DeepChrome: deep-learning for predicting gene expression from histone modifications
By Ritambhara Singh, Jack Lanchantin, Gabriel Robins, Yanjun Qi. 2016
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Travers Ching
- Alexandr A Kalinin
- Brett K. Beaulieu-Jones
- Yoshua Bengio
- Daniel S. Himmelstein
- Adriana Romero
- Jack Lanchantin
- Tristan Sylvain
Citations in the corpus
- Opportunities and obstacles for deep learning in biology and medicine (2017, methodology): “For instance, a convolutional neural network applied to histone modifications, termed DeepChrome, [147] improved prediction accuracy of high or low gene expression over existing methods.”
- Diet Networks: Thin Parameters for Fat Genomic (2016, background): “…the regulatory role of RNA binding proteins in alternative splicing (Alipanahi et al., 2015), inferring gene expression patterns (Chen et al., 2016; Singh et al., 2016) and population genetic parameters (Sheehan & Song, 2016) among others (see Leung et al. (2016) for a detailed example).”
- Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks (2016, background): “However, although these models have proven to be very accurate, they have widely been viewed as “black boxes” due to their complexity, making them hard to understand.”
- SBSM-Pro: support bio-sequence machine for proteins (2023, unknown): “Classical CNNs and RNNs, as well as more recently celebrated transformer architectures like BERT [40, 41] and GPT, have their origins in domains like image analysis (for tasks such as face recognition or autonomous driving) and natural language processing.”
- Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification (2018, background): “17 Other recent applications of deep learning models to prediction of regulatory elements and their interactions with the state-of-the-art performance include enhancer prediction [76-78]; classification of gene expression using histone modification data as input [60]; prediction of DNA methylation s”
- Transfer Learning for Molecular Cancer Classification Using Deep Neural Networks (2019, unknown): “[43] used R.”