Kreitmaier, P., Katsoula, G. & Zeggini, E. Insights from multi-omics integration in complex disease primary tissues. Trends Genet. 39, 46–58 (2023).
Google Scholar
Li, Y. & Ning, K. Biomedical applications: The need for multi-omics. In Methodologies of Multi-Omics Data Integration and Data Mining: Techniques and Applications, 13–31 (Springer, 2023).
Yang, L., Yang, Y., Huang, L., Cui, X. & Liu, Y. From single-to multi-omics: future research trends in medicinal plants. Brief. Bioinforma. 24, bbac485 (2023).
Google Scholar
Brooks, T. G., Lahens, N. F., Mrčela, A. & Grant, G. R. Challenges and best practices in omics benchmarking. Nat. Rev. Genet. 25, 326–339 (2024).
Google Scholar
Neagu, A.-N. et al. Omics-based investigations of breast cancer. Molecules 28, 4768 (2023).
Google Scholar
Liao, J. G. & Chin, K.-V. Logistic regression for disease classification using microarray data: model selection in a large p and small n case. Bioinformatics 23, 1945–1951 (2007).
Google Scholar
Kumar Myakalwar, A. et al. Less is more: Avoiding the LIBS dimensionality curse through judicious feature selection for explosive detection. Sci. Rep. 5, 13169 (2015).
Google Scholar
Liu, H. et al. Evolving feature selection. IEEE Intell. Syst. 20, 64–76 (2005).
Google Scholar
Chen, Y., Gu, Y., Hu, Z. & Sun, X. Sample-specific perturbation of gene interactions identifies breast cancer subtypes. Brief. Bioinform. 22, bbaa268 (2020).
Google Scholar
Buus, R. et al. Molecular drivers of onco DX, prosigna, EndoPredict, and the breast cancer index: A TransATAC study. J. Clin. Oncol. 39, 126–135 (2021).
Google Scholar
Curigliano, G. et al. Incorporating clinicopathological and molecular risk prediction tools to improve outcomes in early hr+/her2–breast cancer. NPJ Breast Cancer 9, 56 (2023).
Google Scholar
Lim, C. X. et al. Healthcare professionals’ and consumers’ knowledge, attitudes, perspectives, and education needs in oncology pharmacogenomics: A systematic review. Clin. Transl. Sci. 16, 2467–2482 (2023).
Google Scholar
Krystel-Whittemore, M., Tan, P. H. & Wen, H. Y. Predictive and prognostic biomarkers in breast tumours. Pathology 56, 186–191 (2024).
Google Scholar
MotieGhader, H., Masoudi-Sobhanzadeh, Y., Ashtiani, S. H. & Masoudi-Nejad, A. mRNA and microRNA selection for breast cancer molecular subtype stratification using meta-heuristic based algorithms. Genomics 112, 3207–3217 (2020).
Google Scholar
Bommert, A., Welchowski, T., Schmid, M. & Rahnenführer, J. Benchmark of filter methods for feature selection in high-dimensional gene expression survival data. Brief. Bioinform 23, bbab354 (2022).
Google Scholar
Jović, A., Brkić, K. & Bogunović, N. A review of feature selection methods with applications. In 38th international convention on information and communication technology, electronics and microelectronics (MIPRO), 1200–1205 (2015).
Pirgazi, J., Alimoradi, M., Esmaeili Abharian, T. & Olyaee, M. H. An efficient hybrid filter-wrapper metaheuristic-based gene selection method for high dimensional datasets. Sci. Rep. 9, 18580 (2019).
Google Scholar
Kundu, R., Chattopadhyay, S., Cuevas, E. & Sarkar, R. AltWOA: Altruistic whale optimization algorithm for feature selection on microarray datasets. Comput. Biol. Med. 144, 105349 (2022).
Google Scholar
Wang, A., Liu, H., Yang, J. & Chen, G. Ensemble feature selection for stable biomarker identification and cancer classification from microarray expression data. Comput. Biol. Med. 142, 105208 (2022).
