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Table representation of search results timeline featuring number of search results per year.

Year Number of Results
2001 1
2004 1
2005 1
2006 3
2007 1
2008 1
2009 5
2010 5
2011 4
2012 8
2013 7
2014 8
2015 8
2016 16
2017 19
2018 27
2019 38
2020 38
2021 70
2022 72
2023 102
2024 37

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434 results

Results by year

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Page 1
Artificial intelligence for proteomics and biomarker discovery.
Mann M, Kumar C, Zeng WF, Strauss MT. Mann M, et al. Cell Syst. 2021 Aug 18;12(8):759-770. doi: 10.1016/j.cels.2021.06.006. Cell Syst. 2021. PMID: 34411543 Free article. Review.
Here, we focus on mass spectrometry (MS)-based proteomics and describe how machine learning and, in particular, deep learning now predicts experimental peptide measurements from amino acid sequences alone. This will dramatically improve the quality and reliab …
Here, we focus on mass spectrometry (MS)-based proteomics and describe how machine learning and, in particular, deep learni …
The benefits and pitfalls of machine learning for biomarker discovery.
Ng S, Masarone S, Watson D, Barnes MR. Ng S, et al. Cell Tissue Res. 2023 Oct;394(1):17-31. doi: 10.1007/s00441-023-03816-z. Epub 2023 Jul 27. Cell Tissue Res. 2023. PMID: 37498390 Free PMC article. Review.
This review aims to introduce some of the basic concepts of machine learning and AI for biomarker discovery with a focus on post hoc explanation of predictions. To illustrate this, we consider how explainable AI has already been used successfully, and …
This review aims to introduce some of the basic concepts of machine learning and AI for biomarker discovery with …
Advancements within Modern Machine Learning Methodology: Impacts and Prospects in Biomarker Discovery.
Ledesma D, Symes S, Richards S. Ledesma D, et al. Curr Med Chem. 2021;28(32):6512-6531. doi: 10.2174/0929867328666210208111821. Curr Med Chem. 2021. PMID: 33557728 Review.
METHODS: A vast array of literature highlighting machine learning for biomarker discovery was reviewed, resulting in the eligibility of 21 machine learning algorithms/networks and 3 combinatory architectures, spanning 17 fields of study. …
METHODS: A vast array of literature highlighting machine learning for biomarker discovery was reviewed, resultin …
Pan-cancer integrative histology-genomic analysis via multimodal deep learning.
Chen RJ, Lu MY, Williamson DFK, Chen TY, Lipkova J, Noor Z, Shaban M, Shady M, Williams M, Joo B, Mahmood F. Chen RJ, et al. Cancer Cell. 2022 Aug 8;40(8):865-878.e6. doi: 10.1016/j.ccell.2022.07.004. Cancer Cell. 2022. PMID: 35944502 Free PMC article.
Our weakly supervised, multimodal deep-learning algorithm is able to fuse these heterogeneous modalities to predict outcomes and discover prognostic features that correlate with poor and favorable outcomes. We present all analyses for morphological and molecular correlates …
Our weakly supervised, multimodal deep-learning algorithm is able to fuse these heterogeneous modalities to predict outcomes and disc …
Machine-learning-driven biomarker discovery for the discrimination between allergic and irritant contact dermatitis.
Fortino V, Wisgrill L, Werner P, Suomela S, Linder N, Jalonen E, Suomalainen A, Marwah V, Kero M, Pesonen M, Lundin J, Lauerma A, Aalto-Korte K, Greco D, Alenius H, Fyhrquist N. Fortino V, et al. Proc Natl Acad Sci U S A. 2020 Dec 29;117(52):33474-33485. doi: 10.1073/pnas.2009192117. Epub 2020 Dec 14. Proc Natl Acad Sci U S A. 2020. PMID: 33318199 Free PMC article.
Currently, diagnostic regimes rely on allergy testing, exposure specification, and follow-up visits; however, distinguishing the clinical phenotype of irritant and allergic contact dermatitis remains challenging. Employing integrative transcriptomic analysis and machine- …
