Filters








43,677 Hits in 4.9 sec

DrugMiner: comparative analysis of machine learning algorithms for prediction of potential druggable proteins

Ali Akbar Jamali, Reza Ferdousi, Saeed Razzaghi, Jiuyong Li, Reza Safdari, Esmaeil Ebrahimie
2016 Drug Discovery Today  
Ebrahimie DrugMiner: comparative analysis of machine learning algorithms for prediction of potential druggable proteins Drug Discovery Today, 2016; 21(5):718-724 After the embargo period  via non-commercial  ...  Implemented machine-learning predictors Here, we evaluated different machine-learning approaches to determine which one predicted drug targets with the highest performance.  ...  In this study, we applied different machine-learning algorithms to make multi-step predictions on various structural and functional features of proteins.  ... 
doi:10.1016/j.drudis.2016.01.007 pmid:26821132 fatcat:shukynoncfba5i3h7pzxl46aba

Transient protein-protein interface prediction: datasets, features, algorithms, and the RAD-T predictor

Calem J Bendell, Shalon Liu, Tristan Aumentado-Armstrong, Bogdan Istrate, Paul T Cernek, Samuel Khan, Sergiu Picioreanu, Michael Zhao, Robert A Murgita
2014 BMC Bioinformatics  
We reveal the need for a larger test set of well studied proteins or domain-specific scoring algorithms to compensate for poor interaction site identification on proteins in general.  ...  Conclusion: Current methods of evaluating machine-learning based PPI predictors tend to undervalue their performance, which may be artificially decreased by the presence of un-identified interaction sites  ...  We thank Joelle Pineau for her constructive comments on the manuscript and her machine learning expertise.  ... 
doi:10.1186/1471-2105-15-82 pmid:24661439 pmcid:PMC4021185 fatcat:drat7w6xcfc4tmu5n2mv4booqa

Predicting Cancer-Related Proteins in Protein-Protein Interaction Networks using Network Approach and SMO-SVM Algorithm

Richard Enyinnaya
2015 International Journal of Computer Applications  
The WEKA software was utilized as the data mining tool, which is an open source collection of machine learning algorithms.  ...  This paper explores genomic interactions networks, investigating protein-protein interaction networks to predict cancer related proteins using sequential minimal Optimization (SMO) for training Support  ...  Precision, recall and F-measure are common evaluation measures utilized in evaluating machine learning performance experiments.  ... 
doi:10.5120/20129-2212 fatcat:uspqkui2wvbkbapirdhy63rb4a

Machine Learning Approaches for Protein–Protein Interaction Hot Spot Prediction: Progress and Comparative Assessment

Siyu Liu, Chuyao Liu, Lei Deng
2018 Molecules  
Here, we describe the basic concepts and recent advances of machine learning applications in inferring the proteinprotein interaction hot spots, and assess the performance of widely used features, machine  ...  learning algorithms, and existing state-of-the-art approaches.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/molecules23102535 fatcat:ygvldxplbza5dbi3avnihfzgta

Random Forest Algorithm for Enhanced Prediction of Drug Target Interactions

2020 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
The performance of proposed approach is evaluated with respect to Matrix factorization, genetic algorithm, Support vector machines, K-nearest neighbor, Decision Trees and Logistic Regression over 4 benchmark  ...  In the recent past, machine learning algorithms have become very popular for DTI predictions. This paper presents an ensemble approach- Random forest algorithm for DTI predictions.  ...  This work primarily focuses on machine learning algorithms.  ... 
doi:10.35940/ijitee.d1722.029420 fatcat:zbozzgrka5auraqfb4susfd3ea

Prediction of Druggable Proteins Using Machine Learning and Systems Biology: A Mini-Review

Gaurav Kandoi, Marcio L. Acencio, Ney Lemke
2015 Frontiers in Physiology  
In this mini-review, we describe promising approaches based on the simultaneous use of systems biology and machine learning to access gene and protein druggability.  ...  Machine learning techniques are powerful tools that can extract relevant information from massive and noisy data sets.  ...  MLA has been supported by the Coordination for the Improvement of Higher Education Personnel (CAPES) in Brazil.  ... 
doi:10.3389/fphys.2015.00366 pmid:26696900 pmcid:PMC4672042 fatcat:rvenukj5pzca5irvgbpu4xt4ha

A Novel Approach for Protein-Named Entity Recognition and Protein-Protein Interaction Extraction

Meijing Li, Tsendsuren Munkhdalai, Xiuming Yu, Keun Ho Ryu
2015 Mathematical Problems in Engineering  
The Protein-NER model, which is named ProNER, identifies the protein names based on two methods: dictionary-based method and machine learning-based method.  ...  In this paper, we developed the protein-protein interaction extraction system named PPIMiner based on Support Vector Machine (SVM) and parsing tree.  ...  Machine learning-based approach is a method which applies machine learning algorithm to classify bionamed entities from sentences.  ... 
doi:10.1155/2015/942435 fatcat:yja77iepjrhlfmdyriafur5jdm

Machine learning in plant disease research

Xin Yang, Tingwei Guo
2017 European Journal of BioMedical Research  
A good example of applying machine learning in plant-pathogen interaction research by Pal et al. showed that supporting vector machine (SVM), which was used to predict plant resistance proteins (R proteins  ...  A typical process of employing machine learning includes data collection, dataset preparation, feature extraction, preprocessing, feature selection, choosing and applying machine learning algorithms and  ...  Examples in machine learning in prediction of plant-pathogen interactions.  ... 
doi:10.18088/ejbmr.3.1.2017.pp6-9 fatcat:xcscviphvrgjfez7m4257wi5fa

