Machine learning in the identification of prognostic DNA methylation biomarkers among patients with cancer: a systematic review of epigenome-wide studies
Background - DNA methylation biomarkers have great potential in improving prognostic classification systems for patients with cancer. Machine learning (ML)-based analytic techniques might help overcome the challenges of analyzing high-dimensional data in relatively small sample sizes. This systemati...
Gespeichert in:
| Hauptverfasser: | , , , , , , |
|---|---|
| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
September 2023
|
| In: |
Artificial intelligence in medicine
Year: 2023, Jahrgang: 143, Pages: 1-12 |
| ISSN: | 1873-2860 |
| DOI: | 10.1016/j.artmed.2023.102589 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1016/j.artmed.2023.102589 Verlag, kostenfrei, Volltext: https://www.sciencedirect.com/science/article/pii/S0933365723001033 
|
| Verfasserangaben: | Tanwei Yuan, Dominic Edelmann, Ziwen Fan, Elizabeth Alwers, Jakob Nikolas Kather, Hermann Brenner, Michael Hoffmeister |
MARC
| LEADER | 00000caa a2200000 c 4500 | ||
|---|---|---|---|
| 001 | 1857588630 | ||
| 003 | DE-627 | ||
| 005 | 20240307060252.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 230822s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.artmed.2023.102589 |2 doi | |
| 035 | |a (DE-627)1857588630 | ||
| 035 | |a (DE-599)KXP1857588630 | ||
| 035 | |a (OCoLC)1425212715 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rda | ||
| 041 | |a eng | ||
| 084 | |a 33 |2 sdnb | ||
| 100 | 1 | |a Yuan, Tanwei |d 1995- |e VerfasserIn |0 (DE-588)1299871909 |0 (DE-627)1857588649 |4 aut | |
| 245 | 1 | 0 | |a Machine learning in the identification of prognostic DNA methylation biomarkers among patients with cancer |b a systematic review of epigenome-wide studies |c Tanwei Yuan, Dominic Edelmann, Ziwen Fan, Elizabeth Alwers, Jakob Nikolas Kather, Hermann Brenner, Michael Hoffmeister |
| 264 | 1 | |c September 2023 | |
| 300 | |a 12 | ||
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Online veröffentlicht: 1. Juni 2023 | ||
| 500 | |a Gesehen am 22.08.2023 | ||
| 520 | |a Background - DNA methylation biomarkers have great potential in improving prognostic classification systems for patients with cancer. Machine learning (ML)-based analytic techniques might help overcome the challenges of analyzing high-dimensional data in relatively small sample sizes. This systematic review summarizes the current use of ML-based methods in epigenome-wide studies for the identification of DNA methylation signatures associated with cancer prognosis. - Methods - We searched three electronic databases including PubMed, EMBASE, and Web of Science for articles published until 2 January 2023. ML-based methods and workflows used to identify DNA methylation signatures associated with cancer prognosis were extracted and summarized. Two authors independently assessed the methodological quality of included studies by a seven-item checklist adapted from 'A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies (PROBAST)' and from the 'Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK). Different ML methods and workflows used in included studies were summarized and visualized by a sunburst chart, a bubble chart, and Sankey diagrams, respectively. - Results - Eighty-three studies were included in this review. Three major types of ML-based workflows were identified. 1) unsupervised clustering, 2) supervised feature selection, and 3) deep learning-based feature transformation. For the three workflows, the most frequently used ML techniques were consensus clustering, least absolute shrinkage and selection operator (LASSO), and autoencoder, respectively. The systematic review revealed that the performance of these approaches has not been adequately evaluated yet and that methodological and reporting flaws were common in the identified studies using ML techniques. - Conclusions - There is great heterogeneity in ML-based methodological strategies used by epigenome-wide studies to identify DNA methylation markers associated with cancer prognosis. In theory, most existing workflows could not handle the high multi-collinearity and potentially non-linearity interactions in epigenome-wide DNA methylation data. Benchmarking studies are needed to compare the relative performance of various approaches for specific cancer types. Adherence to relevant methodological and reporting guidelines are urgently needed. | ||
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Cancer prognosis | |
| 650 | 4 | |a DNA methylation | |
| 650 | 4 | |a Epigenetic biomarkers | |
| 650 | 4 | |a Epigenome-wide studies | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Systematic review | |
| 700 | 1 | |a Edelmann, Dominic |e VerfasserIn |0 (DE-588)1074380223 |0 (DE-627)832085499 |0 (DE-576)442634854 |4 aut | |
| 700 | 1 | |a Fan, Ziwen |e VerfasserIn |0 (DE-588)1299872069 |0 (DE-627)1857588665 |4 aut | |
