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A Deep Learning based Efficacy Prediction System for Drug Discovery [Trametinib]

ABSTRACT: A Deep Learning based Efficacy Prediction System for Drug Discovery [Trametinib]

PROVIDER: PRJNA693497 | ENA |

REPOSITORIES: ENA

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A Deep Learning based Efficacy Prediction System for Drug Discovery [Trametinib]

Project description:Target protein-based drug and research compound discovery has been undeniably successful strategy in life science research, yet many diseases and biological processes lack obvious targets to enable these approaches. Here, to overcome this major challenge we have developed a deep-learning based efficacy prediction system (DLEPS) to identify potent agents to treat diverse diseases; DLEPS was trained using L1000 project chemical induced “changes of transcriptional profiles” (CTP) data as input. Strikingly, we found that the CTPs for previously unexamined molecules were precisely predicted (0.74 Pearson correlation coefficient). We used DLEPS to examine 4 disorders, and experimentally validated that perillen, chikusetsusaponin IV, trametinib, and liquiritin confer disease-relevant impacts against obesity, hyperuricemia, NASH, and COVID-19, respectively. Importantly, DLEPS also uncovered the biological insight that the MEK-ERK signaling pathway should be understood as a target for developing anti-NASH agents. Beyond illustrating that DLEPS is an effective tool for drug repurposing and development with diverse diseases (including those lacking targets), our study shows how diverse transcriptomics datasets can be harnessed to identify inhibitors and activator chemicals that can expand the scope of biological investigations well beyond mutant-bases analyses.

2023-03-31 | GSE165174 | GEO

A Deep Learning based Efficacy Prediction System for Drug Discovery [Perillen]

2023-03-31 | GSE165173 | GEO

A Deep Learning based Efficacy Prediction System for Drug Discovery

Project description:A Deep Learning based Efficacy Prediction System for Drug Discovery

| PRJNA693487 | ENA

A Deep Learning based Efficacy Prediction System for Drug Discovery [Chikusetsusaponin IV]

2023-03-31 | GSE165171 | GEO

A Deep Learning based Efficacy Prediction System for Drug Discovery [Perillen]

Project description:A Deep Learning based Efficacy Prediction System for Drug Discovery [Perillen]

| PRJNA693496 | ENA

A Deep Learning based Efficacy Prediction System for Drug Discovery [Chikusetsusaponin IV]

Project description:A Deep Learning based Efficacy Prediction System for Drug Discovery [Chikusetsusaponin IV]

| PRJNA693494 | ENA

A Deep Learning based Efficacy Prediction System for Drug Discovery

Project description:This SuperSeries is composed of the SubSeries listed below.

2023-03-31 | GSE165175 | GEO

Efficacy of trametinib and dabrafenib associated with radioactive iodine for the treatment of refractory metastatic differentiated thyroid cancer

Project description:Efficacy of a selective MEK (trametinib) and BRAFV600E (dabrafenib) inhibitors associated with radioactive iodine (RAI) for the treatment of refractory metastatic differentiated thyroid cancer with RAS or BRAFV600E mutation. To evaluate the objective response rate according to RECIST criteria in thyroid cancer patients with metastatic radioactive iodine (RAI) refractory disease, 6 months after a treatment combining in each arm of the phase II trial (patients with RAS mutation or patients with BRAFV600E mutation): - Arm A: 6 weeks of trametinib followed by RAI treatment (5.5 GBq following rhTSH) in patients with RAS mutation - Arm B: 6 weeks of trametinib plus dabrafenib followed by RAI treatment (5.5 GBq following rhTSH) in patients with BRAFV600E mutation

2023-09-01 | E-MTAB-12837 | biostudies-arrayexpress

Efficacy of trametinib and dabrafenib associated with radioactive iodine for the treatment of refractory metastatic differentiated thyroid cancer (KRAS cohort)

Project description:Efficacy of a selective MEK (trametinib) and BRAFV600E (dabrafenib) inhibitors associated with radioactive iodine (RAI) for the treatment of refractory metastatic differentiated thyroid cancer with RAS (current cohort) or BRAFV600E mutation. To evaluate the objective response rate according to RECIST criteria in thyroid cancer patients with metastatic radioactive iodine (RAI) refractory disease, 6 months after a treatment combining in each arm of the phase II trial (patients with RAS mutation or patients with BRAFV600E mutation): - Arm A: 6 weeks of trametinib followed by RAI treatment (5.5 GBq following rhTSH) in patients with RAS mutation - Arm B: 6 weeks of trametinib plus dabrafenib followed by RAI treatment (5.5 GBq following rhTSH) in patients with BRAFV600E mutation

2023-09-01 | E-MTAB-12900 | biostudies-arrayexpress

Deep Representation Learning Improves Prediction of LacI-mediated Transcriptional Repression

Project description:Deep Representation Learning Improves Prediction of LacI-mediated Transcriptional Repression

| PRJNA732423 | ENA