Referenced Papers (8)
Application of sand control and stimulation technology with fiber fracturing on unconsolidated deepsea sandstone reservoir
Y Shi, Y Gao, F Zhou, X Yang, C Xiong, X Liu
, 2015
"Cited as a foundational example of early deep learning models for regulatory DNA (sequence-to-function models) that initiated this line of research."
Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks
David R Kelley, Jasper Snoek, John L Rinn
Genome Res., 2016
"Cited as another key example of early deep learning models for regulatory DNA (sequence-to-function models) that helped establish the field."
Repeated electroconvulsive seizures increase the total number of synapses in adult male rat hippocampus
Fenghua Chen, Torsten M Madsen, Gregers Wegener, Jens R Nyengaard
Eur. Neuropsychopharmacol., 2009
"Mentioned as a more recent example of a sequence-to-function deep learning model for regulatory DNA."
Author Correction: scBasset: sequence-based modeling of single-cell ATAC-seq using convolutional neural networks
Han Yuan, David R Kelley
Nat. Methods, 2023
"Mentioned as another recent example of a sequence-to-function deep learning model for regulatory DNA."
ChromBPNet: bias factorized, base-resolution deep learning models of chromatin accessibility reveal cis-regulatory sequence syntax, transcription factor footprints and regulatory variants
Anusri Pampari, A Shcherbina, Evgeny Z Kvon, Michael Kosicki, Surag Nair, Soumya Kundu
bioRxiv, 2025
"Cited as a recent extension of the profile modeling approach to ATAC-seq data, building on the work of BPNet."
Designing realistic regulatory DNA with autoregressive language models
Avantika Lal, David Garfield, Tommaso Biancalani, Gokcen Eraslan
Genome Res., 2024
"Cited as an example of work using language models to explicitly design sequences from functional prompts, addressing an open problem in sequence-to-function modeling."
DART-Eval: A comprehensive DNA language model evaluation benchmark on regulatory DNA
Aman Patel, Arpita Singhal, Austin Wang, Anusri Pampari, Maya Kasowski, Anshul Kundaje
Neural Inf Process Syst, 2024
"Cited as a comprehensive evaluation benchmark for genomic language models, which showed that while gLMs excel at simpler tasks, their performance decreases with more complex tasks involving cell type-specific signals."
Benchmarking DNA sequence models for causal regulatory variant prediction in human genetics
Gonzalo Benegas, Gökçen Eraslan, Yun S Song
bioRxiv, 2025
"Cited as a benchmark for evaluating genomic models on the task of classifying complex and Mendelian non-coding variants."