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Kipoi: accelerating the community exchange and reuse of predictive models for genomics

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What this work says

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

inferredcites →https://doi.org/10.1002/humu.22932openalex-snapshot

dbNSFP v3.0: A One-Stop Database of Functional Predictions and Annotations for Human Nonsynonymous and Splice-Site SNVs

inferredcites →https://doi.org/10.1016/j.cell.2015.09.054openalex-snapshot

Learning the Sequence Determinants of Alternative Splicing from Millions of Random Sequences

inferredcites →https://doi.org/10.1016/j.ymeth.2019.03.020openalex-snapshot

FactorNet: A deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data

inferredcites →https://doi.org/10.1038/nature21056openalex-snapshot

Dermatologist-level classification of skin cancer with deep neural networks

inferredcites →https://doi.org/10.1038/nbt.3300openalex-snapshot

Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning

inferredcites →https://doi.org/10.1038/nbt.4061openalex-snapshot

Deep learning improves prediction of CRISPR–Cpf1 guide RNA activity

inferredcites →https://doi.org/10.1038/nbt.4235openalex-snapshot

A universal SNP and small-indel variant caller using deep neural networks

inferredcites →https://doi.org/10.1038/ng.2892openalex-snapshot

A general framework for estimating the relative pathogenicity of human genetic variants

inferredcites →https://doi.org/10.1038/nmeth.3547openalex-snapshot

Predicting effects of noncoding variants with deep learning–based sequence model

inferredcites →https://doi.org/10.1038/s41467-019-09025-zopenalex-snapshot

A multi-task convolutional deep neural network for variant calling in single molecule sequencing

inferredcites →https://doi.org/10.1038/s41592-018-0046-7openalex-snapshot

Bioconda: sustainable and comprehensive software distribution for the life sciences

inferredcites →https://doi.org/10.1089/1066527041410418openalex-snapshot

Maximum Entropy Modeling of Short Sequence Motifs with Applications to RNA Splicing Signals

inferredcites →https://doi.org/10.1093/bioinformatics/bts480openalex-snapshot

Snakemake—a scalable bioinformatics workflow engine

inferredcites →https://doi.org/10.1093/bioinformatics/btw142openalex-snapshot

LS-GKM: a new gkm-SVM for large-scale datasets

inferredcites →https://doi.org/10.1093/hmg/ddu733openalex-snapshot

Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies

inferredcites →https://doi.org/10.1093/nar/30.1.207openalex-snapshot

Gene Expression Omnibus: NCBI gene expression and hybridization array data repository

inferredcites →https://doi.org/10.1093/nar/gku1057openalex-snapshot

ArrayExpress update—simplifying data submissions

inferredcites →https://doi.org/10.1093/nar/gku1206openalex-snapshot

In silico prediction of splice-altering single nucleotide variants in the human genome

inferredcites →https://doi.org/10.1093/nar/gkv1249openalex-snapshot

HOCOMOCO: expansion and enhancement of the collection of transcription factor binding sites models

inferredcites →https://doi.org/10.1093/nar/gkx177openalex-snapshot

Predicting the impact of non-coding variants on DNA methylation

inferredcites →https://doi.org/10.1101/085118openalex-snapshot

Automating Morphological Profiling with Generic Deep Convolutional Networks

inferredcites →https://doi.org/10.1101/gr.182899.114openalex-snapshot

Genome-wide discovery of human splicing branchpoints

inferredcites →https://doi.org/10.1101/gr.200535.115openalex-snapshot

Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks

inferredcites →https://doi.org/10.1109/cvpr.2019.00277openalex-snapshot

Do Better ImageNet Models Transfer Better?

inferredcites →https://doi.org/10.1126/science.1254806openalex-snapshot

The human splicing code reveals new insights into the genetic determinants of disease

inferredcites →https://doi.org/10.1186/gb-2004-5-10-r80openalex-snapshot

Bioconductor: open software development for computational biology and bioinformatics

inferredcites →https://doi.org/10.1186/s12859-015-0553-9openalex-snapshot

Deep convolutional neural networks for annotating gene expression patterns in the mouse brain

inferredcites →https://doi.org/10.1186/s12859-017-1685-xopenalex-snapshot

Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features

inferredcites →https://doi.org/10.1186/s13059-016-0974-4openalex-snapshot

The Ensembl Variant Effect Predictor

inferredcites →https://doi.org/10.1186/s13059-018-1459-4openalex-snapshot

DeepCRISPR: optimized CRISPR guide RNA design by deep learning

inferredcites →https://doi.org/10.1261/rna.066290.118openalex-snapshot

A sequence-based, deep learning model accurately predicts RNA splicing branchpoints

inferredcites →https://doi.org/10.1371/journal.pcbi.1003711openalex-snapshot

Enhanced Regulatory Sequence Prediction Using Gapped k-mer Features

inferredcites →https://doi.org/10.18653/v1/p18-1031openalex-snapshot

Universal Language Model Fine-tuning for Text Classification

inferredcites →https://doi.org/10.48550/arxiv.1603.04467openalex-snapshot

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

What points at this work

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

inferred← citeshttps://doi.org/10.1007/978-981-15-2445-5_4openalex-snapshot

Impact of Machine Learning in Bioinformatics Research

inferred← citeshttps://doi.org/10.1016/j.jpdc.2020.08.006openalex-snapshot

DLHub: Simplifying publication, discovery, and use of machine learning models in science