Google Scholar
Qu, C. et al. Improving feature selection performance for classification of gene expression data using harris hawks optimizer with variable neighborhood learning. Brief. Bioinform 22, bbab097 (2021).
Google Scholar
Pashaei, E. Mutation-based binary aquila optimizer for gene selection in cancer classification. Comput. Biol. Chem. 101, 107767 (2022).
Google Scholar
Peng, C. et al. MGRFE: Multilayer recursive feature elimination based on an embedded genetic algorithm for cancer classification. IEEE ACM Trans. Comput. Biol. Bioinform. 18, 621–632 (2021).
Google Scholar
Gao, S. et al. RIFS2D: A two-dimensional version of a randomly restarted incremental feature selection algorithm with an application for detecting low-ranked biomarkers. Comput. Biol. Med. 133, 104405 (2021).
Google Scholar
Pudjihartono, N., Fadason, T., Kempa-Liehr, A. W. & O’Sullivan, J. M. A review of feature selection methods for machine learning-based disease risk prediction. Front. Bioinforma. 2, 927312 (2022).
Google Scholar
Hazra, A. & Gogtay, N. Biostatistics series module 3: comparing groups: numerical variables. Indian J. Dermatol. 61, 251–260 (2016).
Google Scholar
Brunner, E. & Munzel, U. The nonparametric behrens-fisher problem: asymptotic theory and a small-sample approximation. Biometrical J. 42, 17–25 (2000).
Google Scholar
Ahmed, S. K. How to choose a sampling technique and determine sample size for research: A simplified guide for researchers. Oral. Oncol. Rep. 12, 100662 (2024).
Google Scholar
Lohr, S. L. Sampling: Design and Analysis (Chapman and Hall/CRC, 2021).
Mangal, A. & Holm, E. A. A comparative study of feature selection methods for stress hotspot classification in materials. Integrating Mater. Manuf. Innov. 7, 87–95 (2018).
Google Scholar
Danasingh, A. A. G. S., Subramanian, Aa. B. & Epiphany, J. L. Identifying redundant features using unsupervised learning for high-dimensional data. SN Appl. Sci. 2, 1367 (2020).
Google Scholar
Lü, X., Meng, L., Chen, C. & Wang, P. Fuzzy removing redundancy restricted boltzmann machine: Improving learning speed and classification accuracy. IEEE Trans. Fuzzy Syst. 28, 2495–2509 (2019).
Zhang, B. & Cao, P. Classification of high dimensional biomedical data based on feature selection using redundant removal. PloS one 14, e0214406 (2019).
Google Scholar
Ding, C. & Peng, H. Minimum redundancy feature selection from microarray gene expression data. J. Bioinforma. Comput. Biol. 3, 185–205 (2005).
Google Scholar
Kraskov, A., Stögbauer, H. & Grassberger, P. Estimating mutual information. Phys. Rev. E-Stat. Nonlinear Soft Matter Phys. 69, 066138 (2004).
Google Scholar
Han, Y., Huang, L. & Zhou, F. A dynamic recursive feature elimination framework (dRFE) to further refine a set of OMIC biomarkers. Bioinformatics 37, 2183–2189 (2021).
Google Scholar
Marjit, S., Bhattacharyya, T., Chatterjee, B. & Sarkar, R. Simulated annealing aided genetic algorithm for gene selection from microarray data. Comput. Biol. Med. 158, 106854 (2023).
Google Scholar
Zou, H. & Hastie, T. Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B Stat. Methodol. 67, 301–320 (2005).
Google Scholar
Arboretti, R., Barzizza, E., Biasetton, N. & Disegna, M. A review of multivariate permutation tests: Findings and trends. J. Multivariate Anal 207, 105421 (2025).
Google Scholar
Cohen, J. Statistical Power Analysis for the Behavioral Sciences (Routledge, 2013).
Eswaran, J. et al. Transcriptomic landscape of breast cancers through mrna sequencing. Sci. Rep. 2, 264 (2012).
Google Scholar
Horvath, A. et al. Novel insights into breast cancer genetic variance through rna sequencing. Sci. Rep. 3, 2256 (2013).
