LOCO-EPI: Leave-one-chromosome-out (LOCO) as a benchmarking paradigm for deep learning based prediction of enhancer-promoter interactions

• URL: http://arxiv.org/abs/2504.00306v1
• Date: Tue, 01 Apr 2025 00:20:15 GMT
• Title: LOCO-EPI: Leave-one-chromosome-out (LOCO) as a benchmarking paradigm for deep learning based prediction of enhancer-promoter interactions
• Authors: Muhammad Tahir, Shehroz S. Khan, James Davie, Soichiro Yamanaka, Ahmed Ashraf,
• Abstract summary: We propose a more thorough training and testing paradigm for Enhancer-Promoter Interactions (EPI)-prediction.<n>We demonstrate that a deep learning algorithm, which gives higher accuracies when trained and tested on random-splitting setting, drops drastically in performance under LOCO setting.<n>We also propose a novel hybrid deep neural network for EPI-prediction that fuses k-mer features of the nucleotide sequence.
• Score: 2.688011048756518
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In mammalian and vertebrate genomes, the promoter regions of the gene and their distal enhancers may be located millions of base-pairs from each other, while a promoter may not interact with the closest enhancer. Since base-pair proximity is not a good indicator of these interactions, there is considerable work toward developing methods for predicting Enhancer-Promoter Interactions (EPI). Several machine learning methods have reported increasingly higher accuracies for predicting EPI. Typically, these approaches randomly split the dataset of Enhancer-Promoter (EP) pairs into training and testing subsets followed by model training. However, the aforementioned random splitting causes information leakage by assigning EP pairs from the same genomic region to both testing and training sets, leading to performance overestimation. In this paper we propose to use a more thorough training and testing paradigm i.e., Leave-one-chromosome-out (LOCO) cross-validation for EPI-prediction. We demonstrate that a deep learning algorithm, which gives higher accuracies when trained and tested on random-splitting setting, drops drastically in performance under LOCO setting, confirming overestimation of performance. We further propose a novel hybrid deep neural network for EPI-prediction that fuses k-mer features of the nucleotide sequence. We show that the hybrid architecture performs significantly better in the LOCO setting, demonstrating it can learn more generalizable aspects of EP interactions. With this paper we are also releasing the LOCO splitting-based EPI dataset. Research data is available in this public repository: https://github.com/malikmtahir/EPI

Related papers

• AsEP: Benchmarking Deep Learning Methods for Antibody-specific Epitope Prediction [12.433560411515575]
  We introduce a filtered antibody-antigen complex structure dataset, AsEP. AsEP is the largest of its kind and provides clustered groups. We propose a novel method, WALLE, which leverages both protein language models and structural modeling from graph neural networks.
  arXiv  Detail & Related papers  (2024-07-25T16:43:56Z)
• The effect of data augmentation and 3D-CNN depth on Alzheimer's Disease detection [51.697248252191265]
  This work summarizes and strictly observes best practices regarding data handling, experimental design, and model evaluation. We focus on Alzheimer's Disease (AD) detection, which serves as a paradigmatic example of challenging problem in healthcare. Within this framework, we train predictive 15 models, considering three different data augmentation strategies and five distinct 3D CNN architectures.
  arXiv  Detail & Related papers  (2023-09-13T10:40:41Z)
• Robust Learning with Progressive Data Expansion Against Spurious Correlation [65.83104529677234]
  We study the learning process of a two-layer nonlinear convolutional neural network in the presence of spurious features. Our analysis suggests that imbalanced data groups and easily learnable spurious features can lead to the dominance of spurious features during the learning process. We propose a new training algorithm called PDE that efficiently enhances the model's robustness for a better worst-group performance.
  arXiv  Detail & Related papers  (2023-06-08T05:44:06Z)
• Convolutional Monge Mapping Normalization for learning on sleep data [63.22081662149488]
  We propose a new method called Convolutional Monge Mapping Normalization (CMMN) CMMN consists in filtering the signals in order to adapt their power spectrum density (PSD) to a Wasserstein barycenter estimated on training data. Numerical experiments on sleep EEG data show that CMMN leads to significant and consistent performance gains independent from the neural network architecture.
  arXiv  Detail & Related papers  (2023-05-30T08:24:01Z)
• ASPEST: Bridging the Gap Between Active Learning and Selective Prediction [56.001808843574395]
  Selective prediction aims to learn a reliable model that abstains from making predictions when uncertain. Active learning aims to lower the overall labeling effort, and hence human dependence, by querying the most informative examples. In this work, we introduce a new learning paradigm, active selective prediction, which aims to query more informative samples from the shifted target domain.
  arXiv  Detail & Related papers  (2023-04-07T23:51:07Z)
• Linear-scaling kernels for protein sequences and small molecules outperform deep learning while providing uncertainty quantitation and improved interpretability [5.623232537411766]
  We develop efficient and scalable approaches for fitting GP models and fast convolution kernels. We implement these improvements by building an open-source Python library called xGPR. We show that xGPR generally outperforms convolutional neural networks on predicting key properties of proteins and small molecules.
  arXiv  Detail & Related papers  (2023-02-07T07:06:02Z)
• HAC-Net: A Hybrid Attention-Based Convolutional Neural Network for Highly Accurate Protein-Ligand Binding Affinity Prediction [0.0]
  We present a novel deep learning architecture consisting of a 3-dimensional convolutional neural network and two graph convolutional networks. HAC-Net obtains state-of-the-art results on the PDBbind v.2016 core set. We envision that this model can be extended to a broad range of supervised learning problems related to structure-based biomolecular property prediction.
  arXiv  Detail & Related papers  (2022-12-23T16:14:53Z)
• A Supervised Machine Learning Approach for Sequence Based Protein-protein Interaction (PPI) Prediction [4.916874464940376]
  Computational protein-protein interaction (PPI) prediction techniques can contribute greatly in reducing time, cost and false-positive interactions. We have described our submitted solution with the results of the SeqPIP competition.
  arXiv  Detail & Related papers  (2022-03-23T18:27:25Z)
• Pre-training Co-evolutionary Protein Representation via A Pairwise Masked Language Model [93.9943278892735]
  Key problem in protein sequence representation learning is to capture the co-evolutionary information reflected by the inter-residue co-variation in the sequences. We propose a novel method to capture this information directly by pre-training via a dedicated language model, i.e., Pairwise Masked Language Model (PMLM) Our result shows that the proposed method can effectively capture the interresidue correlations and improves the performance of contact prediction by up to 9% compared to the baseline.
  arXiv  Detail & Related papers  (2021-10-29T04:01:32Z)
• Towards an Understanding of Benign Overfitting in Neural Networks [104.2956323934544]
  Modern machine learning models often employ a huge number of parameters and are typically optimized to have zero training loss. We examine how these benign overfitting phenomena occur in a two-layer neural network setting. We show that it is possible for the two-layer ReLU network interpolator to achieve a near minimax-optimal learning rate.
  arXiv  Detail & Related papers  (2021-06-06T19:08:53Z)
• PEP: Parameter Ensembling by Perturbation [13.221295194854642]
  Ensembling by Perturbation (PEP) constructs an ensemble of parameter values as random perturbations of the optimal parameter set from training. PEP provides a small improvement in performance, and, in some cases, a substantial improvement in empirical calibration. PEP can be used to probe the level of overfitting that occurred during training.
  arXiv  Detail & Related papers  (2020-10-24T00:16:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.