20 skills found
duolinwang / MusiteDeepMusiteDeep provides a deep-learning method for general and kinase-specific phosphorylation site prediction. It is implemented by deep learning library Keras and Theano backend (the Keras2.0 and Tensorflow backend implementation were also provided under folder MusiteDeep_Keras2.0). At present, MusiteDeep only provides prediction of human phosphorylation sites; however, it also provides customized model training that enables users to train other PTM prediction models by using their own training data sets based on either CPU or GPU.
evocellnet / FunscoRR package for functionally scoring phosphorylation sites
TheKinaseLibrary / Kinase LibraryThe Kinase Library: a Global Atlas of the Human Protein Kinome
jdrudolph / PhotonPHOsphoproteomic dissecTiOn using Networks
ustchangyuanyang / PhosIDNPhosIDN: an integrated deep neural network for improving protein phosphorylation site prediction by combining sequence and protein–protein interaction information
omarwagih / RmimpPredicting the impact of mutations on kinase–substrate phosphorylation
ZhangJJ26 / SAGEPhosSAGEPhos: Sage Bio-Coupled and Augmented Fusion for Phosphorylation Site Detection
saezlab / PHONEMeSPHONEMeS (PHOsphorylation NEtworks for Mass Spectrometry) is an R package to model signalling networks based on untargeted phosphoproteomics
gankLei-X / DeepPSPDeepPSP: A Global−Local Information-Based Deep Neural Network for the Prediction of Protein Phosphorylation Sites
jasperzuallaert / PhosphoLingoPhosphorylation site prediction software using protein language models and convolutional neural networks
KCLabMTU / LMPhosSiteA deep learning-based approach for general protein phosphorylation site prediction using embeddings from local window sequence and pre-trained Protein Language Model
KarrLab / BpformsToolkit for concretely describing non-canonical DNA, RNA, and proteins
kmezhoud / CanceR:chart: The package is user friendly interface based on the cgdsr and other modeling packages to explore, compare, and analyse all available Cancer Data (Clinical data, Gene Mutation, Gene Methylation, Gene Expression, Protein Phosphorylation, Copy Number Alteration) hosted by the Computational Biology Center at Memorial-Sloan-Kettering Cancer Center (MSKCC).
StatXzy7 / PTransIPsPTransIPs: Identification of phosphorylation sites based on protein pretrained language model and Transformer
frl21 / Phosphorylation PredictionThis project is conduct research using deep learning to predict phosphorylation site in protein sequences
kusterlab / ATLANTiCIntegrated analysis of genomes, transcriptomes, proteomes and drug responses of cancer cell lines (CCLs) is an emerging approach to uncover molecular mechanisms of drug action. We extend this paradigm to measuring proteome activity landscapes by acquiring and integrating quantitative data for 10,000 proteins and 55,000 phosphorylation sites (p-sites) from 125 CCLs. These data are used to contextualize proteins and p-sites and predict drug sensitivity. For example, we find that Progesterone Receptor (PGR) phosphorylation is associated with sensitivity to drugs modulating estrogen signaling such as Raloxifene. We also demonstrate that Adenylate kinase isoenzyme 1 (AK1) inactivates antimetabolites like Cytarabine. Consequently, high AK1 levels correlate with poor survival of Cytarabine-treated acute myeloid leukemia patients, qualifying AK1 as a patient stratification marker and possibly as a drug target. We provide an interactive web application termed ATLANTiC (http://atlantic.proteomics.wzw.tum.de), which enables the community to explore the thousands of novel functional associations generated by this work.
danmaclean / PhosCalcThe Sainsbury Lab Tool for estimating the likelihood of phosphorylation at possible sites in a peptide from Mass Spec data
JMLab-tifrh / P Ash1 P Sic1Study on phosphorylation of ash1 and sic1
eliza-m / CrossSpeciesWorkflowCWL workflow that facilitate performing a series of structural and phenotype related third party prediction methods starting from either a protein FASTA file or a list of Uniprot IDs. Integrated prediction methods refer to secondary structure, solvent accessibility, disordered regions, PTS modifications (phosphorylation, glycosylation, lipid modification, sumoylation, etc).
lennylv / DeepMPSFThe source code, training and test datasets of paper 'DeepMPSF: A Deep Learning Network for Predicting General Protein Phosphorylation Sites Based on Multiple Protein Sequence Features'