| 700 | 1 | |a Alwers, Elizabeth |e VerfasserIn |0 (DE-588)1181909058 |0 (DE-627)1662464614 |4 aut | |
| 700 | 1 | |a Kather, Jakob Nikolas |d 1989- |e VerfasserIn |0 (DE-588)1064064914 |0 (DE-627)812897587 |0 (DE-576)423589091 |4 aut | |
| 700 | 1 | |a Brenner, Hermann |e VerfasserIn |0 (DE-588)1020516445 |0 (DE-627)691247005 |0 (DE-576)360642136 |4 aut | |
| 700 | 1 | |a Hoffmeister, Michael |d 1973- |e VerfasserIn |0 (DE-588)134103726 |0 (DE-627)560880820 |0 (DE-576)277089565 |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Artificial intelligence in medicine |d Amsterdam [u.a.] : Elsevier Science, 1989 |g 143(2023) vom: Sept., Artikel-ID 102589, Seite 1-12 |h Online-Ressource |w (DE-627)320415627 |w (DE-600)2001878-2 |w (DE-576)098614916 |x 1873-2860 |7 nnas |a Machine learning in the identification of prognostic DNA methylation biomarkers among patients with cancer a systematic review of epigenome-wide studies |
| 773 | 1 | 8 | |g volume:143 |g year:2023 |g month:09 |g elocationid:102589 |g pages:1-12 |g extent:12 |a Machine learning in the identification of prognostic DNA methylation biomarkers among patients with cancer a systematic review of epigenome-wide studies |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.artmed.2023.102589 |x Verlag |x Resolving-System |z kostenfrei |3 Volltext |
| 856 | 4 | 0 | |u https://www.sciencedirect.com/science/article/pii/S0933365723001033 |x Verlag |z kostenfrei |3 Volltext |
| 951 | |a AR | ||
| 992 | |a 20230822 | ||
| 993 | |a Article | ||
| 994 | |a 2023 | ||
| 998 | |g 134103726 |a Hoffmeister, Michael |m 134103726:Hoffmeister, Michael |d 50000 |e 50000PH134103726 |k 0/50000/ |p 7 |y j | ||
| 998 | |g 1020516445 |a Brenner, Hermann |m 1020516445:Brenner, Hermann |d 850000 |d 851600 |d 50000 |e 850000PB1020516445 |e 851600PB1020516445 |e 50000PB1020516445 |k 0/850000/ |k 1/850000/851600/ |k 0/50000/ |p 6 | ||
| 998 | |g 1064064914 |a Kather, Jakob Nikolas |m 1064064914:Kather, Jakob Nikolas |d 910000 |d 910100 |e 910000PK1064064914 |e 910100PK1064064914 |k 0/910000/ |k 1/910000/910100/ |p 5 | ||
| 998 | |g 1181909058 |a Alwers, Elizabeth |m 1181909058:Alwers, Elizabeth |d 50000 |e 50000PA1181909058 |k 0/50000/ |p 4 | ||
| 998 | |g 1299872069 |a Fan, Ziwen |m 1299872069:Fan, Ziwen |p 3 | ||
| 998 | |g 1074380223 |a Edelmann, Dominic |m 1074380223:Edelmann, Dominic |d 110000 |e 110000PE1074380223 |k 0/110000/ |p 2 | ||
| 998 | |g 1299871909 |a Yuan, Tanwei |m 1299871909:Yuan, Tanwei |d 50000 |e 50000PY1299871909 |k 0/50000/ |p 1 |x j | ||
| 999 | |a KXP-PPN1857588630 |e 4369516285 | ||
| BIB | |a Y | ||
| SER | |a journal | ||
| JSO | |a {"person":[{"given":"Tanwei","role":"aut","display":"Yuan, Tanwei","family":"Yuan"},{"role":"aut","display":"Edelmann, Dominic","family":"Edelmann","given":"Dominic"},{"given":"Ziwen","family":"Fan","display":"Fan, Ziwen","role":"aut"},{"given":"Elizabeth","family":"Alwers","display":"Alwers, Elizabeth","role":"aut"},{"family":"Kather","display":"Kather, Jakob Nikolas","role":"aut","given":"Jakob Nikolas"},{"family":"Brenner","display":"Brenner, Hermann","role":"aut","given":"Hermann"},{"role":"aut","display":"Hoffmeister, Michael","family":"Hoffmeister","given":"Michael"}],"type":{"media":"Online-Ressource","bibl":"article-journal"},"relHost":[{"recId":"320415627","titleAlt":[{"title":"AIM"}],"type":{"media":"Online-Ressource","bibl":"periodical"},"pubHistory":["1.1989 -"],"disp":"Machine learning in the identification of prognostic DNA methylation biomarkers among patients with cancer a systematic review of epigenome-wide studiesArtificial intelligence in medicine","part":{"text":"143(2023) vom: Sept., Artikel-ID 102589, Seite 1-12","extent":"12","year":"2023","pages":"1-12","volume":"143"},"origin":[{"publisher":"Elsevier Science","publisherPlace":"Amsterdam [u.a.]","dateIssuedKey":"1989","dateIssuedDisp":"1989-"}],"id":{"eki":["320415627"],"zdb":["2001878-2"],"issn":["1873-2860"]},"physDesc":[{"extent":"Online-Ressource"}],"language":["eng"],"title":[{"title_sort":"Artificial intelligence in medicine","subtitle":"AIM","title":"Artificial intelligence in medicine"}],"note":["Gesehen am 14.10.2020"]}],"origin":[{"dateIssuedKey":"2023","dateIssuedDisp":"September 2023"}],"recId":"1857588630","id":{"doi":["10.1016/j.artmed.2023.102589"],"eki":["1857588630"]},"physDesc":[{"extent":"12 S."}],"title":[{"title_sort":"Machine learning in the identification of prognostic DNA methylation biomarkers among patients with cancer","subtitle":"a systematic review of epigenome-wide studies","title":"Machine learning in the identification of prognostic DNA methylation biomarkers among patients with cancer"}],"language":["eng"],"note":["Online veröffentlicht: 1. Juni 2023","Gesehen am 22.08.2023"],"name":{"displayForm":["Tanwei Yuan, Dominic Edelmann, Ziwen Fan, Elizabeth Alwers, Jakob Nikolas Kather, Hermann Brenner, Michael Hoffmeister"]}} | ||
| SRT | |a YUANTANWEIMACHINELEA2023 | ||