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RNA

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Research on automatic preprocessing equipment of salivary analyte for PCR inspection of coronavirus.

Science progress
Polymerase chain reaction (PCR) inspection of salivary analyte is performed by pretreatment, RNA extraction setup, RNA extraction, PCR setup, and the PCR process. However, the pretreatment process is conducted manually, and it is a bottleneck to the ...

GraphBind: protein structural context embedded rules learned by hierarchical graph neural networks for recognizing nucleic-acid-binding residues.

Nucleic acids research
Knowledge of the interactions between proteins and nucleic acids is the basis of understanding various biological activities and designing new drugs. How to accurately identify the nucleic-acid-binding residues remains a challenging task. In this pap...

Interpretation of deep learning in genomics and epigenomics.

Briefings in bioinformatics
Machine learning methods have been widely applied to big data analysis in genomics and epigenomics research. Although accuracy and efficiency are common goals in many modeling tasks, model interpretability is especially important to these studies tow...

Modeling multi-species RNA modification through multi-task curriculum learning.

Nucleic acids research
N6-methyladenosine (m6A) is the most pervasive modification in eukaryotic mRNAs. Numerous biological processes are regulated by this critical post-transcriptional mark, such as gene expression, RNA stability, RNA structure and translation. Recently, ...

Prediction of bio-sequence modifications and the associations with diseases.

Briefings in functional genomics
Modifications of protein, RNA and DNA play an important role in many biological processes and are related to some diseases. Therefore, accurate identification and comprehensive understanding of protein, RNA and DNA modification sites can promote rese...

Deep Neural Networks for Epistatic Sequence Analysis.

Methods in molecular biology (Clifton, N.J.)
We report a step-by-step protocol to use pysster, a TensorFlow-based package for building deep neural networks on a broad range of epistatic sequences such as DNA, RNA, or annotated secondary structure sequences. Pysster provides users comprehensive ...

DeepCLIP: predicting the effect of mutations on protein-RNA binding with deep learning.

Nucleic acids research
Nucleotide variants can cause functional changes by altering protein-RNA binding in various ways that are not easy to predict. This can affect processes such as splicing, nuclear shuttling, and stability of the transcript. Therefore, correct modeling...

Graph neural representational learning of RNA secondary structures for predicting RNA-protein interactions.

Bioinformatics (Oxford, England)
MOTIVATION: RNA-protein interactions are key effectors of post-transcriptional regulation. Significant experimental and bioinformatics efforts have been expended on characterizing protein binding mechanisms on the molecular level, and on highlighting...

iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data.

Briefings in bioinformatics
With the explosive growth of biological sequences generated in the post-genomic era, one of the most challenging problems in bioinformatics and computational biology is to computationally characterize sequences, structures and functions in an efficie...

Uncovering tissue-specific binding features from differential deep learning.

Nucleic acids research
Transcription factors (TFs) can bind DNA in a cooperative manner, enabling a mutual increase in occupancy. Through this type of interaction, alternative binding sites can be preferentially bound in different tissues to regulate tissue-specific expres...