From a6f0e92e51cede822c0c2bc06ccab9b0c7f6c948 Mon Sep 17 00:00:00 2001 From: Alejandro Velez Arce Date: Sat, 22 Jun 2024 05:03:30 -0400 Subject: [PATCH 1/6] Update index.html rm 215 from scdti group benchmark --- benchmark/scdti_group/overview/index.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/benchmark/scdti_group/overview/index.html b/benchmark/scdti_group/overview/index.html index b5b49440..da60d55a 100644 --- a/benchmark/scdti_group/overview/index.html +++ b/benchmark/scdti_group/overview/index.html @@ -222,7 +222,7 @@

Single-cell Drug-Ta

We formalize the predictive, non-generative, task definition as a function taking a given protein, cell, and drug as input to output a score representing a probability of drug-target binding. More details to-be-announced.

-

A dataset containing drug-target interactions at single-cell resolution for various contextualized215 +

A dataset containing drug-target interactions at single-cell resolution for various contextualized proteins and diseases must be constructed to train a model for this task. In TDC-2, we assembeled a dataset containing data points for Rheumatoid Arthritis and Inflammatory Bowel Disease. More details to-be-announced.

To access a benchmark in the group, use the following code:

From 81b3e98ece3049a894405ad40e50fc0e5a9aa3f5 Mon Sep 17 00:00:00 2001 From: Alejandro Velez Arce Date: Sat, 22 Jun 2024 05:07:49 -0400 Subject: [PATCH 2/6] Update index.html fix protein-peptide page --- benchmark/proteinpeptide_group/overview/index.html | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/benchmark/proteinpeptide_group/overview/index.html b/benchmark/proteinpeptide_group/overview/index.html index 95e7a1ed..a2250dfb 100644 --- a/benchmark/proteinpeptide_group/overview/index.html +++ b/benchmark/proteinpeptide_group/overview/index.html @@ -232,7 +232,9 @@

Protein-Peptide Interaction Benchmark Group (+TCR-Epitope)

-

TDC-2 introduces the Protein-Peptide Binding Affinity prediction task. The predictive, non-generative task is to learn a model estimating a function of a protein, peptide, antigen processing pathway, biological context, and interaction features. It outputs a binding affinity value or binary label indicating strong or weak binding. The binary label can also include additional biomarkers, such as allowing for a positive label if and only if the binding interaction is specific. To account for additional biomarkers beyond binding affinity value, our task is specified with a binary label. +

TDC-2 introduces the Protein-Peptide Binding Affinity prediction task. The predictive, non-generative task is to learn a model estimating a function of a protein, peptide, antigen processing pathway, biological context, and interaction features. It outputs a binding affinity value or binary label indicating strong or weak binding. The binary label can also include additional biomarkers, such as allowing for a positive label if and only if the binding interaction is specific. +
+ To account for additional biomarkers beyond binding affinity value, our task is specified with a binary label.
TDC-2 provides datasets and benchmarks for a generalized protein-peptide binding interaction prediction task and a TCR-Epitope binding interaction prediction task.

@@ -266,8 +268,6 @@

Protein-Peptide

Follow the instructions on how to use the BenchmarkGroup class and obtain training, validation, and test sets, and how to submit your model to the leaderboard.

-

The evaluation metric is AUC. For TCR-Epitope, we provide other metrics as well. See corresponding leaderboards.

-
From 18f7a888f6bdaa8e7a5738da63e92ee19a328ee3 Mon Sep 17 00:00:00 2001 From: Alejandro Velez Arce Date: Sat, 22 Jun 2024 05:15:51 -0400 Subject: [PATCH 3/6] Update index.html counterfactual fix --- benchmark/counterfactual_group/overview/index.html | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/benchmark/counterfactual_group/overview/index.html b/benchmark/counterfactual_group/overview/index.html index 1ac7573c..cab33852 100644 --- a/benchmark/counterfactual_group/overview/index.html +++ b/benchmark/counterfactual_group/overview/index.html @@ -223,13 +223,15 @@

PerturbOutcome Prediction Ben perturbations, aiming to measure model generalization across cell lines and perturbation types. Understanding cellular responses to genetic perturbation is central to numerous biomedical applications, from identifying genetic interactions involved in cancer to developing methods for regenerative -medicine. Furthermore, counterfactual prediction of drug-based perturbations at single-cell -resolution enables cell-type specific drugs and treatments, facilitating precision medicine [ 10]. The230 +medicine. +
+ Furthermore, counterfactual prediction of drug-based perturbations at single-cell +resolution enables cell-type specific drugs and treatments, facilitating precision medicine. The predictive, non-generative task is then formalized as a function of a cell, with corresponding attributes such as cell line, disease, and tissue, and a perturbation, such as a drug type or a CRISPR-based perturbation, which outputs a count for gene expression of the cell after the input pe

-

In TDC-2, we’ve used the scPerturb [ 18 ] datasets for building benchmarks for this task. More details to-be-announced.

+

In TDC-2, we’ve used the scPerturb datasets for building benchmarks for this task. More details to-be-announced.

