{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T06:00:01Z","timestamp":1780898401776,"version":"3.54.1"},"reference-count":31,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T00:00:00Z","timestamp":1780876800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Bioinform."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Acute Myeloid Leukemia (AML) is a heterogeneous hematological malignancy with poor prognosis, despite therapeutic advances. Gene expression analysis has emerged as a powerful tool for identifying novel biological markers that can aid in predicting patient outcomes. This study is aimed to identify prognostic gene expression signatures from RNA-seq data of 457 AML patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Filtering methods were applied to reduce the set of genes, resulting in a subset of 685. Lasso-Cox, Bayesian Model Averaging (BMA), Random Survival Forest (RSF), and a Cox-time Neural Network (CtNN) were implemented to select the most promising prognostic genes. Explainable tools such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) were used to gain a deeper understanding of neural network outputs. An enrichment analysis was performed to identify enriched biological pathways.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>SHAP and LIME interpretations of the CtNN output identified gene sets that differed from those selected by the other algorithms. According to the two-validation metrics the SHAP-derived signature demonstrated superior performance in the testing set. (C-index[95%CI]: 0.639[0.581\u20130.696] and Integrated Brier Score [95%CI]: 0.172[0.161\u20130.183]).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Enrichment analysis revealed structural and developmental pathways, emphasizing the role of microtubule dynamics and ciliary-related signaling in the bone marrow microenvironment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fbinf.2026.1805826","type":"journal-article","created":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T05:36:53Z","timestamp":1780897013000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Harnessing cutting-edge techniques to identify novel gene expression signatures in acute myeloid leukemia patients"],"prefix":"10.3389","volume":"6","author":[{"given":"Adriana","family":"Blanda","sequence":"first","affiliation":[{"name":"Unit of Bioinformatics and Biostatistics, Fondazione IRCCS Istituto Nazionale Dei Tumori","place":["Milan, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rebecca","family":"Manitto","sequence":"additional","affiliation":[{"name":"Unit of Bioinformatics and Biostatistics, Fondazione IRCCS Istituto Nazionale Dei Tumori","place":["Milan, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sara","family":"Pizzamiglio","sequence":"additional","affiliation":[{"name":"Unit of Bioinformatics and Biostatistics, Fondazione IRCCS Istituto Nazionale Dei Tumori","place":["Milan, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sabina","family":"Sangaletti","sequence":"additional","affiliation":[{"name":"Unit of Molecular Immunology, Fondazione IRCCS Istituto Nazionale Dei Tumori","place":["Milan, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paolo","family":"Verderio","sequence":"additional","affiliation":[{"name":"Unit of Bioinformatics and Biostatistics, Fondazione IRCCS Istituto Nazionale Dei Tumori","place":["Milan, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,6,8]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1186\/1471-2105-10-72","article-title":"Iterative bayesian model averaging: a method for the application of survival analysis to high-dimensional microarray data","volume":"10","author":"Annest","year":"2009","journal-title":"BMC Bioinforma."},{"key":"B2","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1038\/75556","article-title":"Geneontology: tool for the unification of biology","volume":"25","author":"Ashburner","year":"2000","journal-title":"Nat. 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