Google Scholar
Kretschmer, C., Conradi, A., Kemmner, W. & Sterner-Kock, A. Latent transforming growth factor binding protein 4 (ltbp4) is downregulated in mouse and human dcis and mammary carcinomas. Cell. Oncol. 34, 419–434 (2011).
Google Scholar
Kretschmer, C. et al. Identification of early molecular markers for breast cancer. Mol. cancer 10, 15 (2011).
Google Scholar
Piñero, J. et al. The disgenet knowledge platform for disease genomics: 2019 update. Nucleic Acids Res. 48, D845–D855 (2020).
Google Scholar
Haan, J. C. et al. Mammaprint and blueprint comprehensively capture the cancer hallmarks in early-stage breast cancer patients. Genes Chromosomes Cancer 61, 148–160 (2022).
Google Scholar
Jairath, N. K. et al. A systematic review of the evidence for the decipher genomic classifier in prostate cancer. Eur. Urol. 79, 374–383 (2021).
Google Scholar
Koc, M. A. et al. Molecular and translational biology of the blood-based veristrat® proteomic test used in cancer immunotherapy treatment guidance. J. Mass Spectrom. Adv. Clin. lab 30, 51–60 (2023).
Google Scholar
Misra, P. & Yadav, A. S. Improving the classification accuracy using recursive feature elimination with cross-validation. Int. J. Emerg. Technol. 11, 659–665 (2020).
Chan, J. Y.-L. et al. A correlation-embedded attention module to mitigate multicollinearity: An algorithmic trading application. Mathematics 10, 1231 (2022).
Google Scholar
Atenafu, E. G., Hamid, J. S., Stephens, D., To, T. & Beyene, J. A small p-value from an observed data is not evidence of adequate power for future similar-sized studies: A cautionary note. Contemp. Clin. trials 30, 155–157 (2009).
Google Scholar
Efron, B. & Tibshirani, R. J. An Introduction to the Bootstrap (Chapman and Hall/CRC, 1994).
Bui, P. H. D., Nguyen, L. Y. B., Ngo, L. D. & Nguyen, H. T. T-test-based feature selection on dna microarrays gene expression data for leukemia classification. In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, 207–218 (Springer, 2025).
Koul, N. & Manvi, S. S. Feature selection from gene expression data using simulated annealing and partial least squares regression coefficients. Glob. Transit. Proc. 3, 251–256 (2022).
Google Scholar
Rotimi, S. O. et al. Gene expression profiling analysis reveals putative phytochemotherapeutic target for castration-resistant prostate cancer. Front. Oncol. 9, 714 (2019).
Google Scholar
Van Rijsbergen, C. J. Foundation of evaluation. J. Documentation 30, 365–373 (1974).
Google Scholar
Clifford, G. D. et al. Recent advances in heart sound analysis. Physiological Meas. 38, E10–E25 (2017).
Google Scholar
Ren, Y. et al. Gender specificity improves the early-stage detection of clear cell renal cell carcinoma based on methylomic biomarkers. Biomark. Med. 12, 607–618 (2018).
Google Scholar
Guo, D., Li, J., Jiang, S.-H., Li, X. & Chen, Z. Intelligent assistant driving method for tunnel boring machine based on big data. Acta Geotechnica 17, 1019–1030 (2022).
Google Scholar
Grandini, M., Bagli, E. & Visani, G. Metrics for multi-class classification: an overview. Preprint at https://arxiv.org/abs/2008.05756 (2020).
Conti Bellocchi, M. C. et al. Development and validation of a risk score for prediction of clinical success after duodenal stenting for malignant gastric outlet obstruction. Expert Rev. Gastroenterol. Hepatol. 16, 393–399 (2022).
Google Scholar
Moore, J. H. & Williams, S. M. New strategies for identifying gene-gene interactions in hypertension. Ann. Med. 34, 88–95 (2002).
Google Scholar
Satopaa, V., Albrecht, J., Irwin, D. & Raghavan, B. Finding a” kneedle” in a haystack: Detecting knee points in system behavior. In 2011 31st International Conference on Distributed Computing Systems Workshops, 166–171 (IEEE, 2011).
