Publications

@misc{Nordling2019ICSB,

title = {ProtFunAI–An artificial intelligence for prediction of protein function from protein sequence alone},

author = {Chia-Ming Lin and Po-Jung Tsai and Torbjörn E M Nordling},

url = {https://www2.aeplan.co.jp/icsb2019/images/Program191009.pdf},

year  = {2019},

date = {2019-11-01},

booktitle = {The 20th International Conference on Systems Biology (ICSB-2019) in Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan: Abstract book},

pages = {52},

publisher = {Okinawa Institute of Science and Technology (OIST) Graduate University},

address = {Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan},

abstract = {Knowledge about the function of a protein is essential for understanding its role. Prediction of protein functions from the protein sequence alone using computational methods has been attempted. Previously, we created FFANEprot— a deep convolutional neural network trained on a dataset of 81,267 proteins and 1,169 Gene Ontology (GO) terms of the molecular function (MF) from the Swiss-Prot database. It is the best predictor of GO MFs from protein sequence alone, with training and test Matthews correlation coefficients (accuracies) of 0.52 (98.84%) and 0.49 (98.67%), respectively. Based on FFANEprot, we here present the ProtFunAI web service (protfunai.nordlinglab.org) consisting of a database of MF predictions of 20,405 reviewed human proteins and a prediction service that can predict the MF of any supplied protein sequence within roughly a minute.},

howpublished = {The 20th International Conference on Systems Biology (ICSB-2019) in Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan},

keywords = {Convolutional neural network, Protein function, Systems Biology},

pubstate = {published},

tppubtype = {misc}

}