Predicting RNA-Protein Interactions Using Only Sequence Information

to related objects

Location: Deutsche Nationalbibliothek Frankfurt am Main

ISSN: 1471-2105

Extent: Online-Ressource

Language: Englisch

Notes: online resource.

Bibliographic citation: Predicting RNA-Protein Interactions Using Only Sequence Information ; volume:12 ; number:1 ; day:22 ; month:12 ; year:2011 ; pages:1-11 ; date:12.2011
BMC bioinformatics ; 12, Heft 1 (22.12.2011), 1-11, 12.2011

Creator: Muppirala, Usha K.

Contributor: Honavar, Vasant G.
Dobbs, Drena
SpringerLink (Online service)

DOI: 10.1186/1471-2105-12-489

URN: urn:nbn:de:1111-2016021729409

Rights: Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:26 AM CEST

Data provider

This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.

Show original at data provider

Associated

Muppirala, Usha K.
Honavar, Vasant G.
Dobbs, Drena
SpringerLink (Online service)

Other Objects (12)

Predicting RNA splicing from DNA sequence using Pangolin

Predicting interactions between small molecules and RNA

Predicting RNA-binding residues from evolutionary information and sequence conservation

Predicting RNA sequence-structure likelihood via structure-aware deep learning

Selecting high-quality negative samples for effectively predicting protein-RNA interactions

Matrix factorization with neural network for predicting circRNA-RBP interactions

Hierarchical folding of multiple sequence alignments for the prediction of structures and RNA-RNA interactions

Predicting drug-target interactions from drug structure and protein sequence using novel convolutional neural networks

Predicting sequence and structural specificities of RNA binding regions recognized by splicing factor SRSF1

Towards in silico CLIP-seq: predicting protein-RNA interaction via sequence-to-signal learning

Predicting lymph node metastasis and prognosis of individual cancer patients based on miRNA-mediated RNA interactions

Hochschulschrift

RNA Energetics And Sequence Design

Predicting RNA splicing from DNA sequence using Pangolin

Predicting interactions between small molecules and RNA

Predicting RNA-binding residues from evolutionary information and sequence conservation

Predicting RNA sequence-structure likelihood via structure-aware deep learning

Selecting high-quality negative samples for effectively predicting protein-RNA interactions

Matrix factorization with neural network for predicting circRNA-RBP interactions

Hierarchical folding of multiple sequence alignments for the prediction of structures and RNA-RNA interactions

Predicting drug-target interactions from drug structure and protein sequence using novel convolutional neural networks

Predicting sequence and structural specificities of RNA binding regions recognized by splicing factor SRSF1

Towards in silico CLIP-seq: predicting protein-RNA interaction via sequence-to-signal learning

Predicting lymph node metastasis and prognosis of individual cancer patients based on miRNA-mediated RNA interactions

Hochschulschrift

RNA Energetics And Sequence Design

Predicting RNA splicing from DNA sequence using Pangolin

Predicting interactions between small molecules and RNA

Predicting RNA-binding residues from evolutionary information and sequence conservation

Predicting RNA sequence-structure likelihood via structure-aware deep learning

Selecting high-quality negative samples for effectively predicting protein-RNA interactions

Matrix factorization with neural network for predicting circRNA-RBP interactions

Hierarchical folding of multiple sequence alignments for the prediction of structures and RNA-RNA interactions

Predicting drug-target interactions from drug structure and protein sequence using novel convolutional neural networks

Predicting sequence and structural specificities of RNA binding regions recognized by splicing factor SRSF1

Towards in silico CLIP-seq: predicting protein-RNA interaction via sequence-to-signal learning

Predicting lymph node metastasis and prognosis of individual cancer patients based on miRNA-mediated RNA interactions

Hochschulschrift

RNA Energetics And Sequence Design

Cultural heritage institutions wishing to register will find more information here.

Fields marked * need to be filled in.

Username*

Please enter your username

Email*

Please enter your email address

Please do not fill this field

First name

Last name

Password*

Please enter your password

Confirm password*

Please enter the same password

I have read and agree to the privacy policy for the collection of personal data.*

This field is required.

I would like to subscribe to the newsletter of the Deutsche Digitale Bibliothek. See newsletter subscription info.

Account created

Your "My DDB" account has been successfully created. Before you can log in to your account, you must click the confirmation link in the message we just sent to the email address you provided.

Predicting RNA-Protein Interactions Using Only Sequence Information

Show object

References and Relationships

Contributors, Places and Time

Further information

Data provider

Associated

Other Objects (12)

Predicting RNA splicing from DNA sequence using Pangolin

Predicting interactions between small molecules and RNA

Predicting RNA-binding residues from evolutionary information and sequence conservation

Predicting RNA sequence-structure likelihood via structure-aware deep learning

Selecting high-quality negative samples for effectively predicting protein-RNA interactions

Matrix factorization with neural network for predicting circRNA-RBP interactions

Hierarchical folding of multiple sequence alignments for the prediction of structures and RNA-RNA interactions

Predicting drug-target interactions from drug structure and protein sequence using novel convolutional neural networks

Predicting sequence and structural specificities of RNA binding regions recognized by splicing factor SRSF1

Towards in silico CLIP-seq: predicting protein-RNA interaction via sequence-to-signal learning

Predicting lymph node metastasis and prognosis of individual cancer patients based on miRNA-mediated RNA interactions

RNA Energetics And Sequence Design

Predicting RNA splicing from DNA sequence using Pangolin

Predicting interactions between small molecules and RNA

Predicting RNA-binding residues from evolutionary information and sequence conservation

Predicting RNA sequence-structure likelihood via structure-aware deep learning

Selecting high-quality negative samples for effectively predicting protein-RNA interactions

Matrix factorization with neural network for predicting circRNA-RBP interactions

Hierarchical folding of multiple sequence alignments for the prediction of structures and RNA-RNA interactions

Predicting drug-target interactions from drug structure and protein sequence using novel convolutional neural networks

Predicting sequence and structural specificities of RNA binding regions recognized by splicing factor SRSF1

Towards in silico CLIP-seq: predicting protein-RNA interaction via sequence-to-signal learning

Predicting lymph node metastasis and prognosis of individual cancer patients based on miRNA-mediated RNA interactions

RNA Energetics And Sequence Design

Predicting RNA splicing from DNA sequence using Pangolin

Predicting interactions between small molecules and RNA

Predicting RNA-binding residues from evolutionary information and sequence conservation

Predicting RNA sequence-structure likelihood via structure-aware deep learning

Selecting high-quality negative samples for effectively predicting protein-RNA interactions

Matrix factorization with neural network for predicting circRNA-RBP interactions

Hierarchical folding of multiple sequence alignments for the prediction of structures and RNA-RNA interactions

Predicting drug-target interactions from drug structure and protein sequence using novel convolutional neural networks

Predicting sequence and structural specificities of RNA binding regions recognized by splicing factor SRSF1

Towards in silico CLIP-seq: predicting protein-RNA interaction via sequence-to-signal learning

Predicting lymph node metastasis and prognosis of individual cancer patients based on miRNA-mediated RNA interactions

RNA Energetics And Sequence Design

Related objects

Reset password