WFRF:(Frye M. A.)
Sökning: WFRF:(Frye M. A.) > Prediction of lithi...
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| Fältnamn | Indikatorer | Metadata |
|---|---|---|
| 000 | 04280naa a2200889 4500 | |
| 001 | oai:prod.swepub.kib.ki.se:146014115 | |
| 003 | SwePub | |
| 008 | 240701s2021 | |||||||||||000 ||eng| | |
| 024 | 7 | a http://kipublications.ki.se/Default.aspx?queryparsed=id:1460141152 URI |
| 024 | 7 | a https://doi.org/10.1038/s41598-020-80814-z2 DOI |
| 040 | a (SwePub)ki | |
| 041 | a engb eng | |
| 042 | 9 SwePub | |
| 072 | 7 | a ref2 swepub-contenttype |
| 072 | 7 | a art2 swepub-publicationtype |
| 100 | 1 | a Stone, W4 aut |
| 245 | 1 0 | a Prediction of lithium response using genomic data |
| 264 | c 2021-01-13 | |
| 264 | 1 | b Springer Science and Business Media LLC,c 2021 |
| 520 | a Predicting lithium response prior to treatment could both expedite therapy and avoid exposure to side effects. Since lithium responsiveness may be heritable, its predictability based on genomic data is of interest. We thus evaluate the degree to which lithium response can be predicted with a machine learning (ML) approach using genomic data. Using the largest existing genomic dataset in the lithium response literature (n = 2210 across 14 international sites; 29% responders), we evaluated the degree to which lithium response could be predicted based on 47,465 genotyped single nucleotide polymorphisms using a supervised ML approach. Under appropriate cross-validation procedures, lithium response could be predicted to above-chance levels in two constituent sites (Halifax, Cohen’s kappa 0.15, 95% confidence interval, CI [0.07, 0.24]; and Würzburg, kappa 0.2 [0.1, 0.3]). Variants with shared importance in these models showed over-representation of postsynaptic membrane related genes. Lithium response was not predictable in the pooled dataset (kappa 0.02 [− 0.01, 0.04]), although non-trivial performance was achieved within a restricted dataset including only those patients followed prospectively (kappa 0.09 [0.04, 0.14]). Genomic classification of lithium response remains a promising but difficult task. Classification performance could potentially be improved by further harmonization of data collection procedures. | |
| 700 | 1 | a Nunes, A4 aut |
| 700 | 1 | a Akiyama, K4 aut |
| 700 | 1 | a Akula, N4 aut |
| 700 | 1 | a Ardau, R4 aut |
| 700 | 1 | a Aubry, JM4 aut |
| 700 | 1 | a Backlund, Lu Karolinska Institutet4 aut |
| 700 | 1 | a Bauer, M4 aut |
| 700 | 1 | a Bellivier, F4 aut |
| 700 | 1 | a Cervantes, P4 aut |
| 700 | 1 | a Chen, HC4 aut |
| 700 | 1 | a Chillotti, C4 aut |
| 700 | 1 | a Cruceanu, C4 aut |
| 700 | 1 | a Dayer, A4 aut |
| 700 | 1 | a Degenhardt, F4 aut |
| 700 | 1 | a Del Zompo, M4 aut |
| 700 | 1 | a Forstner, AJ4 aut |
| 700 | 1 | a Frye, M4 aut |
| 700 | 1 | a Fullerton, JM4 aut |
| 700 | 1 | a Grigoroiu-Serbanescu, M4 aut |
| 700 | 1 | a Grof, P4 aut |
| 700 | 1 | a Hashimoto, R4 aut |
| 700 | 1 | a Hou, LP4 aut |
| 700 | 1 | a Jimenez, E4 aut |
| 700 | 1 | a Kato, T4 aut |
| 700 | 1 | a Kelsoe, J4 aut |
| 700 | 1 | a Kittel-Schneider, S4 aut |
| 700 | 1 | a Kuo, PH4 aut |
| 700 | 1 | a Kusumi, I4 aut |
| 700 | 1 | a Lavebratt, Cu Karolinska Institutet4 aut |
| 700 | 1 | a Manchia, M4 aut |
| 700 | 1 | a Martinsson, Lu Karolinska Institutet4 aut |
| 700 | 1 | a Mattheisen, M4 aut |
| 700 | 1 | a McMahon, FJ4 aut |
| 700 | 1 | a Millischer, Vu Karolinska Institutet4 aut |
| 700 | 1 | a Mitchell, PB4 aut |
| 700 | 1 | a Nothen, MM4 aut |
| 700 | 1 | a O'Donovan, C4 aut |
| 700 | 1 | a Ozaki, N4 aut |
| 700 | 1 | a Pisanu, C4 aut |
| 700 | 1 | a Reif, A4 aut |
| 700 | 1 | a Rietschel, M4 aut |
| 700 | 1 | a Rouleau, G4 aut |
| 700 | 1 | a Rybakowski, J4 aut |
| 700 | 1 | a Schalling, Mu Karolinska Institutet4 aut |
| 700 | 1 | a Schofield, PR4 aut |
| 700 | 1 | a Schulze, TG4 aut |
| 700 | 1 | a Severino, G4 aut |
| 700 | 1 | a Squassina, A4 aut |
| 700 | 1 | a Veeh, J4 aut |
| 700 | 1 | a Vieta, E4 aut |
| 700 | 1 | a Trappenberg, T4 aut |
| 700 | 1 | a Alda, M4 aut |
| 710 | 2 | a Karolinska Institutet4 org |
| 773 | 0 | t Scientific reportsd : Springer Science and Business Media LLCg 11:1, s. 1155-q 11:1<1155-x 2045-2322 |
| 856 | 4 | u https://www.nature.com/articles/s41598-020-80814-z.pdf |
| 856 | 4 8 | u http://kipublications.ki.se/Default.aspx?queryparsed=id:146014115 |
| 856 | 4 8 | u https://doi.org/10.1038/s41598-020-80814-z |
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- Stone, W
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- Backlund, L
- Bauer, M
- Bellivier, F
- Cervantes, P
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- Del Zompo, M
- Forstner, AJ
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- Grof, P
- Hashimoto, R
- Hou, LP
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- Kusumi, I
- Lavebratt, C
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- Martinsson, L
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- Mitchell, PB
- Nothen, MM
- O'Donovan, C
- Ozaki, N
- Pisanu, C
- Reif, A
- Rietschel, M
- Rouleau, G
- Rybakowski, J
- Schalling, M
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