Chiaretti, S. et al. Gene expression profile of adult t-cell acute lymphocytic leukemia identifies distinct subsets of patients with different response to therapy and survival. Blood 103, 2771–2778 (2004).
Google Scholar
Dabba, A., Tari, A., Meftali, S. & Mokhtari, R. Gene selection and classification of microarray data method based on mutual information and moth flame algorithm. Expert Syst. Appl. 166, 114012 (2021).
Google Scholar
Pomeroy, S. L. et al. Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature 415, 436–442 (2002).
Google Scholar
Alon, U. et al. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. Natl. Acad. Sci. USA 96, 6745–6750 (1999).
Google Scholar
Gravier, E. et al. A prognostic DNA signature for T1T2 node-negative breast cancer patients. Genes Chromosomes Cancer 49, 1125–1134 (2010).
Google Scholar
Alter, M. D. et al. Autism and increased paternal age related changes in global levels of gene expression regulation. PLoS One 6, e16715 (2011).
Google Scholar
Rousseaux, S. et al. Ectopic activation of germline and placental genes identifies aggressive metastasis-prone lung cancers. Sci. Transl. Med. 5, 186ra66 (2013).
Google Scholar
Levy, H. et al. Transcriptional signatures as a disease-specific and predictive inflammatory biomarker for type 1 diabetes. Genes Immun. 13, 593–604 (2012).
Google Scholar
Tian, E. et al. The role of the wnt-signaling antagonist DKK1 in the development of osteolytic lesions in multiple myeloma. N. Engl. J. Med. 349, 2483–2494 (2003).
Google Scholar
Shamir, R. et al. Analysis of blood-based gene expression in idiopathic parkinson disease. Neurology 89, 1676–1683 (2017).
Google Scholar
Sun, L. et al. Neuronal and glioma-derived stem cell factor induces angiogenesis within the brain. Cancer Cell 9, 287–300 (2006).
Google Scholar
Putluri, N. et al. Pathway-centric integrative analysis identifies rrm2 as a prognostic marker in breast cancer associated with poor survival and tamoxifen resistance. Neoplasia 16, 390–402 (2014).
Google Scholar
Tang, W. et al. Correction: Integrated proteotranscriptomics of breast cancer reveals globally increased protein-mrna concordance associated with subtypes and survival. Genome Med. 17, 69 (2025).
Google Scholar
Terunuma, A. et al. Myc-driven accumulation of 2-hydroxyglutarate is associated with breast cancer prognosis. J. Clin. Investig. 124, 398–412 (2014).
Google Scholar
Juul, N. et al. Assessment of an rna interference screen-derived mitotic and ceramide pathway metagene as a predictor of response to neoadjuvant paclitaxel for primary triple-negative breast cancer: a retrospective analysis of five clinical trials. lancet Oncol. 11, 358–365 (2010).
Google Scholar
Li, Y. et al. Amplification of laptm4b and ywhaz contributes to chemotherapy resistance and recurrence of breast cancer. Nat. Med. 16, 214–218 (2010).
Google Scholar
Silver, D. P. et al. Efficacy of neoadjuvant cisplatin in triple-negative breast cancer. J. Clin. Oncol. 28, 1145–1153 (2010).
Google Scholar
Turashvili, G. et al. Novel markers for differentiation of lobular and ductal invasive breast carcinomas by laser microdissection and microarray analysis. BMC cancer 7, 55 (2007).
Google Scholar
Li, S.-Y. et al. Tumor circadian clock strength influences metastatic potential and predicts patient prognosis in luminal a breast cancer. Proc. Natl. Acad. Sci. 121, e2311854121 (2024).
Google Scholar
Seo, J.-S. et al. The transcriptional landscape and mutational profile of lung adenocarcinoma. Genome Res. 22, 2109–2119 (2012).
Google Scholar
Martuscello, R. T. et al. Gene expression analysis of the cerebellar cortex in essential tremor. Neurosci. Lett. 721, 134540 (2020).
Google Scholar