Currently, diagnostic regimes rely on allergy testing, exposure specification, and follow-up visits; however, distinguishing the clinical ph …
Machine Learning Approaches for Biomarker Discovery Using Gene Expression Data.
Zhang X, Jonassen I, Goksøyr A. Zhang X, et al. In: Helder I. N, editor. Bioinformatics [Internet]. Brisbane (AU): Exon Publications; 2021 Mar 20. Chapter 4. In: Helder I. N, editor. Bioinformatics [Internet]. Brisbane (AU): Exon Publications; 2021 Mar 20. Chapter 4. PMID: 33877765 Free Books & Documents. Review.
In recent years, machine learning techniques such as feature selection have gained more popularity. ...This chapter gives an overview of the current research advances and existing issues in biomarker discovery using machine learning appro …
In recent years, machine learning techniques such as feature selection have gained more popularity. ...This chapter gives an o …
Comprehensive assessment of cellular senescence in the tumor microenvironment.
Wang X, Ma L, Pei X, Wang H, Tang X, Pei JF, Ding YN, Qu S, Wei ZY, Wang HY, Wang X, Wei GH, Liu DP, Chen HZ. Wang X, et al. Brief Bioinform. 2022 May 13;23(3):bbac118. doi: 10.1093/bib/bbac118. Brief Bioinform. 2022. PMID: 35419596 Free PMC article.
Single-cell CS quantification revealed intra-tumor heterogeneity and activated immune microenvironment in senescent prostate cancer. Using machine learning algorithms, we identified three CS genes as potential prognostic predictors in prostate cancer and verified th …
Single-cell CS quantification revealed intra-tumor heterogeneity and activated immune microenvironment in senescent prostate cancer. Using …
Applications of Support Vector Machine (SVM) Learning in Cancer Genomics.
Huang S, Cai N, Pacheco PP, Narrandes S, Wang Y, Xu W. Huang S, et al. Cancer Genomics Proteomics. 2018 Jan-Feb;15(1):41-51. doi: 10.21873/cgp.20063. Cancer Genomics Proteomics. 2018. PMID: 29275361 Free PMC article. Review.
Machine learning with maximization (support) of separating margin (vector), called support vector machine (SVM) learning, is a powerful classification tool that has been used for cancer genomic classification or subtyping. ...Herein we reviewed the rec …
Machine learning with maximization (support) of separating margin (vector), called support vector machine (SVM) lear …
Machine learning approaches for biomarker discovery to predict large-artery atherosclerosis.
Sun TH, Wang CC, Wu YL, Hsu KC, Lee TH. Sun TH, et al. Sci Rep. 2023 Sep 13;13(1):15139. doi: 10.1038/s41598-023-42338-0. Sci Rep. 2023. PMID: 37704672 Free PMC article.
However, there are inconsistent findings regarding suitable biomarkers for the prediction of LAA. In this study, we propose a new method integrates multiple machine learning algorithms and feature selection method to handle multidimensional data. Among the six ma …
However, there are inconsistent findings regarding suitable biomarkers for the prediction of LAA. In this study, we propose a new method int …
Multi-omics research strategies in ischemic stroke: A multidimensional perspective.
Li W, Shao C, Zhou H, Du H, Chen H, Wan H, He Y. Li W, et al. Ageing Res Rev. 2022 Nov;81:101730. doi: 10.1016/j.arr.2022.101730. Epub 2022 Sep 7. Ageing Res Rev. 2022. PMID: 36087702 Review.
Integrated analysis of multi-omics approaches will provide helpful insights into stroke pathogenesis, therapeutic target identification and biomarker discovery. Here, we consider advances in genomics, transcriptomics, proteomics and metabolomics and outline their us …
Integrated analysis of multi-omics approaches will provide helpful insights into stroke pathogenesis, therapeutic target identification and …
434 results