NDLI: Interpretable knowledge acquisition for predicting DNA-binding domains using an evolutionary fuzzy classifier method

Content Provider	IEEE Xplore Digital Library
Author	Hui-Ling Huang Fang-Lin Chang Shinn-Jang Ho Li-Sun Shu Shinn-Ying Ho
Copyright Year	2011
Description	Author affiliation: Department of Automation Engineering, National Formosa University, Yunlin 632, Taiwan (Shinn-Jang Ho) \|\| Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan (Hui-Ling Huang; Shinn-Ying Ho) \|\| Department of Information Management, Overseas Chinese University, Taichung 40721, Taiwan (Li-Sun Shu) \|\| Department of Anesthesiology, Tri-Service General Hospital, Taipei, Taiwan (Fang-Lin Chang)
Abstract	DNA-binding domains are functional proteins in a cell, which plays a vital role in various essential biological activities. It is desirable to predict and analyze novel proteins from protein sequences only using machine learning approaches. Numerous prediction methods were proposed by identifying informative features and designing effective classifiers. The support vector machine (SVM) is well recognized as an accurate and robust classifier. However, the block-box mechanism of SVM suffers from low interpretability for biologists. It is better to design a prediction method using interpretable features and prediction results. In this study, we propose an interpretable physicochemical property classifier (named iPPC) with an accurate and compact fuzzy rule base using a scatter partition of feature space for DNA-binding data analysis. In designing iPPC, the flexible membership function, fuzzy rule, and physicochemical properties selection are simultaneously optimized. An intelligent genetic algorithm IGA is used to efficiently solve the design problem with a large number of tuning parameters to maximize prediction accuracy, minimize the number of features selected, and minimize the number of fuzzy rules. Using benchmark datasets of DNA-binding domains, iPPC obtains the training accuracy of 81% and test accuracy of 79% with three fuzzy rules and two physicochemical properties. Compared with the decision tree method with a training accuracy of 77%, iPPC has a more compact and interpretable knowledge base. The two physicochemical properties are Number of hydrogen bond donors and Helix-coil equilibrium constant in the AAindex database.
Starting Page	295
Ending Page	299
File Size	163436
Page Count	5
File Format	PDF
ISBN	9781424487271
e-ISBN	9781424487288
DOI	10.1109/CSAE.2011.5952854
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy classifier Prediction Amino acids DNA-binding Proteins Training Support vector machines Accuracy Genetic algorithm DNA Bioinformatics Knowledge acquistion Physicochemical properties
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Interpretable Knowledge Acquisition for Predicting Bioluminescent Proteins Using an Evolutionary Fuzzy Classifier Method

Predicting protein crystallization using a simple scoring card method

Designing predictors of halophilic and non-halophilic proteins using support vector machines

Designing predictors of DNA-binding proteinsusing an efficient physicochemical propertymining method

Choquet integral algorithm for T-cell epitope prediction based on fuzzy measure

Predicting DNA-Binding Residues of Proteins Using Random Forest and Evolutionary Information Combined with Conservation Information

Prediction of Heme Binding Sites in Heme Proteins Using an Integrative Sequence Profile Coupling Evolutionary Information with Physicochemical Properties

Prediction of Protein-Protein Interaction Using Distance Frequency of Amino Acids Grouped with their Physicochemical Properties

Extracting Sequence Features to Predict DNA-Binding Proteins Using Support Vector Machine

Interpretable knowledge acquisition for predicting DNA-binding domains using an evolutionary fuzzy classifier method

Similar Documents

Interpretable Knowledge Acquisition for Predicting Bioluminescent Proteins Using an Evolutionary Fuzzy Classifier Method

Predicting protein crystallization using a simple scoring card method

Designing predictors of halophilic and non-halophilic proteins using support vector machines

Designing predictors of DNA-binding proteinsusing an efficient physicochemical propertymining method

Choquet integral algorithm for T-cell epitope prediction based on fuzzy measure

Predicting DNA-Binding Residues of Proteins Using Random Forest and Evolutionary Information Combined with Conservation Information

Prediction of Heme Binding Sites in Heme Proteins Using an Integrative Sequence Profile Coupling Evolutionary Information with Physicochemical Properties

Prediction of Protein-Protein Interaction Using Distance Frequency of Amino Acids Grouped with their Physicochemical Properties

Extracting Sequence Features to Predict DNA-Binding Proteins Using Support Vector Machine

Interpretable knowledge acquisition for predicting DNA-binding domains using an evolutionary fuzzy classifier method