Exploring the Computational Methods for Protein- Ligand Binding Site Prediction

Jingtian Zhao, Yang Cao, Le Zhang
2020 Computational and Structural Biotechnology Journal  
We describe representative algorithms in each category and elaborate on machine learning and deep learning-based prediction methods in more detail.  ...  , traditional machine learning-based and deep learning-based methods.  ...  Acknowledgments Conflict of Interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of  ... 
doi:10.1016/j.csbj.2020.02.008 pmid:32140203 pmcid:PMC7049599 fatcat:rdwwizqbirhozm3ski45nom3we

A model to predict the function of hypothetical proteins through a nine-point classification scoring schema

Johny Ijaq, Girik Malik, Anuj Kumar, Partha Sarathi Das, Narendra Meena, Neeraja Bethi, Vijayaraghava Seshadri Sundararajan, Prashanth Suravajhala
2019 BMC Bioinformatics  
We discuss the challenges and performance of these classifiers using machine learning heuristics with an improved accuracy from Perceptron (81.08 to 97.67), Naive Bayes (54.05 to 96.67), Decision tree  ...  best BLAST hits, sorting signals, known databases and visualizers which were used to validate protein interactions.  ...  JI acknowledges the support of CSIR for providing a research fellowship to pursue his Ph.D.  ... 
doi:10.1186/s12859-018-2554-y fatcat:oy4up2xx6bhrnpoe2z3g6qdvme

Predicting Essential Genes and Proteins Based on Machine Learning and Network Topological Features: A Comprehensive Review

Xue Zhang, Marcio Luis Acencio, Ney Lemke
2016 Frontiers in Physiology  
In this review, we show the state-of-the-art methods for identifying essential genes and proteins based on machine learning and network topological features, point out the progress and limitations of current  ...  Essential proteins/genes are indispensable to the survival or reproduction of an organism, and the deletion of such essential proteins will result in lethality or infertility.  ...  MLA was supported by the Coordination for the Improvement of Higher Education Personnel (CAPES) in Brazil.  ... 
doi:10.3389/fphys.2016.00075 pmid:27014079 pmcid:PMC4781880 fatcat:xqbey7p22beyxfysmwocmcjwuy

A Survey of Machine Learning models for the wide Spectrum of Computational Biology

Divya Ebenezer Nathaniel, Sonia Panesar
2019 International Journal of Scientific Research in Computer Science Engineering and Information Technology  
The machine learning being a subfield of Artificial Intelligence is used in numerous research works.  ...  There are numerous kinds of Machine Learning Techniques like Unsupervised, Semi Supervised, Supervised, Reinforcement, and Evolutionary Learning and Deep Learning.  ...  Drug repurposing approaches can be categorized into four groups: (1) On the basis of protein target interaction networks the method predict different uses for currently existing drugs and machine learning  ... 
doi:10.32628/cseit2063149 fatcat:55ci3t7vcze7ljji5miqn5j5p4

Accurate Prediction of ncRNA-Protein Interactions From the Integration of Sequence and Evolutionary Information

Zhao-Hui Zhan, Zhu-Hong You, Li-Ping Li, Yong Zhou, Hai-Cheng Yi
2018 Frontiers in Genetics  
Therefore, an accurate understanding of interactions between ncRNA and protein has a significant impact on current biological research.  ...  On four widely used datasets RPI369, RPI488, RPI1807, and RPI2241, we evaluated the performance of LGBM and obtained an superior performance with AUC of 0.799, 0.914, 0.989, and 0.762, respectively.  ...  ACKNOWLEDGMENTS This work is supported in part by the National Science Foundation of China, under Grants 61373086, 61572506.  ... 
doi:10.3389/fgene.2018.00458 pmid:30349558 pmcid:PMC6186793 fatcat:gewwee2s5zfbhawibztdcg3ugy

AN EMPIRICAL SCIENCE RESEARCH ON BIOINFORMATICS IN MACHINE LEARNING

Sindhu V
2020 JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES  
The subset of Artificial Intelligence (AI) is Machine Learning.  ...  Machine Learning (ML) has a rapid growth in all fields of research such as medical, biosurveillance, robotics and all other industrial applications.  ...  Learning by algorithm refers to program that instructs the computer. Output will be result of machine interaction around it like navigation, generation of speech etc.  ... 
doi:10.26782/jmcms.spl.7/2020.02.00006 fatcat:u5hkuxgamrfw3heyd5xznjnoru

GraphDTI: A robust deep learning predictor of drug-target interactions from multiple heterogeneous data

Guannan Liu, Manali Singha, Limeng Pu, Prasanga Neupane, Joseph Feinstein, Hsiao-Chun Wu, J. Ramanujam, Michal Brylinski
2021 Journal of Cheminformatics  
information on gene expression and protein-protein interactions.  ...  AbstractTraditional techniqueset identification, we developed GraphDTI, a robust machine learning framework integrating the molecular-level information on drugs, proteins, and binding sites with the system-level  ...  Acknowledgements Portions of this research were conducted with high-performance computational resources provided by Louisiana State University.  ... 
doi:10.1186/s13321-021-00540-0 fatcat:bogrqny6pjd5lhdowfxziflvv4
« Previous Showing results 1 — 15 out of 43,677 results