Summary of collection of models that make predictions for a drugs ability to penetrate the blood brain barrier using Therapeutic Data Commons BBB-Martins dataset

(Deprecated; models have been moved to Hugging Face)

This contains data for the Trojan Detection Track of TDC 2023 (LLM Edition), a NeurIPS 2023 competition. To learn more about the competition, please see the competition website.

In particular, this is the folder containing the model and training data for the Large Subtrack.

Therapeutics Data Commons

Therapeutics Data Commons(TDC) provides a publicly available collection of 22 machine learning tasks for therapeutic discovery. Our Hugging Face repository is a mirror of single-instance prediction tasks of TDC, encompassing a total of 46,265,659 data points. The parquet files uploaded to our Hugging Face repository have been sanitized and curated from the original datasets.

Each parquet file corresponds to a separate category (e.g., ADME) and contains… See the full description on the dataset page: https://huggingface.co/datasets/maomlab/TDC.

Training data used in TDC.

Paper Link: https://arxiv.org/pdf/2504.10443 Project Page: https://hoar012.github.io/TDC-Project Code: https://github.com/Hoar012/TDC-Video This repository contains the data used for training and evaluation in Multimodal Long Video Modeling Based on Temporal Dynamic Context.

  Citation

@misc{hao2025multimodallongvideomodeling, title={Multimodal Long Video Modeling Based on Temporal Dynamic Context}, author={Haoran Hao and… See the full description on the dataset page: https://huggingface.co/datasets/Hoar012/TDC_training_data.

A series of isostructural 3D coordination polymers 3∞M(tdc)(bpy) was synthesized and characterized by X-ray diffraction, thermal analysis, and gas adsorption measurements. The materials show high thermal stability up to approximately 400 °C and a solvent induced phase transition. Single crystal X-ray structure determination was successfully performed for all compounds after the phase transition. In the zinc-based coordination polymer, various amounts of a second type of metal ions such as Co2+ or Fe2+ could be incorporated. Furthermore, the catalytic behavior of the homo- and heteronuclear 3D coordination polymers in an oxidation model reaction was investigated.

Ensemble predictions for a drugs ability to penetrate the blood brain barrier using Therapeutic Data Commons BBB-Martins dataset.

Includes .tsv files with the actual and predicted classifications of BBB permeability for each drug in the TDC test set using a Ensemble model that takes validation set predictions from Logistic Regression and Deep Neural Network models as base learners. Note that a vlassification label of 0 means not-permeable and a label of 1 means the drug is BBB permeable.

Total-Current-Assets Time Series for TDC Software Engineering Inc. TDC SOFT Inc. provides IT consulting services in Japan. The company provides Agile, UX design, scrum training, scaled Agile framework, security, artificial intelligence consulting, and system integration infrastructure services and solutions. It also offers Stlyeflow, a cloud application for creating a web application form; Meepa, an EDI cloud system that digitizes transactions with partner companies; M-check+, a cloud application for stress check; and BP-LINKS, an EdI service that makes ordering, and monthly billing and settlement paperless. In addition, the company provides HuTaCT, a human resource talent management system; Trustpro, a cloud-based development framework; HANDyTRUSt application to support work efficiency and speedup related to work reports; and MoobizSync, a tool to browse Salesforce from mobile. The company was founded in 1962 and is headquartered in Tokyo, Japan.

Jmrludan/full-properties-sample-tdc dataset hosted on Hugging Face and contributed by the HF Datasets community

Logistic Regression predictions for a drugs ability to penetrate the blood brain barrier using Therapeutic Data Commons BBB-Martins dataset.

Includes .tsv files with the actual and predicted classifications of BBB permeability for each drug in the TDC test set using a Logistic Regression model. Note that a vlassification label of 0 means not-permeable and a label of 1 means the drug is BBB permeable.

Enterprise-Value-To-Sales-Ratio Time Series for TDC Software Engineering Inc. TDC SOFT Inc. provides IT consulting services in Japan. The company provides Agile, UX design, scrum training, scaled Agile framework, security, artificial intelligence consulting, and system integration infrastructure services and solutions. It also offers SAFe, a product owner / product manager; UNIX server migration service; ASTERIA Warp, a domestic data integration tool that connects various systems in a non-coding manner; amazon web services; M-check+, a cloud application for stress check; and BP-LINKS, an EdI service that makes ordering, and monthly billing and settlement paperless. In addition, the company provides HuTaCT, a human resource talent management system; Trustpro, a cloud-based development framework; GitLab, a DevOps platform that allows you to manage version control, code review, and CI/CD in a single application; OpenCanvas; oracle ERP related services; PC procurement kitting service; progress corticon, Qlik sense; salesforce related services; SAP related services; total security services, Tegata; Trustpro; and WinActor, a software-type robot. The company was founded in 1962 and is headquartered in Chiyoda-ku, Japan.

Deep Neural Network predictions for a drugs ability to penetrate the blood brain barrier using Therapeutic Data Commons BBB-Martins dataset.

Includes .tsv files with the actual and predicted classifications of BBB permeability for each drug in the TDC test set using a Deep Neural Network model. Note that a vlassification label of 0 means not-permeable and a label of 1 means the drug is BBB permeable.

Random Forest predictions for a drugs ability to penetrate the blood brain barrier using Therapeutic Data Commons BBB-Martins dataset.

Includes .tsv files with the actual and predicted classifications of BBB permeability for each drug in the TDC test set using a Random Forest model. Note that a vlassification label of 0 means not-permeable and a label of 1 means the drug is BBB permeable.

