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Model-based branching point detection in single-cell data by K-branches clustering

Bioinformatics(2017)10.1093/bioinformatics/btx325Source: DataRank Database

Model-based branching point detection in single-cell data by K-branches clustering is a research paper published in Bioinformatics (2017). On theSindex it has a DataRank of 1.0. It has been cited 17 times, with 15 citing works in its 1-hop citation network.

N/A

1.0DataRank · unranked

Scored on demand from live citation data

Open Access17 citations · 15 citing works in its network

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Linked data & code

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (1 grants)Impact (FWCI 0.66)OA: bronzeTopics (37)IDs (PubMed, PMC, OpenAlex)Annotations (1)SDGs (1)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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DataRank Breakdown

Base Score 42%Citation Network 58%

Base Score Contribution

0.434

From this paper's citation signal

Citation Network Contribution

0.588

From 13 citing papers with measurable signal

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Top 5 citers driving the network score

Ranked by each citer's contribution to N(p) — log1p(C_q) divided by its reference count — out of 15 citers.

SCC: an accurate imputation method for scRNA-seq dropouts based on a mixture model
BMC Bioinformatics202119 citations16 referencesContributes 0.187
Defining murine organogenesis at single-cell resolution reveals a role for the leukotriene pathway in regulating blood progenitor formation
Nature Cell Biology2017139 citations47 referencesContributes 0.105
Single cell imaging-based chromatin biomarkers for tumor progression
Scientific Reports202119 citations55 referencesContributes 0.054
Actomyosin contractility as a mechanical checkpoint for cell state transitions
Scientific Reports20226 citations36 referencesContributes 0.054
scAMAC: self-supervised clustering of scRNA-seq data based on adaptive multi-scale autoencoder
Briefings in Bioinformatics202417 citations54 referencesContributes 0.054

Authors (4)

Francesca Finotello ORCID,Fabian Joachim Theis ORCID,Florian Wolf ORCID,Nikolaos K. Chlis

Live enrichment

PubMed:28582478 PMC:PMC5860029 OpenAlex:https://openalex.org/W2951876422

Deutsche Forschungsgemeinschaft

Grant: 278529602/SFB 1243

Genetic and Epigenetic Evolution of Hematopoietic Neoplasms

FWCI

0.66

Citation Percentile

0.7%

Citation Trend

2017

2018

2020

2021

2022

2023

2024

2026

bronzeLicense: CC BY NC ND

journalhttps://academic.oup.com/bioinformatics/article-pdf/33/20/3211/25165588/btx325.pdf

publisherhttps://academic.oup.com/bioinformatics/article-pdf/33/20/3211/25165588/btx325.pdf

journalhttps://doi.org/10.1093/bioinformatics/btx325

repositoryhttps://pubmed.ncbi.nlm.nih.gov/28582478

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/5860029

Fields of Study

Single-cell and spatial transcriptomicsGene expression and cancer classificationCancer Genomics and Diagnostics0301 basic medicine0303 health sciences03 medical and health sciences

MeSH Terms

AlgorithmsAnimalsCell DifferentiationHumansModels, BiologicalSoftwareCluster AnalysisSequence Analysis, RNAGene Expression ProfilingMiceSingle-Cell Analysis

Keywords

Computer scienceCluster analysisComputational biologyInferenceBranching (polymer chemistry)Data miningAlgorithmBiologyArtificial intelligenceSequence Analysis, RNAGene Expression ProfilingCell DifferentiationOriginal PapersModels, BiologicalMiceAnimalsHumansSingle-Cell AnalysisAlgorithmsSoftware

Sustainable Development Goals

SDG 0: Partnerships for the goals