On the public citation graph

Deep learning of representations for transcriptomics-based phenotype prediction

0 signed · 64 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-1-4757-3462-1openalex-snapshot

Regression Modeling Strategies

inferredcites →https://doi.org/10.1007/978-1-4939-7493-1_14openalex-snapshot

Bioinformatics Approaches to Predict Drug Responses from Genomic Sequencing

inferredcites →https://doi.org/10.1007/s12064-012-0162-3openalex-snapshot

Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples

inferredcites →https://doi.org/10.1016/j.cell.2018.02.052openalex-snapshot

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

inferredcites →https://doi.org/10.1016/j.immuni.2014.04.019openalex-snapshot

Activation of the Aryl Hydrocarbon Receptor Dampens the Severity of Inflammatory Skin Conditions

inferredcites →https://doi.org/10.1016/j.jaci.2015.03.003openalex-snapshot

RNA sequencing atopic dermatitis transcriptome profiling provides insights into novel disease mechanisms with potential therapeutic implications

inferredcites →https://doi.org/10.1016/s1672-0229(06)60022-3openalex-snapshot

Improve Survival Prediction Using Principal Components of Gene Expression Data

inferredcites →https://doi.org/10.1021/acs.molpharmaceut.5b00982openalex-snapshot

Applications of Deep Learning in Biomedicine

inferredcites →https://doi.org/10.1038/75556openalex-snapshot

Gene Ontology: tool for the unification of biology

inferredcites →https://doi.org/10.1038/nbt.2702openalex-snapshot

Reproducibility of high-throughput mRNA and small RNA sequencing across laboratories

inferredcites →https://doi.org/10.1038/nbt.3000openalex-snapshot

Detecting and correcting systematic variation in large-scale RNA sequencing data

inferredcites →https://doi.org/10.1038/nbt.3838openalex-snapshot

Reproducible RNA-seq analysis using recount2

inferredcites →https://doi.org/10.1038/ncomms13427openalex-snapshot

In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development

inferredcites →https://doi.org/10.1038/ng.2653openalex-snapshot

The Genotype-Tissue Expression (GTEx) project

inferredcites →https://doi.org/10.1038/nmeth.1439openalex-snapshot

Cell type–specific gene expression differences in complex tissues

inferredcites →https://doi.org/10.1038/s41467-018-03751-6openalex-snapshot

Massive mining of publicly available RNA-seq data from human and mouse

inferredcites →https://doi.org/10.1038/s41592-018-0229-2openalex-snapshot

Deep generative modeling for single-cell transcriptomics

inferredcites →https://doi.org/10.1038/s41598-018-19635-0openalex-snapshot

Robust phenotype prediction from gene expression data using differential shrinkage of co-regulated genes

inferredcites →https://doi.org/10.1053/j.gastro.2015.10.041openalex-snapshot

Association Between Response to Etrolizumab and Expression of Integrin αE and Granzyme A in Colon Biopsies of Patients With Ulcerative Colitis

inferredcites →https://doi.org/10.1073/pnas.0506580102openalex-snapshot

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

inferredcites →https://doi.org/10.1073/pnas.1402665111openalex-snapshot

Global genomic and transcriptomic analysis of human pancreatic islets reveals novel genes influencing glucose metabolism

inferredcites →https://doi.org/10.1080/01621459.1977.10480613openalex-snapshot

The Efficiency of Cox's Likelihood Function for Censored Data

inferredcites →https://doi.org/10.1093/bioinformatics/btaa293openalex-snapshot

scVAE: variational auto-encoders for single-cell gene expression data

inferredcites →https://doi.org/10.1093/bioinformatics/btt351openalex-snapshot

CellMix: a comprehensive toolbox for gene expression deconvolution

inferredcites →https://doi.org/10.1093/bioinformatics/btt432openalex-snapshot

TFcheckpoint: a curated compendium of specific DNA-binding RNA polymerase II transcription factors

inferredcites →https://doi.org/10.1093/bioinformatics/btt703openalex-snapshot

Causal analysis approaches in Ingenuity Pathway Analysis

inferredcites →https://doi.org/10.1093/bioinformatics/btu272openalex-snapshot

Robust clinical outcome prediction based on Bayesian analysis of transcriptional profiles and prior causal networks

inferredcites →https://doi.org/10.1093/nar/gks1193openalex-snapshot

NCBI GEO: archive for functional genomics data sets—update

inferredcites →https://doi.org/10.1093/nar/gkv1351openalex-snapshot

The Reactome pathway Knowledgebase

inferredcites →https://doi.org/10.1093/nar/gkx1132openalex-snapshot

The Reactome Pathway Knowledgebase

inferredcites →https://doi.org/10.1093/nar/gky102openalex-snapshot

Improving the value of public RNA-seq expression data by phenotype prediction

inferredcites →https://doi.org/10.1097/mib.0000000000000478openalex-snapshot

MicroRNAs Classify Different Disease Behavior Phenotypes of Crohnʼs Disease and May Have Prognostic Utility

