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DNA

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MLSNet: a deep learning model for predicting transcription factor binding sites.

Briefings in bioinformatics
Accurate prediction of transcription factor binding sites (TFBSs) is essential for understanding gene regulation mechanisms and the etiology of diseases. Despite numerous advances in deep learning for predicting TFBSs, their performance can still be ...

An integrated machine-learning model to predict nucleosome architecture.

Nucleic acids research
We demonstrate that nucleosomes placed in the gene body can be accurately located from signal decay theory assuming two emitters located at the beginning and at the end of genes. These generated wave signals can be in phase (leading to well defined n...

BertSNR: an interpretable deep learning framework for single-nucleotide resolution identification of transcription factor binding sites based on DNA language model.

Bioinformatics (Oxford, England)
MOTIVATION: Transcription factors are pivotal in the regulation of gene expression, and accurate identification of transcription factor binding sites (TFBSs) at high resolution is crucial for understanding the mechanisms underlying gene regulation. T...

iM-Seeker: a webserver for DNA i-motifs prediction and scoring via automated machine learning.

Nucleic acids research
DNA, beyond its canonical B-form double helix, adopts various alternative conformations, among which the i-motif, emerging in cytosine-rich sequences under acidic conditions, holds significant biological implications in transcription modulation and t...

EGPDI: identifying protein-DNA binding sites based on multi-view graph embedding fusion.

Briefings in bioinformatics
Mechanisms of protein-DNA interactions are involved in a wide range of biological activities and processes. Accurately identifying binding sites between proteins and DNA is crucial for analyzing genetic material, exploring protein functions, and desi...

Prediction of DNA i-motifs via machine learning.

Nucleic acids research
i-Motifs (iMs), are secondary structures formed in cytosine-rich DNA sequences and are involved in multiple functions in the genome. Although putative iM forming sequences are widely distributed in the human genome, the folding status and strength of...

Multiple sequence alignment-based RNA language model and its application to structural inference.

Nucleic acids research
Compared with proteins, DNA and RNA are more difficult languages to interpret because four-letter coded DNA/RNA sequences have less information content than 20-letter coded protein sequences. While BERT (Bidirectional Encoder Representations from Tra...

Simulations of Sequence Evolution: How (Un)realistic They Are and Why.

Molecular biology and evolution
MOTIVATION: Simulating multiple sequence alignments (MSAs) using probabilistic models of sequence evolution plays an important role in the evaluation of phylogenetic inference tools and is crucial to the development of novel learning-based approaches...

Peptidic Compound as DNA Binding Agent: Fragment-based Design, Machine Learning, Molecular Modeling, Synthesis, and DNA Binding Evaluation.

Protein and peptide letters
BACKGROUND: Cancer remains a global burden, with increasing mortality rates. Current cancer treatments involve controlling the transcription of malignant DNA genes, either directly or indirectly. DNA exhibits various structural forms, including the G...

Classification of DNA Sequence Based on a Non-gradient Algorithm: Pseudoinverse Learners.

Methods in molecular biology (Clifton, N.J.)
This chapter proposes a prototype-based classification approach for analyzing DNA barcodes that uses a spectral representation of DNA sequences and a non-gradient neural network. Biological sequences can be viewed as data components with higher non-f...