Machine learning techniques for protein function prediction

2019 ◽  
Vol 88 (3) ◽  
pp. 397-413 ◽  
Author(s):  
Rosalin Bonetta ◽  
Gianluca Valentino
Keyword(s):  
Machine Learning ◽  
Protein Function ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Machine Learning Techniques ◽  
Learning Techniques

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 Assessing the Performances of Protein Function Prediction Algorithms from the Perspectives of Identification Accuracy and False Discovery Rate

2017 ◽  
Author(s):  
Chunyan Yu ◽  
Xiaoxu Li ◽  
Hong Yang ◽  
Yinghong Li ◽  
Weiwei Xue ◽  
...  
Keyword(s):  
Machine Learning ◽  
False Discovery Rate ◽  
Protein Function ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Machine Learning Algorithms ◽  
Identification Accuracy ◽  
Homologous Proteins ◽  
Prediction Algorithms ◽  
False Discovery

The knowledge of protein function is essential for the study of biological processes, the understanding of disease mechanism and the exploration of novel therapeutic target. Apart from experimental methods, a number of in-silico approaches have been developed and extensively used for protein function prediction. Among these approaches, BLAST predicts functions based on protein sequence similarity, and machine learning predicts functional families from protein sequences irrespective of their similarity, which complements BLAST and other methods in predicting diverse classes of proteins including distantly related proteins and homologous proteins of different functions. However, their identification accuracies and the false discovery rate have not yet been assessed so far, which greatly limits the usage of these prediction algorithms. Herein, a comprehensive comparison of the performances among four popular functional prediction algorithms (BLAST, SVM, PNN and KNN) was conducted. In particular, the performance of these algorithms were systematically assessed by four metrics (sensitivity, specificity, accuracy and Matthews correlation coefficient) based on the independent test datasets generated from 93 protein families defined by UniProtKB Keywords. Moreover, the false discovery rates of these algorithms were evaluated by scanning the genomes of four representative model species (homo sapiens, arabidopsis thaliana, saccharomyces cerevisiae and mycobacterium tuberculosis). As a result, the substantially higher sensitivity and stability of BLAST and SVM were observed compared with that of PNN and KNN. But the machine learning algorithms (PNN, KNN and SVM) were found capable of significantly reducing the false discovery rate (SVM < PNN ≈ KNN). In summary, this study comprehensively assessed the performance of four popular algorithms applied to protein function prediction, which could facilitate the selection of the most appropriate method in the related biomedical research.

scholarly journals Fiscore Package: Effective Protein Structural Data Visualisation and Exploration

2021 ◽  
Author(s):  
Auste Kanapeckaite
Keyword(s):  
Machine Learning ◽  
Protein Function ◽  
Mathematical Formulation ◽  
Structural Data ◽  
R Package ◽  
Gaussian Mixture ◽  
Structural Features ◽  
Machine Learning Techniques ◽  
Topological Features ◽  
Learning Techniques

Lack of bioinformatics tools to quickly assess protein conformational and topological features motivated to create an integrative and user-friendly R package. Moreover,Fiscore package implements a pipeline for Gaussian mixture modelling making such machine learning techniques readily accessible to non-experts. This is especially important since probabilistic machine learning techniques can help with a better interpretation of complex biological phenomena when it is necessary to elucidate various structural features that might play a role in protein function. Thus,Fiscore package builds on the mathematical formulation of protein physicochemical properties that can aid in drug discovery, target evaluation, or relational database building. Moreover, the package provides interactive environments to explore various features of interest. Finally, one of the goals of this package was to engage structural bioinformaticians and develop more R tools that could help researchers not necessarily specialising in this field. Package Fiscore(v.0.1.2) is distributed via CRAN and Github.

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