AIMC Topic: Sequence Analysis, DNA

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Minimum Functional Length Analysis of K-Mer Based on BPNN.

IEEE/ACM transactions on computational biology and bioinformatics
BP neural network (BPNN), as a multilayer feed-forward network, can realize the deep cognition to target data and high accuracy to output results. However, there were still no related research of k-mer based on BPNN yet. In present study, BPNN was us...

Using Artificial Neural Networks to Model Errors in Biochemical Manipulation of DNA Molecules.

IEEE/ACM transactions on computational biology and bioinformatics
In recent years, the non-biological applications of DNA molecules have made considerable progress; most of these applications were performed in vitro, involving biochemical operations such as synthesis, amplification and sequencing. Because errors ma...

SVision: a deep learning approach to resolve complex structural variants.

Nature methods
Complex structural variants (CSVs) encompass multiple breakpoints and are often missed or misinterpreted. We developed SVision, a deep-learning-based multi-object-recognition framework, to automatically detect and haracterize CSVs from long-read sequ...

Automated filtering of genome-wide large deletions through an ensemble deep learning framework.

Methods (San Diego, Calif.)
Computational methods based on whole genome linked-reads and short-reads have been successful in genome assembly and detection of structural variants (SVs). Numerous variant callers that rely on linked-reads and short reads can detect genetic variati...

DeepBarcoding: Deep Learning for Species Classification Using DNA Barcoding.

IEEE/ACM transactions on computational biology and bioinformatics
DNA barcodes with short sequence fragments are used for species identification. Because of advances in sequencing technologies, DNA barcodes have gradually been emphasized. DNA sequences from different organisms are easily and rapidly acquired. There...

Single-cell specific and interpretable machine learning models for sparse scChIP-seq data imputation.

PloS one
MOTIVATION: Single-cell Chromatin ImmunoPrecipitation DNA-Sequencing (scChIP-seq) analysis is challenging due to data sparsity. High degree of sparsity in biological high-throughput single-cell data is generally handled with imputation methods that c...

Using deep learning to detect digitally encoded DNA trigger for Trojan malware in Bio-Cyber attacks.

Scientific reports
This article uses Deep Learning technologies to safeguard DNA sequencing against Bio-Cyber attacks. We consider a hybrid attack scenario where the payload is encoded into a DNA sequence to activate a Trojan malware implanted in a software tool used i...

Machine Learning Methods for Exploring Sequence Determinants of 3D Genome Organization.

Journal of molecular biology
In higher eukaryotic cells, chromosomes are folded inside the nucleus. Recent advances in whole-genome mapping technologies have revealed the multiscale features of 3D genome organization that are intertwined with fundamental genome functions. Howeve...

A successful hybrid deep learning model aiming at promoter identification.

BMC bioinformatics
BACKGROUND: The zone adjacent to a transcription start site (TSS), namely, the promoter, is primarily involved in the process of DNA transcription initiation and regulation. As a result, proper promoter identification is critical for further understa...

Machine learning sequence prioritization for cell type-specific enhancer design.

eLife
Recent discoveries of extreme cellular diversity in the brain warrant rapid development of technologies to access specific cell populations within heterogeneous tissue. Available approaches for engineering-targeted technologies for new neuron subtype...