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A Markov random field model-based approach for differentially expressed gene detection from single-cell RNA-seq data

Briefings in Bioinformatics(2022)10.1093/bib/bbac166Source: DataRank Database

A Markov random field model-based approach for differentially expressed gene detection from single-cell RNA-seq data is a research paper published in Briefings in Bioinformatics (2022). On theSindex it has a DataRank of 0.323. It has been cited 6 times, with 4 citing works in its 1-hop citation network.

N/A

0.323DataRank · unranked

Scored on demand from live citation data

Open Access6 citations · 4 citing works in its network

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (15 grants)Impact (FWCI 0.47)OA: hybridTopics (53)IDs (PubMed, PMC, OpenAlex)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 90%Citation Network 10%

Base Score Contribution

0.292

From this paper's citation signal

Citation Network Contribution

0.0308

From 3 citing papers with measurable signal

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Top 4 citers driving the network score

Ranked by each citer's contribution to N(p) — log1p(C_q) divided by its reference count — out of 4 citers.

Recent advances in differential expression analysis for single-cell RNA-seq and spatially resolved transcriptomic studies
Briefings in Functional Genomics202320 citations212 referencesContributes 0.014
Immune Profiling Among Colorectal Cancer Subtypes Using Dependent Mixture Models
Journal of the American Statistical Association20241 citations54 referencesContributes 0.013
Single-cell RNA sequencing reveals the transcriptional heterogeneity of Tbx18-positive cardiac cells during heart development
Functional & Integrative Genomics20242 citations121 referencesContributes 0.009
Artificial Intelligence and Machine Learning of Single‐Cell Transcriptomics of Engineered Nanoparticles
20230 citations38 referencesContributes 0.000

Authors (5)

Hongyu Li ORCID,Le Zhang ORCID,Sreeganga S. Chandra ORCID,Luca Pinello ORCID,Biqing Zhu ORCID

Live enrichment

PubMed:35514182 PMC:PMC9487630 OpenAlex:https://openalex.org/W4226113200

National Cancer Institute

Grant: P50CA196530

National Science Foundation

Grant: DMS 1902903

National Institutes of Health

Grant: R01GM134005

National Institutes of Health

Grant: R56 AG074015

National Institutes of Health

Grant: RF1 NS110354

Aligning Science Across Parkinson’s

Grant: ASAP-000529

Tracing the Origin and Progression of Parkinson’s Disease through the Neuro-Immune Interactome

NIDA Neuroproteomic Center

Grant: DA018343-11A1

NIDA NIH HHS

Grant: P30 DA018343

NCATS NIH HHS

Grant: UL1 TR001863

National Institutes of Health

Grant: 1R56AG074015-01

Sex-Specific Single Cell Expression Profiles, Genetic Risk and Drug Responsiveness in Alzheimer's Disease

National Science Foundation

Grant: 1902903

New Statistical Methods for High-Dimensional Association Tests with Applications to Large-Scale Genetic Data

National Institutes of Health

Grant: 3P50CA196530-03S1

MicroRNA-based interventions to prevent progression from lung preneoplasia to adenocarcinoma

National Institutes of Health

Grant: 1R01GM134005-01A1

Novel statistical methods and tools to integrate multiple endophenotypes and functional annotation data to study the roles of rare variants in complex human diseases using sequencing data

National Institutes of Health

Grant: 5P30DA018343-14

Yale/NIDA Neuroproteomics Center

National Institutes of Health

Grant: 3RF1NS110354-01S1

Role of Glucosylsphingosine in Parkinson Disease and Dementia with Lewy Bodies

FWCI

0.47

Citation Percentile

0.6%

Citation Trend

2023

2024

hybridLicense: cc-by

journalhttps://academic.oup.com/bib/article-pdf/23/5/bbac166/45937268/bbac166.pdf

publisherhttps://academic.oup.com/bib/article-pdf/23/5/bbac166/45937268/bbac166.pdf

journalhttps://doi.org/10.1093/bib/bbac166

repositoryhttps://pubmed.ncbi.nlm.nih.gov/35514182

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/9487630

Fields of Study

Single-cell and spatial transcriptomicsGene expression and cancer classificationGene Regulatory Network Analysis0206 medical engineering02 engineering and technologyAnimalsGene Expression ProfilingHumansLipopolysaccharidesMiceRNARNA-SeqSequence Analysis, RNASingle-Cell Analysis

MeSH Terms

RNA-SeqAnimalsHumansLipopolysaccharidesRNASequence Analysis, RNAGene Expression ProfilingMiceSingle-Cell Analysis

Keywords

GeneComputational biologyRNA-SeqMarkov random fieldMarkov chainComputer scienceCell typeSet (abstract data type)BiologyData setHidden Markov modelRNACellGeneticsGene expressionArtificial intelligenceMachine learningTranscriptomeDifferential expressionParkinson’s DiseaseScrna-seqPseudobulkLipopolysaccharidesMiceSequence Analysis, RNAGene Expression ProfilingProblem Solving ProtocolAnimalsHumansSingle-Cell Analysis

Sustainable Development Goals

SDG 3: 3. Good health