Searching journal content for articles similar to Kelley et al. 28 (5): 739.

Displaying results 1-10 of 21

For checked items

view abstracts
download to citation manager

Select this article
Method: Iterative improvement of deep learning models using synthetic regulatory genomics
- André M. Ribeiro-dos-Santos and
- Matthew T. Maurano
Genome Res. November 2025 35: 2539-2549; Published in Advance October 22, 2025, doi:10.1101/gr.280540.125
...of deep learning models across different regimes, we analyzed the Enformer model owing to its high accuracy and wide receptive field that allows investigation of large regulatory landscapes (Avsec et al. 2021a). Based on previous work showing that Enformer retains most of its predictive performance...
Select this article
Method: Early feature extraction drives model performance in high-resolution chromatin accessibility prediction
- Aayush Grover,
- Till Muser,
- Liine Kasak,
- Lin Zhang,
- Ekaterina Krymova,
- and Valentina Boeva
Genome Res. March 2026 36: 619-629; Published in Advance January 12, 2026, doi:10.1101/gr.281042.125
...for genomic data. Integrated into diverse architectures such as convoluted neural networks (CNNs), long short-term memory (LSTM), dilated CNNs, and transformers, ConvNeXt V2 blocks consistently improve performance, leading to similar prediction accuracy across these different model types. This reveals...
OPEN ACCESS ARTICLE
Select this article
Method: Chromatin interaction–aware gene regulatory modeling with graph attention networks
- Alireza Karbalayghareh,
- Merve Sahin,
- and Christina S. Leslie
Genome Res. May 2022 32: 930-944; Published in Advance April 8, 2022, doi:10.1101/gr.275870.121
...accessibility from DNase-seq or ATAC-seq.GraphReg models use convolutional neural network (CNN) layers to learn local representations from 1D inputs, followed by graph attention network (GAT) layers to propagate these representations over the 3D interaction graph, to predict gene expression (CAGE-seq) across...
OPEN ACCESS ARTICLE
Select this article
Method: Diffusion-enhanced characterization of 3D chromatin structure reveals its linkage to gene regulatory networks and the interactome
- Yueying He,
- Yue Xue,
- Jingyao Wang,
- Yupeng Huang,
- Lu Liu,
- Yanyi Huang,
- and Yi Qin Gao
Genome Res. August 2023 33: 1354-1368; Published in Advance July 25, 2023, doi:10.1101/gr.277737.123
...a reliable and quantitative understanding of the alterations of genomic structures under different cellular conditions. The genomic structure yielded by CTG serves as an architectural blueprint of the dynamic gene regulatory network, based on which cell-specific correspondence between gene...
Select this article
Method: Domain-adaptive neural networks improve cross-species prediction of transcription factor binding
- Kelly Cochran,
- Divyanshi Srivastava,
- Avanti Shrikumar,
- Akshay Balsubramani,
- Ross C. Hardison,
- Anshul Kundaje,
- and Shaun Mahony
Genome Res. March 2022 32: 512-523; Published in Advance January 18, 2022, doi:10.1101/gr.275394.121
...species should generalize to closely matched cell types in a related species. To assess the viability of cross-species TF binding prediction, we train neural networks to discriminate ChIP-seq peak locations from genomic background and evaluate their performance within and across species. Cross...
Select this article
Method: Deep neural networks for interpreting RNA-binding protein target preferences
- Mahsa Ghanbari and
- Uwe Ohler
Genome Res. February 2020 30: 214-226; Published in Advance January 28, 2020, doi:10.1101/gr.247494.118
.... 2015 first showed that convolutional neural networks (CNNs) can learn TF/RBP binding sites with high accuracy compared to state-of-the-art methods, using only the DNA/RNA sequences as input. Since then, several convolutional and recurrent neural network models for genomics data have improved prediction...
Select this article
Method: Genome-wide prediction of DNA mutation effect on nucleosome positions for yeast synthetic genomics
- Etienne Routhier,
- Edgard Pierre,
- Ghazaleh Khodabandelou,
- and Julien Mozziconacci
Genome Res. February 2021 31: 317-326; Published in Advance December 18, 2020, doi:10.1101/gr.264416.120
.... Recently, deep neural networks were also applied to discriminate between 147-bp-long sequences bound by a nucleosome and 147-bp-long sequences without any nucleosomes (Di Gangi et al. 2018; Zhang et al. 2018).Building on these previous works, we use here convolutional neural networks (CNNs) to predict...
Select this article
Method: Anchor: trans-cell type prediction of transcription factor binding sites
- Hongyang Li,
- Daniel Quang,
- and Yuanfang Guan
Genome Res. February 2019 29: 281-292; Published in Advance December 19, 2018, doi:10.1101/gr.237156.118
.... In this study, we describe our first place solution to the 2017 ENCODE-DREAM in vivo TF binding site prediction challenge. By carefully addressing multisource biases and information imbalance across cell types, we created a pipeline that significantly outperforms the current state-of-the-art methods...
Select this article
Research: Integrating genetic variation with deep learning provides context for variants impacting transcription factor binding during embryogenesis
- Olga M. Sigalova,
- Mattia Forneris,
- Frosina Stojanovska,
- Bingqing Zhao,
- Rebecca R. Viales,
- Adam Rabinowitz,
- Fayrouz Hammal,
- Benoît Ballester,
- Judith B. Zaugg,
- and Eileen E.M. Furlong
Genome Res. May 2025 35: 1138-1153; Published in Advance April 15, 2025, doi:10.1101/gr.279652.124
...by 30%–50%. This fine-grained “mutagenesis” can reconstruct functionalized binding motifs for all factors. To prioritize causal variants, we trained a convolutional neural network (Basenji) to accurately predict binding from DNA sequence. The model can also predict measured allelic imbalance for strong...
OPEN ACCESS ARTICLE
Select this article
Resource: An integrative TAD catalog in lymphoblastoid cell lines discloses the functional impact of deletions and insertions in human genomes
- Chong Li,
- Marc Jan Bonder,
- Sabriya Syed,
- Matthew Jensen,
- Human Genome Structural Variation Consortium (HGSVC),
- HGSVC Functional Analysis Working Group,
- Mark B. Gerstein,
- Michael C. Zody,
- Mark J.P. Chaisson,
- Michael E. Talkowski,
- Tobias Marschall,
- Jan O. Korbel,
- Evan E. Eichler,
- Charles Lee,
- and Xinghua Shi
Genome Res. December 2024 34: 2304-2318; Published in Advance December 5, 2024, doi:10.1101/gr.279419.124
...and overlapping with particularly noncoding regulatory regions and functional data.Our study develops a strategy for reanalyzing and harmonizing existing data to generate a unified resource to characterize 3D structures across human s. Several systematic evaluation studies have revealed a notable lack...

For checked items

view abstracts
download to citation manager

Searching journal content for articles similar to Kelley et al. 28 (5): 739.

Search Results

Modify Results

This search

Preprint Server

Current Issue

In This Issue

Citation format:
Results / page:
Results order: