AIMC Topic: Protein Binding

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Biological sequence modeling with convolutional kernel networks.

Bioinformatics (Oxford, England)
MOTIVATION: The growing number of annotated biological sequences available makes it possible to learn genotype-phenotype relationships from data with increasingly high accuracy. When large quantities of labeled samples are available for training a mo...

Prediction of regulatory motifs from human Chip-sequencing data using a deep learning framework.

Nucleic acids research
The identification of transcription factor binding sites and cis-regulatory motifs is a frontier whereupon the rules governing protein-DNA binding are being revealed. Here, we developed a new method (DEep Sequence and Shape mOtif or DESSO) for cis-re...

Multifaceted protein-protein interaction prediction based on Siamese residual RCNN.

Bioinformatics (Oxford, England)
MOTIVATION: Sequence-based protein-protein interaction (PPI) prediction represents a fundamental computational biology problem. To address this problem, extensive research efforts have been made to extract predefined features from the sequences. Base...

GkmExplain: fast and accurate interpretation of nonlinear gapped k-mer SVMs.

Bioinformatics (Oxford, England)
SUMMARY: Support Vector Machines with gapped k-mer kernels (gkm-SVMs) have been used to learn predictive models of regulatory DNA sequence. However, interpreting predictive sequence patterns learned by gkm-SVMs can be challenging. Existing interpreta...

The PSIPRED Protein Analysis Workbench: 20 years on.

Nucleic acids research
The PSIPRED Workbench is a web server offering a range of predictive methods to the bioscience community for 20 years. Here, we present the work we have completed to update the PSIPRED Protein Analysis Workbench and make it ready for the next 20 year...

mCSM-PPI2: predicting the effects of mutations on protein-protein interactions.

Nucleic acids research
Protein-protein Interactions are involved in most fundamental biological processes, with disease causing mutations enriched at their interfaces. Here we present mCSM-PPI2, a novel machine learning computational tool designed to more accurately predic...

PrankWeb: a web server for ligand binding site prediction and visualization.

Nucleic acids research
PrankWeb is an online resource providing an interface to P2Rank, a state-of-the-art method for ligand binding site prediction. P2Rank is a template-free machine learning method based on the prediction of local chemical neighborhood ligandability cent...

AIControl: replacing matched control experiments with machine learning improves ChIP-seq peak identification.

Nucleic acids research
ChIP-seq is a technique to determine binding locations of transcription factors, which remains a central challenge in molecular biology. Current practice is to use a 'control' dataset to remove background signals from a immunoprecipitation (IP) 'targ...

Simple tricks of convolutional neural network architectures improve DNA-protein binding prediction.

Bioinformatics (Oxford, England)
MOTIVATION: With the accumulation of DNA sequencing data, convolution neural network (CNN) based methods such as DeepBind and DeepSEA have achieved great success for predicting the function of primary DNA sequences. Previous studies confirm the impor...

Development of a protein-ligand extended connectivity (PLEC) fingerprint and its application for binding affinity predictions.

Bioinformatics (Oxford, England)
MOTIVATION: Fingerprints (FPs) are the most common small molecule representation in cheminformatics. There are a wide variety of FPs, and the Extended Connectivity Fingerprint (ECFP) is one of the best-suited for general applications. Despite the ove...