TDC PAMPA NCATS

PAMPA NCATS dataset [1], part of TDC [2] benchmark. It is intended to be used through scikit-fingerprints library. NCATS subset of PAMPA dataset. PAMPA (parallel artificial membrane permeability assay) is an assay to evaluate drug permeability across the cellular membrane. The task models only the passive membrane diffusion. This is “NCATS” subset of the dataset created at National Center for Advancing Translational Sciences (NCATS). This dataset is a part of… See the full description on the dataset page: https://huggingface.co/datasets/scikit-fingerprints/TDC_pampa_ncats.

Therapeutics Data Commons (TDC) is an open-science initiative started at Harvard with AI/ML-ready datasets and ML tasks for therapeutics. It provides an ecosystem of tools, leaderboards, and community resources, including data functions, model benchmarking and comparison strategies, meaningful data splits, data processors, public leaderboards, and molecule generation oracles. All resources are integrated and accessible via an open Python library. TDC is available at https://tdcommons.ai.

Tristan Da Cunha, Ground-based Vector Magnetic Field Level 2 Data, 1.0 min Time Resolution, Station Code: (TDC), Station Location: (GEO Latitude -37.1, Longitude 347.7), DTU Network

A dataset of public corporate filings (such as annual reports, quarterly reports, and ad-hoc disclosures) for TDC SOFT Inc. (4687), provided by FinancialReports.eu.

lhkhiem28/TDC dataset hosted on Hugging Face and contributed by the HF Datasets community

Comprehensive YouTube channel statistics for TDC, featuring 3,320,000 subscribers and 1,819,165,661 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Gaming category and is based in CA. Track 1,494 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

BackgroundGlobally, esophageal squamous cell carcinoma (ESCC) stands out as a common cancer type, characterized by its notably high rates of occurrence and mortality. Recent advancements in treatment methods, including immunotherapy, have shown promise, yet the prognosis remains poor. In the context of tumor development and treatment outcomes, the tumor microenvironment (TME), especially the function of dendritic cells (DCs), is significantly influential. Our study aims to delve deeper into the heterogeneity of DCs in ESCC using single-cell RNA sequencing (scRNA-seq) and bulk RNA analysis.MethodsIn the scRNA-seq analysis, we utilized the SCP package for result visualization and functional enrichment analysis of cell subpopulations. CellChat was employed to identify potential oncogenic mechanisms in DCs, while Monocle 2 traced the evolutionary trajectory of the three DC subtypes. CopyKAT assessed the benign or malignant nature of cells, and SCENIC conducted transcription factor regulatory network analysis, offering a preliminary exploration of DC heterogeneity. In Bulk-RNA analysis, we constructed a prognostic model for ESCC prognosis and immunotherapy response, based on DC marker genes. This model was validated through quantitative PCR (qPCR) and immunohistochemistry (IHC), confirming the gene expression levels.ResultsIn this study, through intercellular communication analysis, we identified GALECTIN and MHC-I signaling pathways as potential oncogenic mechanisms within dendritic cells. We categorized DCs into three subtypes: plasmacytoid (pDC), conventional (cDC), and tolerogenic (tDC). Our findings revealed that pDCs exhibited an increased proportion of cells in the G2/M and S phases, indicating enhanced cellular activity. Pseudotime trajectory analysis demonstrated that cDCs were in early stages of differentiation, whereas tDCs were in more advanced stages, with pDCs distributed across both early and late differentiation phases. Prognostic analysis highlighted a significant correlation between pDCs and tDCs with the prognosis of ESCC (P< 0.05), while no significant correlation was observed between cDCs and ESCC prognosis (P = 0.31). The analysis of cell malignancy showed the lowest proportion of malignant cells in cDCs (17%), followed by pDCs (29%), and the highest in tDCs (48%), with these results being statistically significant (P< 0.05). We developed a robust ESCC prognostic model based on marker genes of pDCs and tDCs in the GSE53624 cohort (n = 119), which was validated in the TCGA-ESCC cohort (n = 139) and the IMvigor210 immunotherapy cohort (n = 298) (P< 0.05). Additionally, we supplemented the study with a novel nomogram that integrates clinical features and risk assessments. Finally, the expression levels of genes involved in the model were validated using qPCR (n = 8) and IHC (n = 16), thereby confirming the accuracy of our analysis.ConclusionThis study enhances the understanding of dendritic cell heterogeneity in ESCC and its impact on patient prognosis. The insights gained from scRNA-seq and Bulk-RNA analysis contribute to the development of novel biomarkers and therapeutic targets. Our prognostic models based on DC-related gene signatures hold promise for improving ESCC patient stratification and guiding treatment decisions.

Abstract Objective To evaluate the antitumoral role of γδ TDC cells and αβ TDC cells in an experimental model of breast cancer. Methods Thirty female Balb/c mice were divided into 2 groups: control group (n=15) and induced-4T1 group (n=15), in which the mice received 2 x 105 4T1 mammary tumor cell line. Following the 28-day experimental period, immune cells were collected from the spleen and analyzed by flow cytometry for comparison of αβ TDC (TCRαβ+ CD11c+MHCII+) and γδ TDC (TCRγδ+CD11c+MHCII+) cells regarding surface markers (CD4+ and C8+) and cytokines (IFN-γ, TNF-α, IL-12 and IL-17). Results A total of 26.53% of γδ TDC- control group (p