To access a benchmark in the group, use the following code:

From 255279ca6bc804e3d50682e5856e39f6501afce9 Mon Sep 17 00:00:00 2001 From: Alejandro Velez Arce Date: Sat, 22 Jun 2024 05:17:27 -0400 Subject: [PATCH 4/6] Update index.html fix clinical trial benchmark --- benchmark/clinical_trial/overview/index.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/benchmark/clinical_trial/overview/index.html b/benchmark/clinical_trial/overview/index.html index d0b6e630..07e67ba5 100644 --- a/benchmark/clinical_trial/overview/index.html +++ b/benchmark/clinical_trial/overview/index.html @@ -221,7 +221,7 @@

TrialOutcome Prediction Benchma

Clinical trial outcome prediction is a machine learning task that aims to forecast the outcome of clinical trials, such as the approval rate of a drug or treatment. It utilizes various clinical trial features, including the drug's molecular structure, disease code representing the medical condition, and eligibility criteria that specify participant selection criteria. This task is formulated as a binary classification problem, where the machine learning model predicts whether a clinical trial will have a positive or negative outcome.

-

Our benchmark uses the Trial Outcome Prediction (TOP) dataset \cite{Fu2022}. TOP consists of 17,538 clinical trials with 13,880 small-molecule drugs and 5,335 diseases.

+

Our benchmark uses the Trial Outcome Prediction (TOP) dataset. TOP consists of 17,538 clinical trials with 13,880 small-molecule drugs and 5,335 diseases.

To access a benchmark in the group, use the following code:

From 08ffea27c15d1c0c806cf57823db9fd47101d161 Mon Sep 17 00:00:00 2001 From: Alejandro Velez Arce Date: Sat, 22 Jun 2024 05:39:51 -0400 Subject: [PATCH 5/6] Update index.html fix protein peptide main page --- multi_pred_tasks/proteinpeptide/index.html | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/multi_pred_tasks/proteinpeptide/index.html b/multi_pred_tasks/proteinpeptide/index.html index 0f0341e1..70316be3 100644 --- a/multi_pred_tasks/proteinpeptide/index.html +++ b/multi_pred_tasks/proteinpeptide/index.html @@ -315,11 +315,10 @@

Ye X et al

Dataset Split: Random Split (stratified)

-``` python -from tdc.multi_pred import ProteinPeptide -data = ProteinPeptide(name="brown_mdm2_ace2_12ca5") -data.get_split() -``` +
from tdc.multi_pred import ProteinPeptide 
+data = ProteinPeptide(name = 'brown_mdm2_ace2_12ca5', path = './data')
+split = data.get_split()
+

Note: If listed as a "putative binder," AS-MS alone was used to isolate the ligands to the target, with KD < 1 uM required and often observed in orthogonal assays, though there is some (< 50%) chance that the ligand is nonspecific.

From b5bdf5c6d2d3ac5887bb39d76ec9dc615227cd4f Mon Sep 17 00:00:00 2001 From: Alejandro Velez Arce Date: Sat, 22 Jun 2024 05:45:39 -0400 Subject: [PATCH 6/6] Update index.html fix scperturb --- multi_pred_tasks/counterfactual/index.html | 16 ++++++---------- 1 file changed, 6 insertions(+), 10 deletions(-) diff --git a/multi_pred_tasks/counterfactual/index.html b/multi_pred_tasks/counterfactual/index.html index 07a59ed7..3cd1489b 100644 --- a/multi_pred_tasks/counterfactual/index.html +++ b/multi_pred_tasks/counterfactual/index.html @@ -296,10 +296,8 @@

Counterfactual Prediction Task

Product: Drug Repurposing, Predicting Adverse Drug Reactions, Biopharmaceuticals

Pipeline: Target discovery, Phenotypic Screening.

- -</div> - -### scPerturb + +

scPerturb

Dataset Description: The scPerturb dataset is a comprehensive collection of single-cell perturbation data, harmonized to facilitate the development and benchmarking of computational methods in systems biology. It includes various types of molecular readouts, such as transcriptomics, proteomics, and epigenomics. scPerturb is a harmonized dataset that compiles single-cell perturbation-response data. This dataset is designed to support the development and validation of computational tools by providing a consistent and comprehensive resource. The data includes responses to various genetic and chemical perturbations, which are crucial for understanding cellular mechanisms and developing therapeutic strategies. Data from different sources are uniformly pre-processed to ensure consistency. Rigorous quality control measures are applied to maintain high data quality. Features across different datasets are standardized for easy comparison and integration. @@ -309,12 +307,10 @@

Counterfactual Prediction Task

Dataset Split: Random Split, Seen-unseen splits across cell line and perturbation

-``` python -from tdc.multi_pred.perturboutcome import PerturbOutcome -test_loader = PerturbOutcome( - name="scperturb_drug_SrivatsanTrapnell2020_sciplex2") -testdf = test_loader.get_data() -``` +
from tdc.multi_pred.perturboutcome import PerturbOutcome
+data = PerturbOutcome(name = 'scperturb_drug_SrivatsanTrapnell2020_sciplex2')
+split = data.get_split()
+

References: