ArticleNucleic acids research2015
Sparse expression bases in cancer reveal tumor drivers.
Article in Nucleic acids research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.
- Meta-Analysis of the Alzheimer's Disease Human Brain Transcriptome and Functional Dissection in Mouse Models.Cell reports · 2020Pooled it
- Integrative Computational Framework,bioRxiv : the preprint server for biology · 2024Article
- Bayesian Machine Learning Enables Identification of Transcriptional Network Disruptions Associated with Drug-Resistant Prostate Cancer.Cancer research · 2023Article
- SimiC enables the inference of complex gene regulatory dynamics across cell phenotypes.Communications biology · 2022Article
- Multi-tissue neocortical transcriptome-wide association study implicates 8 genes across 6 genomic loci in Alzheimer's disease.Genome medicine · 2021Article
- ComHub: Community predictions of hubs in gene regulatory networks.BMC bioinformatics · 2021Article
- Review
- Imputation of Gene Expression Data in Blood Cancer and Its Significance in Inferring Biological Pathways.Frontiers in oncology · 2019Article
- Genome-wide RNAseq study of the molecular mechanisms underlying microglia activation in response to pathological tau perturbation in the rTg4510 tau transgenic animal model.Molecular neurodegeneration · 2018Article
- A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia.Nature communications · 2018Article
- Article
- Driver pattern identification over the gene co-expression of drug response in ovarian cancer by integrating high throughput genomics data.Scientific reports · 2017Article
- Extracting a low-dimensional description of multiple gene expression datasets reveals a potential driver for tumor-associated stroma in ovarian cancer.Genome medicine · 2016Article
- Leveraging global gene expression patterns to predict expression of unmeasured genes.BMC genomics · 2015Article
- Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
We define a new category of candidate tumor drivers in cancer genome evolution: 'selected expression regulators' (SERs)-genes driving dysregulated transcriptional programs in cancer evolution. The SERs are identified from genome-wide tumor expression data with a novel method, namely SPARROW ( SPAR: se selected exp R: essi O: n regulators identified W: ith penalized regression). SPARROW uncovers a previously unknown connection between cancer expression variation and driver events, by using a novel sparse regression technique. Our results indicate that SPARROW is a powerful complementary approach to identify candidate genes containing driver events that are hard to detect from sequence data, due to a large number of passenger mutations and lack of comprehensive sequence information from a sufficiently large number of samples. SERs identified by SPARROW reveal known driver mutations in multiple human cancers, along with known cancer-associated processes and survival-associated genes, better than popular methods for inferring gene expression networks. We demonstrate that when applied to acute myeloid leukemia expression data, SPARROW identifies an apoptotic biomarker (PYCARD) for an investigational drug obatoclax. The PYCARD and obatoclax association is validated in 30 AML patient samples.
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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.