Feb 28, 2024 · This study presents an innovative method for improving the resolution of Hi-C data by exploiting the Transformer model. The proposed method ...
The HiCTF method uses the Transformer model to enhance the resolution of the Hi-C matrix, making it easier to compare data of different resolutions and ...
The trained models are applied to the validation and test data within the dataset and show a 3-5 dB rise in image quality compared to bicubic interpolation, ...
2020. DeepHiC: A generative adversarial network for enhancing Hi-C data resolution. PLoS computational biology 16(2) e1007287. https://doi.org/10.1371/journal.
Jun 4, 2024 · HiCVAE: A Variational Auto-Encoder Framework for Simulating Hi-C Data. ... HiCTF: A Transformer Model for enhancing Hi-C data resolution. ICBBE ...
Oct 24, 2024 · SHARP tackles this problem by modeling the Hi-C contact matrix as the superposition of signals caused by three types of chromatin contacts, ...
Missing: HiCTF: | Show results with:HiCTF:
This study presents a novel approach for enhancing Hi-C data resolution, and provides fascinating insights into disclosing complex mechanism underlying the ...
Oct 15, 2024 · We propose the HiCDiffusion, sequence-only model that addresses this problem. We first train the encoder-decoder neural network and then use it as a component ...
Missing: HiCTF: | Show results with:HiCTF:
We demonstrated that DeepHiC is capable of reproducing high-resolution Hi-C data from as few as 1% downsampled reads. Empowered by adversarial training, our ...
SRHiC: A Deep Learning Model to Enhance the Resolution of Hi-C Data
pmc.ncbi.nlm.nih.gov › PMC7156553
Apr 8, 2020 · Here we developed a novel and simple computational method based on deep learning named super-resolution Hi-C (SRHiC) to enhance the resolution of Hi-C data.
Missing: HiCTF: Transformer