Machine Learning to Advance Human Genome-Wide Association Studies
Machine learning, including deep learning, reinforcement learning, and generative artificial intelligence are revolutionising every area of our lives when data are made available. With the help of these methods, we can decipher information from larger datasets while addressing the complex nature of biological systems in a more efficient way. Although machine learning methods have been introduced to human genetic epidemiological research as early as 2004, those were never used to their full capacity. In this review, we outline some of the main applications of machine learning to assigning human genetic loci to health outcomes. We summarise widely used methods and discuss their advantages and challenges. We also identify several tools, such as Combi, GenNet, and GMSTool, specifically designed to integrate these methods for hypothesis-free analysis of genetic variation data. We elaborate on the additional value and limitations of these tools from a geneticist's perspective. Finally, we discuss the fast-moving field of foundation models and large multi-modal omics biobank initiatives.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:15 |
|---|---|
| Enthalten in: |
Genes - 15(2023), 1 vom: 25. Dez. |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Sigala, Rafaella E [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Genome-wide association |
|---|
| Anmerkungen: |
Date Completed 24.01.2024 Date Revised 28.01.2024 published: Electronic Citation Status MEDLINE |
|---|
| doi: |
10.3390/genes15010034 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM367454092 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM367454092 | ||
| 003 | DE-627 | ||
| 005 | 20240128232016.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240123s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.3390/genes15010034 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1273.xml |
| 035 | |a (DE-627)NLM367454092 | ||
| 035 | |a (NLM)38254924 | ||
| 035 | |a (PII)34 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Sigala, Rafaella E |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Machine Learning to Advance Human Genome-Wide Association Studies |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 24.01.2024 | ||
| 500 | |a Date Revised 28.01.2024 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Machine learning, including deep learning, reinforcement learning, and generative artificial intelligence are revolutionising every area of our lives when data are made available. With the help of these methods, we can decipher information from larger datasets while addressing the complex nature of biological systems in a more efficient way. Although machine learning methods have been introduced to human genetic epidemiological research as early as 2004, those were never used to their full capacity. In this review, we outline some of the main applications of machine learning to assigning human genetic loci to health outcomes. We summarise widely used methods and discuss their advantages and challenges. We also identify several tools, such as Combi, GenNet, and GMSTool, specifically designed to integrate these methods for hypothesis-free analysis of genetic variation data. We elaborate on the additional value and limitations of these tools from a geneticist's perspective. Finally, we discuss the fast-moving field of foundation models and large multi-modal omics biobank initiatives | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Review | |
| 650 | 4 | |a genome-wide association | |
| 650 | 4 | |a human genetics | |
| 650 | 4 | |a machine learning | |
| 700 | 1 | |a Lagou, Vasiliki |e verfasserin |4 aut | |
| 700 | 1 | |a Shmeliov, Aleksey |e verfasserin |4 aut | |
| 700 | 1 | |a Atito, Sara |e verfasserin |4 aut | |
| 700 | 1 | |a Kouchaki, Samaneh |e verfasserin |4 aut | |
| 700 | 1 | |a Awais, Muhammad |e verfasserin |4 aut | |
| 700 | 1 | |a Prokopenko, Inga |e verfasserin |4 aut | |
| 700 | 1 | |a Mahdi, Adam |e verfasserin |4 aut | |
| 700 | 1 | |a Demirkan, Ayse |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Genes |d 2011 |g 15(2023), 1 vom: 25. Dez. |w (DE-627)NLM220446326 |x 2073-4425 |7 nnns |
| 773 | 1 | 8 | |g volume:15 |g year:2023 |g number:1 |g day:25 |g month:12 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.3390/genes15010034 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 15 |j 2023 |e 1 |b 25 |c 12 | ||