inferred← citeshttps://doi.org/10.1016/j.ymeth.2019.03.020openalex-snapshot

FactorNet: A deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data

inferred← citeshttps://doi.org/10.1038/s41467-020-17155-yopenalex-snapshot

Deep learning for genomics using Janggu

inferred← citeshttps://doi.org/10.1038/s41576-019-0122-6openalex-snapshot

Deep learning: new computational modelling techniques for genomics

inferred← citeshttps://doi.org/10.1038/s41588-018-0295-5openalex-snapshot

A primer on deep learning in genomics

inferred← citeshttps://doi.org/10.1038/s42256-022-00467-7openalex-snapshot

Ad hoc learning of peptide fragmentation from mass spectra enables an interpretable detection of phosphorylated and cross-linked peptides

inferred← citeshttps://doi.org/10.1038/s42256-024-00851-5openalex-snapshot

Interpreting cis-regulatory mechanisms from genomic deep neural networks using surrogate models

inferred← citeshttps://doi.org/10.1093/bioinformatics/btz352openalex-snapshot

Integrating regulatory DNA sequence and gene expression to predict genome-wide chromatin accessibility across cellular contexts

inferred← citeshttps://doi.org/10.1101/2020.02.29.970962openalex-snapshot

Correlating predicted epigenetic marks with expression data to find interactions between SNPs and genes

inferred← citeshttps://doi.org/10.1101/2021.02.02.429064openalex-snapshot

Predicting Epigenomic Functions of Genetic Variants in the Context of Neurodevelopment via Deep Transfer Learning

inferred← citeshttps://doi.org/10.1101/2025.07.20.665722openalex-snapshot

RNALens: Study on 5’ UTR Modeling and Cell-Specificity

inferred← citeshttps://doi.org/10.1101/438291openalex-snapshot

Selene: a PyTorch-based deep learning library for biological sequence-level data

inferred← citeshttps://doi.org/10.1101/463778openalex-snapshot

Vec2SPARQL: integrating SPARQL queries and knowledge graph embeddings

inferred← citeshttps://doi.org/10.1101/gr.237156.118openalex-snapshot

Anchor: trans-cell type prediction of transcription factor binding sites

inferred← citeshttps://doi.org/10.1109/access.2023.3330647openalex-snapshot

Metadata Representations for Queryable Repositories of Machine Learning Models

inferred← citeshttps://doi.org/10.1109/icde.2019.00243openalex-snapshot

Automated Documentation of End-to-End Experiments in Data Science

inferred← citeshttps://doi.org/10.1109/ipdps.2019.00038openalex-snapshot

DLHub: Model and Data Serving for Science

inferred← citeshttps://doi.org/10.1109/tsc.2021.3100498openalex-snapshot

Do Auto-Regressive Models Protect Privacy? Inferring Fine-Grained Energy Consumption From Aggregated Model Parameters

inferred← citeshttps://doi.org/10.1186/s13059-019-1644-0openalex-snapshot

Combined single-cell profiling of expression and DNA methylation reveals splicing regulation and heterogeneity

inferred← citeshttps://doi.org/10.1186/s13059-019-1653-zopenalex-snapshot

MMSplice: modular modeling improves the predictions of genetic variant effects on splicing

inferred← citeshttps://doi.org/10.1255/jsi.2022.a7openalex-snapshot

spectrai: a deep learning framework for spectral data

inferred← citeshttps://doi.org/10.1371/journal.pcbi.1010011openalex-snapshot

Annotating functional effects of non-coding variants in neuropsychiatric cell types by deep transfer learning

inferred← citeshttps://doi.org/10.1557/mrc.2019.118openalex-snapshot

A data ecosystem to support machine learning in materials science

inferred← citeshttps://doi.org/10.2174/1381612825666191210160906openalex-snapshot

Neurological Disorder Drug Discovery from Gene Expression with Tensor Decomposition

inferred← citeshttps://doi.org/10.48550/arxiv.1905.13105openalex-snapshot

ImJoy: an open-source computational platform for the deep learning era

inferred← citeshttps://doi.org/10.48550/arxiv.1911.13218openalex-snapshot

ModelHub.AI: Dissemination Platform for Deep Learning Models

inferred← citeshttps://doi.org/10.53846/goediss-8555openalex-snapshot

Discovery and prediction of protein binding sites in DNA and RNA sequences using Bayesian Markov models

inferred← citeshttps://doi.org/10.6084/m9.figshare.7356416.v2openalex-snapshot

Vec2SPARQL: integrating SPARQL queries and knowledge graph embeddings

inferred← citeshttps://doi.org/10.70675/900909c4zcc8dz40c8z92d2z28b31331b257openalex-snapshot

Approche à large échelle visant à détecter de nouveaux régulateurs de l'épissage alternatif au cours de la transition épithélio-mésenchymateuse.

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