 (2023).
Bychkov, D. et al. Deep learning primarily based tissue evaluation predicts final result in colorectal cancer. Sci. Rep. 8(1), 3395 (2018).
Li, J., Wang, P., Zhou, Y., Liang, H. & Luan, Okay. Completely different machine learning and deep learning strategies for the classification of colorectal cancer lymph node metastasis photographs. Entrance. Bioeng. Biotechnol. 8, 620257 (2021).
Nazari, E., Aghemiri, M., Avan, A., Mehrabian, A. & Tabesh, H. Machine learning approaches for classification of colorectal cancer with and with out function choice technique on microarray knowledge. Gene Rep. 25, 101419 (2021).
Escorcia-Gutierrez, J. et al. Galactic swarm optimization with deep switch learning pushed colorectal cancer classification for picture guided intervention. Comput. Electr. Eng. 104, 108462 (2022).
Gao, Y., Zhu, Z. & Solar, F. Growing prediction efficiency of colorectal cancer illness standing utilizing random forests classification primarily based on metagenomic shotgun sequencing knowledge. Synth. Syst. biotechnol. 7(1), 574–585 (2022).
Li, Z., Solar, Y., An, F., Chen, H. & Liao, J. Self-supervised clustering evaluation of colorectal cancer biomarkers primarily based on multi-scale complete slides picture and mass spectrometry imaging fused photographs. Talanta. 263, 124727 (2023).
Xu, H. et al. Classification of colorectal cancer consensus molecular subtypes utilizing attention-based multi-instance learning community on whole-slide photographs. Acta Histochem. 125(6), 152057 (2023).
Zhou, C. et al. Histopathology classification and localization of colorectal cancer utilizing international labels by weakly supervised deep learning. Comput. Med. Imaging Gr. 88, 101861 (2021).
Lo, C.-M. et al. Modeling the survival of colorectal cancer sufferers primarily based on colonoscopic options in a function ensemble imaginative and prescient transformer. Comput. Med. Imaging Gr. 107, 102242 (2023).
Schirris, Y., Gavves, E., Nederlof, I., Horlings, H. M. & Teuwen, J. DeepSMILE: Contrastive self-supervised pre-training advantages MSI and HRD classification straight from H&E whole-slide photographs in colorectal and breast cancer. Med. Picture Anal. 79, 102464 (2022).
Raghav, S. et al. A hierarchical clustering approach for colorectal cancer molecular subtypes identification from gene expression knowledge. Intell. Med. 4(1), 43–51 (2024).
Chang, X. et al. Predicting colorectal cancer microsatellite instability with a self-attention-enabled convolutional neural community. Cell Rep. Med. 4(2), 100914 (2023).
Luo, R. & Bocklitz, T. A scientific examine of switch learning for colorectal cancer detection. Inform. Med. Unlocked. 40, 101292 (2023).
Kumar, A., Vishwakarma, A. & Bajaj, V. Crccn-net: Automated framework for classification of colorectal tissue utilizing histopathological photographs. Biomed. Sign Course of. Management. 79, 104172 (2023).
Arowolo, M. O., Aigbogun, H. E., Michael, P. E., Adebiyi, M. O. & Tyagi, A. Okay. A predictive mannequin for classifying colorectal cancer utilizing principal element evaluation 205–216 (Elsevier, 2023).
Parhami, P., Fateh, M., Rezvani, M. & Alinejad-Rokny, H. A comparability of deep neural community fashions for cluster cancer sufferers by way of somatic level mutations. J. Ambient Intell. Humaniz. Comput. 14(8), 10883–10898 (2023).
Kim, S.-H., Koh, H. M. & Lee, B.-D. Classification of colorectal cancer in histological photographs utilizing deep neural networks: An investigation. Multimed. Instruments Appl. 80(28), 35941–35953 (2021).
Su, Y. et al. Colon cancer analysis and staging classification primarily based on machine learning and bioinformatics evaluation. Comput. Boil. Med. 145, 105409 (2022).
Wang, T. et al. scMultiGAN: Cell-specific imputation for single-cell transcriptomes with a number of deep generative adversarial networks. Temporary. Bioinform. https://doi.org/10.1093/bib/bbad384 (2023).
Wang, T. et al. Precisely deciphering spatial domains for spatially resolved transcriptomics with stCluster. Temporary. Bioinform. https://doi.org/10.1093/bioinformatics/btac837 (2024).
Wang, T. et al. DFinder: A novel end-to-end graph embedding-based technique to establish drug–meals interactions. Bioinformatics https://doi.org/10.1093/bioinformatics/btac837 (2023).
Wang, T. et al. Enhancing discoveries of molecular QTL research with small pattern dimension utilizing abstract statistic imputation. Temporary. Bioinform. https://doi.org/10.1093/bib/bbab370 (2022).
Wang, T. et al. Exploring causal results of sarcopenia on threat and development of Parkinson illness by Mendelian randomization. npj Parkinson’s Dis. 10(1), 164 (2024).
