i6mA-CNN: a convolution based computational approach towards identification of DNA N6-methyladenine sites in rice genome

• URL: http://arxiv.org/abs/2007.10458v2
• Date: Tue, 11 Aug 2020 11:05:22 GMT
• Title: i6mA-CNN: a convolution based computational approach towards identification of DNA N6-methyladenine sites in rice genome
• Abstract summary: We develop a convolutional neural network based tool i6mA-CNN capable of identifying 6mA sites in the rice genome. Results suggest that our proposed tool is able to generalize its ability of 6mA site identification on plant genomes irrespective of plant species.
• Score: 1.9723060056911002
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: DNA N6-methylation (6mA) in Adenine nucleotide is a post replication modification and is responsible for many biological functions. Experimental methods for genome wide 6mA site detection is an expensive and manual labour intensive process. Automated and accurate computational methods can help to identify 6mA sites in long genomes saving significant time and money. Our study develops a convolutional neural network based tool i6mA-CNN capable of identifying 6mA sites in the rice genome. Our model coordinates among multiple types of features such as PseAAC inspired customized feature vector, multiple one hot representations and dinucleotide physicochemical properties. It achieves area under the receiver operating characteristic curve of 0.98 with an overall accuracy of 0.94 using 5 fold cross validation on benchmark dataset. Finally, we evaluate our model on two other plant genome 6mA site identification datasets besides rice. Results suggest that our proposed tool is able to generalize its ability of 6mA site identification on plant genomes irrespective of plant species. Web tool for this research can be found at: https://cutt.ly/Co6KuWG. Supplementary data (benchmark dataset, independent test dataset, comparison purpose dataset, trained model, physicochemical property values, attention mechanism details for motif finding) are available at https://cutt.ly/PpDdeDH.

