Notice: This post has been published on the International Journal of Epidemiology's Blog. This repository contains the source for the post. The published version may contain some additional minor copyedits. Please refer to the IJE Blog for the authoritative version.
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compound | disease | |
---|---|---|
beclometasone | acute and chronic inflammation | |
betamethasone | acute and chronic inflammation | |
budesonide | acute and chronic inflammation | |
dexamethasone | acute and chronic inflammation | |
diflorasone | acute and chronic inflammation | |
fludroxycortide | acute and chronic inflammation | |
flunisolide | acute and chronic inflammation | |
fluticasone | acute and chronic inflammation | |
hydrocortisone | acute and chronic inflammation |
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probeset | fold_change | dugas_symbol | dugas_name | platform | ensembl_id | hgnc_id | hgnc_symbol | hgnc_id_manual | hgnc_symbol_manual | |
---|---|---|---|---|---|---|---|---|---|---|
D28111_g_at | 119.43 | MOBP | Myelin-assoc OL basic protein | HGNC:7189 | MOBP | |||||
K00512_at | 98.36 | MBP | Myelin basic protein | affy_rg_u34a | ENSRNOG00000016516 | HGNC:6925 | MBP | HGNC:6925 | MBP | |
M99485_at | 97.68 | MOG | Myelin oligodendrocyte glycoprotein | affy_rg_u34a | ENSRNOG00000000775 | HGNC:7197 | MOG | HGNC:7197 | MOG | |
rc_AI233181_at | 93.05 | ESTs, no homologies found | ||||||||
rc_AI072770_s_at | 77.71 | PLP | Proteolipid protein | affy_rg_u34a | ENSRNOG00000002419 | HGNC:9086 | PLP1 | HGNC:9086 | PLP1 | |
X55572_at | 71.01 | APOD | Apolipoprotein D | affy_rg_u34a | ENSRNOG00000048273 | HGNC:612 | APOD | HGNC:612 | APOD | |
rc_AA891719_at | 66.72 | ENPP6 | Ectonuc. pyrophos./phosphodiesterase 6 | affy_rg_u34a | ENSRNOG00000009660 | HGNC:23409 | ENPP6 | HGNC:23409 | ENPP6 | |
D88534_s_at | 60.55 | PNLIP | Pancreatic lipase | affy_rg_u34a | ENSRNOG00000017725 | HGNC:9155 | PNLIP | HGNC:9155 | PNLIP | |
rc_AA901342_at | 49.87 | OSP | Claudin 11/OL specific protein | affy_rg_u34b | ENSRNOG00000010263 | HGNC:8514 | CLDN11 | HGNC:8514 | C |
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We designed a hetnet for drug repurposing that contains 50 thousand nodes (of 10 labels) and 3 million relationships (of 27 types). And we’ve chosen neo4j for handling network storage and interaction.
I hereby claim:
- I am dhimmel on github.
- I am dhimmel (https://keybase.io/dhimmel) on keybase.
- I have a public key whose fingerprint is 8CA9 B3A5 A4B4 D442 A285 867B 03A8 B7F8 47EE A45D
To claim this, I am signing this object:
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# Compare four methods for computing the R-squared (R2, coefficient of determination) | |
# with wieghted observations for a linear regression model in R. | |
# This work was written by Daniel Himmelstein (@dhimmel) with guidance | |
# from Alex Pankov (@a-pankov). It is released as CC0 (public domain). | |
get_r2_cor <- function(y, y_pred, w) { | |
# Calculate R2 using the correlation coefficient method | |
xy = cbind(y, y_pred) | |
return(boot::corr(d=xy, w=w) ^ 2) | |
} |
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bto_id | bto_name | cell_line | |
---|---|---|---|
BTO:0000038 | SW-480 cell | 1 | |
BTO:0000041 | medulla oblongata | 0 | |
BTO:0000045 | adrenal cortex | 0 | |
BTO:0000047 | adrenal gland | 0 | |
BTO:0000084 | vermiform appendix | 0 | |
BTO:0000089 | blood | 0 | |
BTO:0000141 | bone marrow | 0 | |
BTO:0000142 | brain | 0 | |
BTO:0000211 | caudate nucleus | 0 |