ArticleF1000Research2015
DREAMTools: a Python package for scoring collaborative challenges.
Article in F1000Research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Transcriptome data are insufficient to control false discoveries in regulatory network inference.Cell systems · 2024Article
- Technologies for whole-cell modeling: Genome-wide reconstruction of a cell in silico.Development, growth & differentiation · 2023Review
- Computational approaches to understand transcription regulation in development.Biochemical Society transactions · 2023Review
- Fast and accurate inference of gene regulatory networks through robust precision matrix estimation.Bioinformatics (Oxford, England) · 2022Article
- PEPN-GRN: A Petri net-based approach for the inference of gene regulatory networks from noisy gene expression data.PloS one · 2021Article
- Data Science Approaches for Effective Use of Mobile Device-Based Collection of Real-World Data.Clinical pharmacology and therapeutics · 2020Article
- Robust network inference using response logic.Bioinformatics (Oxford, England) · 2019Article
- Crowdsourcing biomedical research: leveraging communities as innovation engines.Nature reviews. Genetics · 2016Review
- Inferring causal molecular networks: empirical assessment through a community-based effort.Nature methods · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
22 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
unlabelledDREAM challenges are community competitions designed to advance computational methods and address fundamental questions in system biology and translational medicine. Each challenge asks participants to develop and apply computational methods to either predict unobserved outcomes or to identify unknown model parameters given a set of training data. Computational methods are evaluated using an automated scoring metric, scores are posted to a public leaderboard, and methods are published to facilitate community discussions on how to build improved methods. By engaging participants from a wide range of science and engineering backgrounds, DREAM challenges can comparatively evaluate a wide range of statistical, machine learning, and biophysical methods. Here, we describe DREAMTools, a Python package for evaluating DREAM challenge scoring metrics. DREAMTools provides a command line interface that enables researchers to test new methods on past challenges, as well as a framework for scoring new challenges. As of March 2016, DREAMTools includes more than 80% of completed DREAM challenges. DREAMTools complements the data, metadata, and software tools available at the DREAM website http://dreamchallenges.org and on the Synapse platform at https://www.synapse.org. AVAILABILITY: DREAMTools is a Python package. Releases and documentation are available at http://pypi.python.org/pypi/dreamtools. The source code is available at http://github.com/dreamtools/dreamtools.
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.