Wang, T. et al. PostGWAS: An online server for deciphering the causality put up the genome-wide affiliation research. Comput. Biol. Med. 171, 108108 (2024).
VOSviewer model 1.6.20. (Leiden College, 2024).
Kotsiantis, S. B. Resolution timber: A latest overview. Artif Intell. Rev. 39, 261–283 (2013).
Yeon, Y.-Okay., Han, J.-G. & Ryu, Okay. H. Landslide susceptibility mapping in Injae, Korea, utilizing a choice tree. Eng. Geol. 116(3–4), 274–283 (2010).
Mitchell, T. M. & Mitchell, T. M. Machine learning (McGraw-hill, New York, 1997).
Freund, Y. & Schapire, R. E. A call-theoretic generalization of on-line learning and an utility to boosting. J. comput. Syst. Sci. 55(1), 119–139 (1997).
Alizamir, M. et al. Growing an efficient explainable artificial intelligence approach for correct reverse osmosis desalination plant efficiency prediction: Utility of SHAP evaluation. Eng. Appl. Comput. Fluid Mech. 18(1), 2422060 (2024).
Hastie, T., Tibshirani, R., Friedman, J. & Franklin, J. The parts of statistical learning: knowledge mining, inference and prediction. Math. Intell. 27(2), 83–85 (2005).
Breiman, L. Random forests. Mach. be taught. 45, 5–32 (2001).
Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J., Chica-Olmo, M. & Rigol-Sanchez, J. P. An evaluation of the effectiveness of a random forest classifier for land-cover classification. ISPRS J. Photogram. Distant Sensing. 67, 93–104 (2012).
Alizamir, M., Gholampour, A., Kim, S., Keshtegar, B. & Jung, W.-t. Designing a dependable machine learning system for precisely estimating the last word situation of FRP-confined concrete. Sci. Rep. 14(1), 20466 (2024).
Dang, V.-H., Dieu, T. B., Tran, X.-L. & Hoang, N.-D. Enhancing the accuracy of rainfall-induced landslide prediction alongside mountain roads with a GIS-based random forest classifier. Bull. Eng. Geol. Environ. 78, 2835–2849 (2019).
Alizamir, M., Heddam, S., Kim, S. & Mehr, A. D. On the implementation of a novel data-intelligence mannequin primarily based on excessive learning machine optimized by bat algorithm for estimating each day chlorophyll-a focus: Case research of river and lake in USA. J. Clear. Prod. 285, 124868 (2021).
Alizamir, M. et al. Modelling each day soil temperature by hydro-meteorological knowledge at completely different depths utilizing a novel data-intelligence mannequin: Deep echo state community mannequin. Artif. Intell. Rev. 54, 2863–2890 (2021).
Friedman, J. H. Grasping perform approximation: A gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001).
Bentéjac, C., Csörgő, A. & Martínez-Muñoz, G. A comparative evaluation of gradient boosting algorithms. Artif. Intell. Rev. 54, 1937–1967 (2021).
Alizamir, M., Kim, S., Kisi, O. & Zounemat-Kermani, M. A comparative examine of a number of machine learning primarily based non-linear regression strategies in estimating photo voltaic radiation: Case research of the USA and Turkey areas. Power. 197, 117239 (2020).
Alizamir, M., Kisi, O., Kim, S. & Heddam, S. A novel technique for lake degree prediction: Deep echo state community. Arab. J. Geosci. 13, 1–18 (2020).
Alizamir, M. et al. Bettering the accuracy of each day photo voltaic radiation prediction by climatic knowledge utilizing an efficient hybrid deep learning mannequin: Lengthy short-term reminiscence (LSTM) community coupled with wavelet rework. Eng. Appl. Artif. Intell. 123, 106199 (2023).
Alizamir, M. et al. Prediction of each day chlorophyll-a focus in rivers by water high quality parameters utilizing an efficient data-driven mannequin: On-line sequential excessive learning machine. Acta Geophys. 69, 2339–2361 (2021).
Kisi, O. & Alizamir, M. Modelling reference evapotranspiration utilizing a brand new wavelet conjunction heuristic technique: Wavelet excessive learning machine vs wavelet neural networks. Agric. For. Meteorol. 263, 41–48 (2018).
Alizamir, M. et al. Investigating landfill leachate and groundwater high quality prediction utilizing a strong built-in artificial intelligence mannequin: Gray wolf metaheuristic optimization algorithm and excessive learning machine. Water. 15(13), 2453 (2023).
Heidari, E., Sobati, M. A. & Movahedirad, S. Correct prediction of nanofluid viscosity utilizing a multilayer perceptron artificial neural community (MLP-ANN). Chemom. Intel. Lab. syst. 155, 73–85 (2016).
LeCun, Y., Touresky, D., Hinton, G., Sejnowski, T. (ed.) A theoretical framework for back-propagation. In Proceedings of the 1988 connectionist fashions summer time faculty (1988).
LeCun, Y. & Bengio, Y. Convolutional networks for photographs, speech, and time sequence. Handb. Mind Concept Neur. Netw. 3361(10), 1995 (1995).
