Table 1 Listing of used transcriptomics and literature mining datasets

LIT-HGSOC

Set of molecular features linked to HGSOC via literature mining.

Input for generating the HGSOCr molecular model

–

TX-HELLEMAN

Meta-analysis of nine transcriptomics studies reporting differentially regulated genes associated with ovarian cancer relapse.

Input for generating the HGSOCr molecular model

[19]

TX-VERHAAK

Transcriptomics dataset from The Cancer Genome Atlas reporting on differentially expressed genes linked with ovarian cancer disease prognosis.

Input for generating the HGSOCr molecular model

[20]

TX-FERRISS

Transcriptomics study on ovarian cancer patients to identify predictors of platinum resistance.

Input for evaluating the status of mTOR signaling pathway members

[25]

TX-TOTHILL

Transcriptomics study involving more than 200 ovarian cancer patients in order to identify molecular signature for subtyping ovarian cancer.

Training set for deriving the prognostic transcript panel

[26]

TX-YOSHIHARA

Transcriptomics study to identify survival signatures in serous ovarian cancer patients.

Test set for validating the prognostic transcript panel

[27]

LIT-CISPLATIN

Set of molecular features linked to cisplatin via literature mining.

Input for generating the cisplatin MoA molecular model

–

LIT-PACLITAXEL

Set of molecular features linked to paclitaxel via literature mining.

Input for generating the paclitaxel MoA molecular model

–

LIT-SIROLIMUS

Set of molecular features linked to sirolimus via literature mining.

Input for generating the sirolimus MoA molecular model

–