ReviewTheranostics2021
Computational methods for cancer driver discovery: A survey.
Review in Theranostics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- PICDGI: A framework for predicting cancer driver genes through dynamic gene-gene interaction modeling of single-cell data.PLoS computational biology · 2026Article
- A core driver gene set identified based on geMER reveals its potential driver mechanism in pan-cancer.NPJ precision oncology · 2025Article
- Integrating gene mutation spectra from tumors and the general population with gene expression topological networks to identify novel cancer driver genes.Human genetics · 2025Article
- Decoding the functional impact of the cancer genome through protein-protein interactions.Nature reviews. Cancer · 2025Review
- A novel hypergraph model for identifying and prioritizing personalized drivers in cancer.PLoS computational biology · 2024Article
- CMC: Cancer miRNA Census - a list of cancer-related miRNA genes.Nucleic acids research · 2024Article
- A workflow to study mechanistic indicators for driver gene prediction with Moonlight.Briefings in bioinformatics · 2023Article
- High-Throughput Identification, Modeling, and Analysis of Cancer Driver Genes In Vivo.Cold Spring Harbor perspectives in medicine · 2023Review
- Dynamic cancer drivers: a causal approach for cancer driver discovery based on bio-pathological trajectories.Briefings in functional genomics · 2022Article
- A New View of Activating Mutations in Cancer.Cancer research · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying the genes responsible for driving cancer is of critical importance for directing treatment. Accordingly, multiple computational tools have been developed to facilitate this task. Due to the different methods employed by these tools, different data considered by the tools, and the rapidly evolving nature of the field, the selection of an appropriate tool for cancer driver discovery is not straightforward. This survey seeks to provide a comprehensive review of the different computational methods for discovering cancer drivers. We categorise the methods into three groups; methods for single driver identification, methods for driver module identification, and methods for identifying personalised cancer drivers. In addition to providing a "one-stop" reference of these methods, by evaluating and comparing their performance, we also provide readers the information about the different capabilities of the methods in identifying biologically significant cancer drivers. The biologically relevant information identified by these tools can be seen through the enrichment of discovered cancer drivers in GO biological processes and KEGG pathways and through our identification of a small cancer-driver cohort that is capable of stratifying patient survival.
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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.