Thenmozhi, Okay. & Reddy, U. S. Crop pest classification primarily based on deep convolutional neural community and switch learning. Comput. Electron. Agric. 164, 104906 (2019).
Agostini, M. et al. A purposeful organic community centered on XRCC3: A brand new potential marker of chemoradiotherapy resistance in rectal cancer sufferers. Cancer Biol. ther. 16(8), 1160–1171 (2015).
Boyle, P. & Langman, M. J. ABC of colorectal cancer: Epidemiology. Bmj 321((Suppl S6)), 0012452 (2000).
Parkin, D. M. Worldwide variation. Oncogene. 23(38), 6329–6340 (2004).
Moradi, A. et al. Survival of colorectal cancer in Iran. Asian Pac J. Cancer Prev. 10(4), 583–586 (2009).
Ai, D. et al. Utilizing resolution tree aggregation with random forest mannequin to establish intestine microbes related to colorectal cancer. Genes. 10(2), 112 (2019).
Cai, D. et al. A metabolism-related radiomics signature for predicting the prognosis of colorectal cancer. Entrance. Mol. Biosci. 7, 613918 (2021).
Ansari, A.A., Iqbal, A., Sahoo, B. (eds.) Heterogeneous defect prediction utilizing ensemble learning method. In Artificial intelligence and evolutionary computations in engineering techniques (Springer, 2020).
Abbas, A., Abdelsamea, M. M. & Gaber, M. M. Detrac: Switch learning of class decomposed medical photographs in convolutional neural networks. IEEE Entry. 8, 74901–74913 (2020).
Azzawi, H., Hou, J., Xiang, Y. & Alanni, R. Lung cancer prediction from microarray knowledge by gene expression programming. IET Syst. Biol. 10(5), 168–178 (2016).
Cowl, T. M. Parts of data idea (John Wiley & Sons, 1999).
Kraskov, A., Stögbauer, H. & Grassberger, P. Estimating mutual data. Phys. Rev. E––Stat. Nonlinear Mushy Matter Phys. 69(6), 066138 (2004).
Fan, X.-N., Zhang, S.-W., Zhang, S.-Y., Zhu, Okay. & Lu, S. Prediction of lncRNA-disease associations by integrating various heterogeneous data sources with RWR algorithm and optimistic pointwise mutual data. BMC Bioinform. 20, 1–12 (2019).
Fux, A. et al. Goal video-based evaluation of ADHD-like canine habits utilizing machine learning. Animals. 11(10), 2806 (2021).
Fawcett, T. An introduction to ROC evaluation. Sample Recognit. Lett. 27(8), 861–874 (2006).
Ahn, J. H. et al. Development of a novel prognostic mannequin for predicting lymph node metastasis in early colorectal cancer: Evaluation primarily based on the surveillance, epidemiology, and finish outcomes database. Entrance. Oncol. 11, 614398 (2021).
Masud, M., Sikder, N., Nahid, A.-A., Bairagi, A. Okay. & AlZain, M. A. A machine learning approach to diagnosing lung and colon cancer utilizing a deep learning-based classification framework. Sensors. 21(3), 748 (2021).
Yang, T. et al. Clever imaging know-how in analysis of colorectal cancer utilizing deep learning. IEEE Entry. 7, 178839–178847 (2019).
Hanley, J. A. & McNeil, B. J. The which means and use of the realm beneath a receiver working attribute (ROC) curve. Radiology. 143(1), 29–36 (1982).
Li, S., Wu, X. & Tan, M. Gene choice utilizing hybrid particle swarm optimization and genetic algorithm. Mushy Comput. 12, 1039–1048 (2008).
Zhang, Z., Yang, P., Wu, X. & Zhang, C. An agent-based hybrid system for microarray knowledge evaluation. IEEE Intell. Syst. 24(5), 53–63 (2009).
Yang, P., Zhou, B. B., Zhang, Z. & Zomaya, A. Y. A multi-filter enhanced genetic ensemble system for gene choice and pattern classification of microarray knowledge. BMC Bioinform. 11, 1–12 (2010).
Kulkarni, A., Kumar, B. N., Ravi, V. & Murthy, U. S. Colon cancer prediction with genetics profiles utilizing evolutionary methods. Professional Syst. Appl. 38(3), 2752–2757 (2011).
Al-Rajab, M., Lu, J. & Xu, Q. Analyzing making use of excessive efficiency genetic knowledge function choice and classification algorithms for colon cancer analysis. Comput. Strategies Progr. Biomed. 146, 11–24 (2017).
Salem, H., Attiya, G. & El-Fishawy, N. Classification of human cancer ailments by gene expression profiles. Appl. Mushy Comput. 50, 124–134 (2017).
Zhao, D. et al. A dependable technique for colorectal cancer prediction primarily based on function choice and help vector machine. Med. Boil. Eng. Comput. 57, 901–912 (2019).
Al-Rajab, M., Lu, J. & Xu, Q. A framework mannequin utilizing multifilter function choice to improve colon cancer classification. Plos One. 16(4), e0249094 (2021).