Evidence map›Paper›PMID 26072497›Full record

ArticleBioinformatics (Oxford, England)2015

In silico phenotyping via co-training for improved phenotype prediction from genotype.

Damian Roqueiro, Menno J Witteveen, Verneri Anttila, Gisela M Terwindt, Arn M J M van den Maagdenberg, Karsten Borgwardt

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Damian RoqueiroMachine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zurich, Switzerland, Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA, Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA, Department of Neurology and Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands.
Menno J WitteveenMachine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zurich, Switzerland, Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA, Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA, Department of Neurology and Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands.
Verneri AnttilaMachine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zurich, Switzerland, Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA, Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA, Department of Neurology and Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands Machine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zurich, Switzerland, Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA, Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA, Department of Neurology and Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands Machine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zurich, Switzerland, Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA, Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA, Department of Neurology and Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands.
Gisela M TerwindtMachine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zurich, Switzerland, Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA, Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA, Department of Neurology and Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands.
Arn M J M van den MaagdenbergMachine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zurich, Switzerland, Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA, Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA, Department of Neurology and Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands Machine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zurich, Switzerland, Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA, Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA, Department of Neurology and Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands.
Karsten BorgwardtMachine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zurich, Switzerland, Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA, Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA, Department of Neurology and Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

motivationPredicting disease phenotypes from genotypes is a key challenge in medical applications in the postgenomic era. Large training datasets of patients that have been both genotyped and phenotyped are the key requisite when aiming for high prediction accuracy. With current genotyping projects producing genetic data for hundreds of thousands of patients, large-scale phenotyping has become the bottleneck in disease phenotype prediction.

resultsHere we present an approach for imputing missing disease phenotypes given the genotype of a patient. Our approach is based on co-training, which predicts the phenotype of unlabeled patients based on a second class of information, e.g. clinical health record information. Augmenting training datasets by this type of in silico phenotyping can lead to significant improvements in prediction accuracy. We demonstrate this on a dataset of patients with two diagnostic types of migraine, termed migraine with aura and migraine without aura, from the International Headache Genetics Consortium.

conclusionsImputing missing disease phenotypes for patients via co-training leads to larger training datasets and improved prediction accuracy in phenotype prediction. AVAILABILITY AND IMPLEMENTATION: The code can be obtained at: http://www.bsse.ethz.ch/mlcb/research/bioinformatics-and-computational-biology/co-training.html

Indexed as

Computer SimulationPhenotypeAlgorithmsDiseaseGenotypeGenotyping TechniquesHumansMigraine with AuraMigraine without Aura

Identifiers

PMID26072497
PMCPMC4765855

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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.