scholarly journals 2P-242 FCANAL, structure-based protein function prediction method, applied to various types of proteins(Bioinformatics:Functional genomics,The 47th Annual Meeting of the Biophysical Society of Japan)

2009 ◽  
Vol 49 (supplement) ◽  
pp. S145
Author(s):  
Yuuichi Watanabe ◽  
Kousuke Kaido ◽  
Takashi Ando ◽  
Ichiro Yamato ◽  
Satoru Miyazaki
Keyword(s):  
Annual Meeting ◽  
Protein Function ◽  
Protein Function Prediction ◽  
Prediction Method ◽  
Function Prediction ◽  
Biophysical Society

scholarly journals DeepGOZero: Improving protein function prediction from sequence and zero-shot learning based on ontology axioms

2022 ◽  
Author(s):  
Maxat Kulmanov ◽  
Robert Hoehndorf
Keyword(s):  
Machine Learning ◽  
Protein Function ◽  
Protein Function Prediction ◽  
Prediction Method ◽  
Function Prediction ◽  
Training Data ◽  
Large Set ◽  
Theoretic Approach ◽  
Machine Learning Model ◽  
Protein Functions

Motivation: Protein functions are often described using the Gene Ontology (GO) which is an ontology consisting of over 50,000 classes and a large set of formal axioms. Predicting the functions of proteins is one of the key challenges in computational biology and a variety of machine learning methods have been developed for this purpose. However, these methods usually require significant amount of training data and cannot make predictions for GO classes which have only few or no experimental annotations. Results: We developed DeepGOZero, a machine learning model which improves predictions for functions with no or only a small number of annotations. To achieve this goal, we rely on a model-theoretic approach for learning ontology embeddings and combine it with neural networks for protein function prediction. DeepGOZero can exploit formal axioms in the GO to make zero-shot predictions, i.e., predict protein functions even if not a single protein in the training phase was associated with that function. Furthermore, the zero-shot prediction method employed by DeepGOZero is generic and can be applied whenever associations with ontology classes need to be predicted. Availability: http://github.com/bio-ontology-research-group/deepgozero

scholarly journals Phylo-PFP: improved automated protein function prediction using phylogenetic distance of distantly related sequences

2018 ◽  
Vol 35 (5) ◽  
pp. 753-759 ◽  
Author(s):  
Aashish Jain ◽  
Daisuke Kihara
Keyword(s):  
Protein Function ◽  
Transfer Functions ◽  
Sequence Similarity ◽  
Protein Function Prediction ◽  
Prediction Method ◽  
Query Protein ◽  
Function Prediction ◽  
Homology Search ◽  
Supplementary Information ◽  
Phylogenetic Distance

Abstract Motivation Function annotation of proteins is fundamental in contemporary biology across fields including genomics, molecular biology, biochemistry, systems biology and bioinformatics. Function prediction is indispensable in providing clues for interpreting omics-scale data as well as in assisting biologists to build hypotheses for designing experiments. As sequencing genomes is now routine due to the rapid advancement of sequencing technologies, computational protein function prediction methods have become increasingly important. A conventional method of annotating a protein sequence is to transfer functions from top hits of a homology search; however, this approach has substantial short comings including a low coverage in genome annotation. Results Here we have developed Phylo-PFP, a new sequence-based protein function prediction method, which mines functional information from a broad range of similar sequences, including those with a low sequence similarity identified by a PSI-BLAST search. To evaluate functional similarity between identified sequences and the query protein more accurately, Phylo-PFP reranks retrieved sequences by considering their phylogenetic distance. Compared to the Phylo-PFP’s predecessor, PFP, which was among the top ranked methods in the second round of the Critical Assessment of Functional Annotation (CAFA2), Phylo-PFP demonstrated substantial improvement in prediction accuracy. Phylo-PFP was further shown to outperform prediction programs to date that were ranked top in CAFA2. Availability and implementation Phylo-PFP web server is available for at http://kiharalab.org/phylo_pfp.php. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals 2P303 FCANAL : A structure based protein function prediction method. Application to enzymes and binding proteins

2005 ◽  
Vol 45 (supplement) ◽  
pp. S195
Author(s):  
A. Suzuki ◽  
T. Ando ◽  
A. Matsumura ◽  
H. Sakao ◽  
I. Yamato ◽  
...  
Keyword(s):  
Protein Function ◽  
Binding Proteins ◽  
Protein Function Prediction ◽  
Prediction Method ◽  
Function Prediction

scholarly journals FCANAL: Structure based protein function prediction method. Application to enzymes and binding proteins

2005 ◽  
Vol 5 (3) ◽  
pp. 39-55
Author(s):  
Ayumi Suzuki ◽  
Tadashi Ando ◽  
Ichiro Yamato ◽  
Satoru Miyazaki
Keyword(s):  
Protein Function ◽  
Binding Proteins ◽  
Protein Function Prediction ◽  
Prediction Method ◽  
Function Prediction
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