ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2019
PLATYPUS: A Multiple-View Learning Predictive Framework for Cancer Drug Sensitivity Prediction.
Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Effectiveness of Artificial Intelligence for Personalized Medicine in Neoplasms: A Systematic Review.BioMed research international · 2022Pooled it
- A fair experimental comparison of neural network architectures for latent representations of multi-omics for drug response prediction.BMC bioinformatics · 2023Article
- DeepInsight-3D architecture for anti-cancer drug response prediction with deep-learning on multi-omics.Scientific reports · 2023Article
- Prediction of drug sensitivity based on multi-omics data using deep learning and similarity network fusion approaches.Frontiers in bioengineering and biotechnology · 2023Article
- Identification of phenocopies improves prediction of targeted therapy response over DNA mutations alone.NPJ genomic medicine · 2022Article
- Predicting breast cancer drug response using a multiple-layer cell line drug response network model.BMC cancer · 2021Article
- Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions.Medicinal research reviews · 2021Review
- Data integration for prediction of weight loss in randomized controlled dietary trials.Scientific reports · 2020Article
- Contributions from the 2019 Literature on Bioinformatics and Translational Informatics.Yearbook of medical informatics · 2020Review
- Artificial Intelligence (AI)-Based Systems Biology Approaches in Multi-Omics Data Analysis of Cancer.Frontiers in oncology · 2020Review
- MOLI: multi-omics late integration with deep neural networks for drug response prediction.Bioinformatics (Oxford, England) · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Cancer is a complex collection of diseases that are to some degree unique to each patient. Precision oncology aims to identify the best drug treatment regime using molecular data on tumor samples. While omics-level data is becoming more widely available for tumor specimens, the datasets upon which computational learning methods can be trained vary in coverage from sample to sample and from data type to data type. Methods that can 'connect the dots' to leverage more of the information provided by these studies could offer major advantages for maximizing predictive potential. We introduce a multi-view machinelearning strategy called PLATYPUS that builds 'views' from multiple data sources that are all used as features for predicting patient outcomes. We show that a learning strategy that finds agreement across the views on unlabeled data increases the performance of the learning methods over any single view. We illustrate the power of the approach by deriving signatures for drug sensitivity in a large cancer cell line database. Code and additional information are available from the PLATYPUS website https://sysbiowiki.soe.ucsc.edu/platypus.
Indexed as
Identifiers
30864317PMC6417802What 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.