ArticleiScience2020
Tumor Phylogeny Topology Inference via Deep Learning.
Article in iScience, 2020. 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.
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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.
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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.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep representation learning for temporal inference in cancer omics: a systematic literature review.Briefings in bioinformatics · 2026Pooled it
- Statistical Inconsistency of Error-correction Objectives for Perfect Phylogenies.bioRxiv : the preprint server for biology · 2026Article
- Dolphyin: a combinatorial algorithm for identifying 1-Dollo phylogenies in cancer.Algorithms for molecular biology : AMB · 2026Article
- Single-cell omics data-driven decoding of tumor clonal evolution through reinforcement learning.Genome medicine · 2026Article
- Resolving tumor evolution: a phylogenetic approach.Journal of the National Cancer Center · 2024Review
- Current progress and open challenges for applying deep learning across the biosciences.Nature communications · 2022Review
- CellPhy: accurate and fast probabilistic inference of single-cell phylogenies from scDNA-seq data.Genome biology · 2022Article
- Studying the History of Tumor Evolution from Single-Cell Sequencing Data by Exploring the Space of Binary Matrices.Journal of computational biology : a journal of computational molecular cell biology · 2021Article
- Distinguishing linear and branched evolution given single-cell DNA sequencing data of tumors.Algorithms for molecular biology : AMB · 2021Article
- Incorporating Machine Learning into Established Bioinformatics Frameworks.International journal of molecular sciences · 2021Review
- Machine Intelligence in Single-Cell Data Analysis: Advances and New Challenges.Frontiers in genetics · 2021Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Principled computational approaches for tumor phylogeny reconstruction via single-cell sequencing typically aim to build the most likely perfect phylogeny tree from the noisy genotype matrix - which represents genotype calls of single cells. This problem is NP-hard, and as a result, existing approaches aim to solve relatively small instances of it through combinatorial optimization techniques or Bayesian inference. As expected, even when the goal is to infer basic topological features of the tumor phylogeny, rather than reconstructing the topology entirely, these approaches could be prohibitively slow. In this paper, we introduce fast deep learning solutions to the problems of inferring whether the most likely tree has a linear (chain) or branching topology and whether a perfect phylogeny is feasible from a given genotype matrix. We also present a reinforcement learning approach for reconstructing the most likely tumor phylogeny. This preliminary work demonstrates that data-driven approaches can reconstruct key features of tumor evolution.
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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.