AIMC Topic: DNA
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Machine Learning Recognition of Artificial DNA Sequence with Quantum Tunneling Nanogap Junction.
The journal of physical chemistry. B
Jan 9, 2025
Artificially synthesized DNA holds significant promise in addressing fundamental biochemical questions and driving advancements in biotechnology, genetics, and DNA digital data storage. Rapid and precise electric identification of these artificial DN...
DNA promoter task-oriented dictionary mining and prediction model based on natural language technology.
Scientific reports
Jan 2, 2025
Promoters are essential DNA sequences that initiate transcription and regulate gene expression. Precisely identifying promoter sites is crucial for deciphering gene expression patterns and the roles of gene regulatory networks. Recent advancements in...
ChromatinHD connects single-cell DNA accessibility and conformation to gene expression through scale-adaptive machine learning.
Nature communications
Jan 2, 2025
Gene regulation is inherently multiscale, but scale-adaptive machine learning methods that fully exploit this property in single-nucleus accessibility data are still lacking. Here, we develop ChromatinHD, a pair of scale-adaptive models that uses the...
High-Risk Sequence Prediction Model in DNA Storage: The LQSF Method.
IEEE transactions on nanobioscience
Jan 2, 2025
Traditional DNA storage technologies rely on passive filtering methods for error correction during synthesis and sequencing, which result in redundancy and inadequate error correction. Addressing this, the Low Quality Sequence Filter (LQSF) was intro...
A Robust and Efficient Representation-based DNA Storage Architecture by Deep Learning.
Small methods
Dec 29, 2024
As one main form of multimedia data, images play a critical role in various applications. In this paper, a representation-based architecture is proposed which takes advantage of the outstanding representation and image-generation abilities of deep le...
A sparse and wide neural network model for DNA sequences.
Neural networks : the official journal of the International Neural Network Society
Dec 19, 2024
Accurate modeling of DNA sequences requires capturing distant semantic relationships between the nucleotide acid bases. Most existing deep neural network models face two challenges: (1) they are limited to short DNA fragments and cannot capture long-...
Ense-i6mA: Identification of DNA N-Methyladenine Sites Using XGB-RFE Feature Selection and Ensemble Machine Learning.
IEEE/ACM transactions on computational biology and bioinformatics
Dec 10, 2024
DNA N-methyladenine (6mA) is an important epigenetic modification that plays a vital role in various cellular processes. Accurate identification of the 6mA sites is fundamental to elucidate the biological functions and mechanisms of modification. How...
Self-distillation improves self-supervised learning for DNA sequence inference.
Neural networks : the official journal of the International Neural Network Society
Dec 7, 2024
Self-supervised Learning (SSL) has been recognized as a method to enhance prediction accuracy in various downstream tasks. However, its efficacy for DNA sequences remains somewhat constrained. This limitation stems primarily from the fact that most e...
Representing DNA for machine learning algorithms: A primer on one-hot, binary, and integer encodings.
Biochemistry and molecular biology education : a bimonthly publication of the International Union of Biochemistry and Molecular Biology
Dec 5, 2024
This short paper presents an educational approach to teaching three popular methods for encoding DNA sequences: one-hot encoding, binary encoding, and integer encoding. Aimed at bioinformatics and computational biology students, our learning interven...
Translation as a Biosignature.
Astrobiology
Nov 29, 2024
Life on Earth relies on mechanisms to store heritable information and translate this information into cellular machinery required for biological activity. In all known life, storage, regulation, and translation are provided by DNA, RNA, and ribosomes...
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