inferredcites →https://doi.org/10.1098/rsif.2017.0387openalex-snapshot

Opportunities and obstacles for deep learning in biology and medicine

inferredcites →https://doi.org/10.1101/278739openalex-snapshot

DeepProfile: Deep learning of cancer molecular profiles for precision medicine

inferredcites →https://doi.org/10.1101/299107openalex-snapshot

Who is this gene and what does it do? A toolkit for munging transcriptomics data in python

inferredcites →https://doi.org/10.1109/allerton.2012.6483308openalex-snapshot

Stochastic optimization for PCA and PLS

inferredcites →https://doi.org/10.1109/bibm.2015.7359871openalex-snapshot

Learning structure in gene expression data using deep architectures, with an application to gene clustering

inferredcites →https://doi.org/10.1111/j.2517-6161.1982.tb01195.xopenalex-snapshot

The Statistical Analysis of Compositional Data

inferredcites →https://doi.org/10.1142/9789813235533_0008openalex-snapshot

Extracting a biologically relevant latent space from cancer transcriptomes with variational autoencoders

inferredcites →https://doi.org/10.1186/1471-2105-12-323openalex-snapshot

RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

inferredcites →https://doi.org/10.1186/1471-2105-12-449openalex-snapshot

ReCount: A multi-experiment resource of analysis-ready RNA-seq gene count datasets

inferredcites →https://doi.org/10.1186/2049-2618-2-15openalex-snapshot

Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis

inferredcites →https://doi.org/10.1186/gm214openalex-snapshot

Gene expression deconvolution in clinical samples

inferredcites →https://doi.org/10.1186/s12859-016-1311-3openalex-snapshot

Integrating gene set analysis and nonlinear predictive modeling of disease phenotypes using a Bayesian multitask formulation

inferredcites →https://doi.org/10.1186/s12859-017-1984-2openalex-snapshot

A biological network-based regularized artificial neural network model for robust phenotype prediction from gene expression data

inferredcites →https://doi.org/10.1186/s13073-015-0208-5openalex-snapshot

Proteogenomic analysis of psoriasis reveals discordant and concordant changes in mRNA and protein abundance

inferredcites →https://doi.org/10.1214/aoms/1177730491openalex-snapshot

On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other

inferredcites →https://doi.org/10.1371/journal.pcbi.1004075openalex-snapshot

Proportionality: A Valid Alternative to Correlation for Relative Data

inferredcites →https://doi.org/10.18653/v1/k16-1002openalex-snapshot

Generating Sentences from a Continuous Space

inferredcites →https://doi.org/10.4230/oasics.dx.2026.15openalex-snapshot

AutoResearch Lite: a reproducible paper-inspired experiment runner (Adam mechanism reproduction and convergence-speed comparison)

inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-snapshot

Scikit-learn: Machine Learning in Python

inferredcites →https://doi.org/10.48550/arxiv.1312.6114openalex-snapshot

Auto-Encoding Variational Bayes

inferredcites →https://doi.org/10.48550/arxiv.1412.6980openalex-snapshot

Adam: A Method for Stochastic Optimization

inferredcites →https://doi.org/10.48550/arxiv.1706.02515openalex-snapshot

Self-Normalizing Neural Networks

inferredcites →https://doi.org/10.48550/arxiv.1706.08203openalex-snapshot

Dr.VAE: Drug Response Variational Autoencoder

inferredcites →https://doi.org/10.48550/arxiv.1711.04828openalex-snapshot

Evaluating deep variational autoencoders trained on pan-cancer gene expression

inferredcites →https://doi.org/10.57702/o9raffedopenalex-snapshot

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1016/j.tips.2019.06.003openalex-snapshot

Insights into Computational Drug Repurposing for Neurodegenerative Disease

inferred← citeshttps://doi.org/10.1101/653907openalex-snapshot

Deep learning does not outperform classical machine learning for cell-type annotation

inferred← citeshttps://doi.org/10.1109/access.2019.2947687openalex-snapshot

An End-to-End-Based Low Dimensional Binary Embedding for Chrysanthemum Phenotypic Petal Similarity Evaluation

inferred← citeshttps://doi.org/10.2174/0118715273329531240911075309openalex-snapshot

Revolutionising Neurological Therapeutics: Investigating Drug Repurposing Strategies

inferred← citeshttps://doi.org/10.3389/fphar.2022.1007315openalex-snapshot

Therapeutic drug repositioning with special emphasis on neurodegenerative diseases: Threats and issues

inferred← citeshttps://doi.org/10.3390/app9153065openalex-snapshot

Occam’s Razor for Big Data? On Detecting Quality in Large Unstructured Datasets

inferred← citeshttps://doi.org/10.3390/ijms22020840openalex-snapshot

Application of Machine-Learning Methods to Recognize mitoBK Channels from Different Cell Types Based on the Experimental Patch-Clamp Results

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.