Related papers

• MEDNA-DFM: A Dual-View FiLM-MoE Model for Explainable DNA Methylation Prediction [7.3621714430935805]
  We introduce a high-performance model MEDNA-DFM, alongside mechanism-inspired signal purification algorithms.<n>Our investigation demonstrates that MEDNA-DFM effectively captures conserved methylation patterns.<n>Applying our developed algorithms extracted motifs with significantly higher reliability than prior studies.
  arXiv  Detail & Related papers  (2026-02-26T10:38:41Z)
• DPCformer: An Interpretable Deep Learning Model for Genomic Prediction in Crops [9.578611448561992]
  Genomic Selection (GS) uses whole-genome information to predict crop phenotypes and accelerate breeding.<n>Traditional GS methods, however, struggle with prediction accuracy for complex traits and large datasets.<n>We propose DPCformer, a deep learning model integrating convolutional neural networks with a self-attention mechanism to model complex genotype-phenotype relationships.
  arXiv  Detail & Related papers  (2025-10-09T13:53:36Z)
• Learning Genomic Structure from $k$-mers [2.07180164747172]
  We present a method for analyzing read data using contrastive learning.<n>An encoder model is trained to produce embeddings that cluster together sequences from the same genomic region.<n>The model can also be trained fully self-supervised on read data, enabling analysis without the need to construct a full genome assembly.
  arXiv  Detail & Related papers  (2025-05-22T13:46:18Z)
• Stacked ensemble\-based mutagenicity prediction model using multiple modalities with graph attention network [0.9736758288065405]
  Mutagenicity is a concern due to its association with genetic mutations which can result in a variety of negative consequences. In this work, we introduce a novel stacked ensemble based mutagenicity prediction model.
  arXiv  Detail & Related papers  (2024-09-03T09:14:21Z)
• A Benchmark Dataset for Multimodal Prediction of Enzymatic Function Coupling DNA Sequences and Natural Language [3.384797724820242]
  Predicting gene function from its DNA sequence is a fundamental challenge in biology. Deep learning models have been proposed to embed DNA sequences and predict their enzymatic function. Much of the scientific community's knowledge of biological function is not represented in categorical labels.
  arXiv  Detail & Related papers  (2024-07-21T19:27:43Z)
• BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments [112.25067497985447]
  We introduce BioDiscoveryAgent, an agent that designs new experiments, reasons about their outcomes, and efficiently navigates the hypothesis space to reach desired solutions. BioDiscoveryAgent can uniquely design new experiments without the need to train a machine learning model. It achieves an average of 21% improvement in predicting relevant genetic perturbations across six datasets.
  arXiv  Detail & Related papers  (2024-05-27T19:57:17Z)
• RoseNet: Predicting Energy Metrics of Double InDel Mutants Using Deep Learning [0.7776497736451751]
  We make use of our data sets of exhaustive double InDel mutations for three proteins which we computationally generated using a robotics inspired inverse kinematics approach available in Rosetta. We develop and train a neural network, RoseNet, on several structural and energetic metrics output by Rosetta during the mutant generation process. We show that the model can be trained on minimal data (50%) and still retain a high level of accuracy.
  arXiv  Detail & Related papers  (2023-10-20T20:36:13Z)
• HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution [76.97231739317259]
  We present HyenaDNA, a genomic foundation model pretrained on the human reference genome with context lengths of up to 1 million tokens at the single nucleotide-level. On fine-tuned benchmarks from the Nucleotide Transformer, HyenaDNA reaches state-of-the-art (SotA) on 12 of 18 datasets using a model with orders of magnitude less parameters and pretraining data.
  arXiv  Detail & Related papers  (2023-06-27T20:46:34Z)
• Deep metric learning improves lab of origin prediction of genetically engineered plasmids [63.05016513788047]
  Genetic engineering attribution (GEA) is the ability to make sequence-lab associations. We propose a method, based on metric learning, that ranks the most likely labs-of-origin. We are able to extract key signatures in plasmid sequences for particular labs, allowing for an interpretable examination of the model's outputs.
  arXiv  Detail & Related papers  (2021-11-24T16:29:03Z)
• Multi-modal Self-supervised Pre-training for Regulatory Genome Across Cell Types [75.65676405302105]
  We propose a simple yet effective approach for pre-training genome data in a multi-modal and self-supervised manner, which we call GeneBERT. We pre-train our model on the ATAC-seq dataset with 17 million genome sequences.
  arXiv  Detail & Related papers  (2021-10-11T12:48:44Z)
• MuCoMiD: A Multitask Convolutional Learning Framework for miRNA-Disease Association Prediction [0.4061135251278187]
  We propose a novel multi-tasking convolution-based approach, which we refer to as MuCoMiD. MuCoMiD allows automatic feature extraction while incorporating knowledge from 4 heterogeneous biological information sources. We construct large-scale experiments on standard benchmark datasets as well as our proposed larger independent test sets and case studies. MuCoMiD shows an improvement of at least 5% in 5-fold CV evaluation on HMDDv2.0 and HMDDv3.0 datasets and at least 49% on larger independent test sets with unseen diseases and unseen diseases over state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-08-08T10:01:46Z)
• Efficient approximation of DNA hybridisation using deep learning [0.0]
  We present the first comprehensive study of machine learning methods applied to the task of predicting DNA hybridisation. We introduce a synthetic hybridisation dataset of over 2.5 million data points, enabling the use of a wide range of machine learning algorithms.
  arXiv  Detail & Related papers  (2021-02-19T19:23:49Z)
• A Novel Approach to Radiometric Identification [68.8204255655161]
  This paper demonstrates that highly accurate radiometric identification is possible using CAPoNeF feature engineering method. We tested basic ML classification algorithms on experimental data gathered by SDR.
  arXiv  Detail & Related papers  (2020-12-02T10:54:44Z)
• A deep learning classifier for local ancestry inference [63.8376359764052]
  Local ancestry inference identifies the ancestry of each segment of an individual's genome. We develop a new LAI tool using a deep convolutional neural network with an encoder-decoder architecture. We show that our model is able to learn admixture as a zero-shot task, yielding ancestry assignments that are nearly as accurate as those from the existing gold standard tool, RFMix.
  arXiv  Detail & Related papers  (2020-11-04T00:42:01Z)

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.