Tag: Neural Network

SGNNMD – Signed Graph Neural Network for Predicting Deregulation Types of MiRNA-disease Associations

SGNNMD

:: DESCRIPTION

SGNNMD extracts subgraphs around miRNA-disease pairs from the signed bipartite network and learns structural features of subgraphs via a labeling algorithm and a neural network, and then combines them with biological features (i.e. miRNA-miRNA functional similarity and disease-disease semantic similarity) to build the prediction model.

::DEVELOPER

Zhang Wen

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

SGNNMD

:: MORE INFORMATION

Citation

Zhang G, Li M, Deng H, Xu X, Liu X, Zhang W.
SGNNMD: signed graph neural network for predicting deregulation types of miRNA-disease associations.
Brief Bioinform. 2021 Dec 8:bbab464. doi: 10.1093/bib/bbab464. Epub ahead of print. PMID: 34875683.

EditPredict – Prediction of RNA editable sites with convolutional Neural network

EditPredict

:: DESCRIPTION

EditPredict is a sequence-only, sequencing-independent tool, which can be used stand-alone to predict novel RNA editing and to assist in filtering for candidate RNA editing detected from RNA-Seq data.

::DEVELOPER

Steven Wong

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

EditPredict

:: MORE INFORMATION

Citation

Wang J, Ness S, Brown R, Yu H, Oyebamiji O, Jiang L, Sheng Q, Samuels DC, Zhao YY, Tang J, Guo Y.
EditPredict: Prediction of RNA editable sites with convolutional neural network.
Genomics. 2021 Sep 23;113(6):3864-3871. doi: 10.1016/j.ygeno.2021.09.016. Epub ahead of print. PMID: 34562567.

INSCT – scRNAseq Integration with Triplet Neural Networks

INSCT

:: DESCRIPTION

INSCT (INtegration of millions of Single Cells using batch-aware Triplet networks) is a deep learning algorithm which calculates an integrated embedding for scRNA-seq data.

::DEVELOPER

Bioinformatics and Systems Medicine Laboratory

:: SCREENSHOTS

n/a

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

INSCT

:: MORE INFORMATION

Citation:

Simon, L.M., Wang, YY. & Zhao, Z.
Integration of millions of transcriptomes using batch-aware triplet neural networks.
Nat Mach Intell 3, 705–715 (2021).
https://doi.org/10.1038/s42256-021-00361-8

DPPI – Convolutional Neural network to predict PPI Interactions

DPPI

:: DESCRIPTION

DPPI uses a convolutional neural network to predict PPI interactions by using only the protein sequences.

::DEVELOPER

Somaye Hashemifar

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

DPPI

:: MORE INFORMATION

Citation:

Hashemifar S, Neyshabur B, Khan AA, Xu J.
Predicting protein-protein interactions through sequence-based deep learning.
Bioinformatics. 2018 Sep 1;34(17):i802-i810. doi: 10.1093/bioinformatics/bty573. PMID: 30423091; PMCID: PMC6129267.

DanQ – A Hybrid Convolutional and Recurrent Neural Network for predicting the function of DNA Sequences

DanQ

:: DESCRIPTION

DanQ is a hybrid convolutional and recurrent neural network model for predicting the function of DNA de novo from sequence.

::DEVELOPER

CBCL Lab (Computational Biology and Computational Learning) @ UCI

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

 DanQ

:: MORE INFORMATION

Citation

DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences.
Quang D, Xie X.
Nucleic Acids Res. 2016 Apr 15. pii: gkw226.

PSSpred v2 – Multiple Neural Network Training program for Protein Secondary Strucure Prediction

PSSpred v2

:: DESCRIPTION

PSSpred (Protein Secondary Structure PREDiction) is a simple neural network training algorithm for accurate protein secondary structure prediction. It first collects multiple sequence alignments using PSI-BLAST. Amino-acid frequence and log-odds data with Henikoff weights are then used to train secondary structure, separately, based on the Rumelhart error backpropagation method. The final secondary structure prediction result is a combination of 7 neural network predictors from different profile data and parameters.

::DEVELOPER

Yang Zhang’s Research Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 PSSpred

:: MORE INFORMATION

NetChop 3.1d – Neural Network Predictions for Cleavage Sites of Human Proteasome

NetChop 3.1d

:: DESCRIPTION

NetChop produces neural network predictions for cleavage sites of the human proteasome.

NetChop has been trained on human data only, and will therefore presumably have better performance for prediction of the cleavage sites of the human proteasome. However, since the proteasome structure is quite conserved, we believe that the server is able to produce reliable predictions for at least the other mammalian proteasomes.

::DEVELOPER

DTU Health Tech

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

NetChop

:: MORE INFORMATION

Citation

The role of the proteasome in generating cytotoxic T cell epitopes: Insights obtained from improved predictions of proteasomal cleavage.
M. Nielsen, C. Lundegaard, O. Lund, and C. Kesmir. Immunogenetics., 57(1-2):33-41, 2005.

BetaTPred2: Prediction of Beta-turns in Proteins using Neural Networks and Multiple Alignment

BetaTPred2

:: DESCRIPTION

The aim of BetaTPred2 server is to predict Beta turns in proteins from multiple alignment by using neural network from the given amino acid sequence. For ? turn prediction, it uses the position specific score matrices generated by PSI-BLAST and secondary structure predicted by PSIPRED. The net is trained and tested on a set of 426 non-homologous protein chains with 7-fold cross-validation. It predicts ? turns in proteins with residue accuracy of 75.5% and MCC value of 0.43.

::DEVELOPER

BetaTPred2 team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

Kaur, H. and Raghava, G.P.S.
Prediction of beta-turns in proteins from multiple alignment using neural network.
Protein Science 2003 12: 627-634

nHLAPred – Neural Network based MHC Class-I Binding Peptide Prediction Server

nHLAPred

:: DESCRIPTION

nHLAPred allow to predict binding peptide for 67 MHC Class I alleles. This also allow to predict the proteasome cleavage site and binding peptide that have cleavage site at C terminus (potential T cell epitopes).

::DEVELOPER

nHLAPred Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

Bhasin M. and Raghava G P S (2006)
A hybrid approach for predicting promiscuous MHC class I restricted T cell epitopes;
J. Biosci. 32:31-42.

NETASA – Prediction of Solvent Accessibility using Neural Networks

NETASA

:: DESCRIPTION

NETASA ,a server, for predicting solvent accessibility of amino acids using our newly optimized neural network algorithm. Several new features in the neural network architecture and training method have been introduced, and the network learns faster to provide accuracy values, which are comparable or better than other methods of ASA prediction.

::DEVELOPER

Shandar Ahmad

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Server

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

NETASA: neural network based prediction of solvent accessibility.
Ahmad S, Gromiha MM.
Bioinformatics. 2002 Jun;18(6):819-24.