ArticleBioinformatics (Oxford, England)2017
CIIPro: a new read-across portal to fill data gaps using public large-scale chemical and biological data.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed.
- ToxiVerse: chemical bioprofiling, toxicity data sharing and customizable predictive modeling.Bioinformatics (Oxford, England) · 2026Article
- ToxiVerse: A Public Platform for Chemical Toxicity Data Sharing and Customizable Predictive Modeling.bioRxiv : the preprint server for biology · 2026Article
- "intelligent Read Across (iRA)"- A tool for read-across-based toxicity prediction of nanoparticles.Computational and structural biotechnology journal · 2025Article
- Integrating Concentration-Dependent Toxicity Data and Toxicokinetics To Inform Hepatotoxicity Response Pathways.Environmental science & technology · 2023Article
- Data-Driven Quantitative Structure-Activity Relationship Modeling for Human Carcinogenicity by Chronic Oral Exposure.Environmental science & technology · 2023Article
- Mechanism-driven modeling of chemical hepatotoxicity using structural alerts and an in vitro screening assay.Journal of hazardous materials · 2022Article
- Predicting Prenatal Developmental Toxicity Based On the Combination of Chemical Structures and Biological Data.Environmental science & technology · 2022Article
- Comparing the performance and coverage of selectedComputational toxicology (Amsterdam, Netherlands) · 2022Article
- High-Throughput Screening Assay Profiling for Large Chemical Databases.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Construction of a Virtual Opioid Bioprofile: A Data-Driven QSAR Modeling Study to Identify New Analgesic Opioids.ACS sustainable chemistry & engineering · 2021Article
- Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling.Drug discovery today · 2020Review
- Mechanism-Driven Read-Across of Chemical Hepatotoxicants Based on Chemical Structures and Biological Data.Toxicological sciences : an official journal of the Society of Toxicology · 2020Article
- Big Data and Artificial Intelligence Modeling for Drug Discovery.Annual review of pharmacology and toxicology · 2020Review
- Using a hybrid read-across method to evaluate chemical toxicity based on chemical structure and biological data.Ecotoxicology and environmental safety · 2019Article
- Exploring current read-across applications and needs among selected U.S. Federal Agencies.Regulatory toxicology and pharmacology : RTP · 2019Article
- Advancing Computational Toxicology in the Big Data Era by Artificial Intelligence: Data-Driven and Mechanism-Driven Modeling for Chemical Toxicity.Chemical research in toxicology · 2019Review
- Linking Bisphenol S to Adverse Outcome Pathways Using a Combined Text Mining and Systems Biology Approach.Environmental health perspectives · 2019Article
- Nonanimal Models for Acute Toxicity Evaluations: Applying Data-Driven Profiling and Read-Across.Environmental health perspectives · 2019Article
- Comparing Multiple Machine Learning Algorithms and Metrics for Estrogen Receptor Binding Prediction.Molecular pharmaceutics · 2018Article
- Big-data and machine learning to revamp computational toxicology and its use in risk assessment.Toxicology research · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Summary: We have developed a public Chemical In vitro–In vivo Profiling (CIIPro) portal, which can automatically extract in vitro biological data from public resources (i.e. PubChem) for user-supplied compounds. For compounds with in vivo target activity data (e.g. animal toxicity testing results), the integrated cheminformatics algorithm will optimize the extracted biological data using in vitro–in vivo correlations. The resulting in vitro biological data for target compounds can be used for read-across risk assessment of target compounds. Additionally, the CIIPro portal can identify the most similar compounds based on their optimized bioprofiles. The CIIPro portal provides new powerful assessment capabilities to the scientific community and can be easily integrated with other cheminformatics tools. Availability and Implementation: ciipro.rutgers.edu. Contact: danrusso@scarletmail.rutgers.edu or hao.zhu99@rutgers.edu
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.