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Incremental Learning for Classification of Protein Sequences

Shakir Mohamed, David Rubin, Tshilidzi Marwala
2007 Neural Networks (IJCNN), International Joint Conference on  
The problem of protein structural family classification remains a core problem in computational biology, with application of this technology applicable to problems in drug discovery programs and hypothetical  ...  We utilize the fuzzy ARTMAP as an alternate machine learning system that has the ability of incrementally learning new data as it becomes available.  ...  This paper introduces the use of a classification system based upon an evolutionary strategy, incremental learning and the Fuzzy ARTMAP to realise a protein classification system for the GPCR protein superfamily  ... 
doi:10.1109/ijcnn.2007.4370924 dblp:conf/ijcnn/MohamedRM07 fatcat:qcwqdauubvgzbo7c6mnxh7ug7q

An adaptive strategy for the classification of g-protein coupled receptors

S. Mohamed, D. Rubin, T. Marwala
2007 SAIEE Africa Research Journal  
The prohlem of static classification models is addressed in this paper by the introduction of incrcmelllal learning for problems in hioinformatics.  ...  We utilize the fuzzy ARTMAP as an alternate machine learning system that has the ahility of incrementally learning new data as it becomes available.  ...  This paper introduces the use of a classification system based upon an evolutionary strategy, incremental learning and the Fuzzy ARTMAP to realise a protein classification system for the GPCR protein super-family  ... 
doi:10.23919/saiee.2007.9488130 fatcat:kcn7lxjblbhwbjga4mi7mmmuxy

Variable selection from a feature representing protein sequences: a case of classification on bacterial type IV secreted effectors

Jian Zhang, Lixin Lv, Donglei Lu, Denan Kong, Mohammed Abdoh Ali Al-Alashaari, Xudong Zhao
2020 BMC Bioinformatics  
Encoding approaches of protein sequences for feature extraction play an important role in protein classification.  ...  Background Classification of certain proteins with specific functions is momentous for biological research.  ...  Background Feature extraction from protein sequences plays an important role in protein classification [1] [2] [3] [4] of many areas, such as identification of plant pentatricopeptide repeat coding protein  ... 
doi:10.1186/s12859-020-03826-6 pmid:33109082 fatcat:x6i3fqeiyrfodjyydj7vbjmcmi

Prediction of Enzyme Mutant Activity Using Computational Mutagenesis and Incremental Transduction

Nada Basit, Harry Wechsler
2011 Advances in Bioinformatics  
Incremental learning is in tune with both eScience and actual experimentation, as it accounts for cumulative annotation effects of enzyme mutant activity over time.  ...  This paper expands on standard one-shot learning by proposing an incremental transductive method (T2bRF) for the prediction of enzyme mutant activity during mutagenesis using Delaunay tessellation and  ...  Majid Masso and Iosif Vaisman for proposing the problem and representation, and for providing the datasets.  ... 
doi:10.1155/2011/958129 pmid:22007208 pmcid:PMC3189455 fatcat:dredqf3dq5dghmzfcjrtu2wzea

A SVM for GPCR Protein Prediction Using Pattern Discovery

Francisco Nascimento Junior, Ing Ren Tsang, George D.C. Cavalcanti
2008 2008 Eighth International Conference on Hybrid Intelligent Systems  
Similarly, pattern discovery algorithms have also been used to uncover hidden motifs in protein sequences, contributing greatly to the understanding of the problem of protein classification.  ...  G-protein coupled receptors (GPCRs) represent one of the largest protein families in Human Genome. Most of these receptors are major target for drug discovery and development.  ...  Support Vector Machine Support vector machines (SVMs) are a set of related supervised learning methods used for classification and regression.  ... 
doi:10.1109/his.2008.51 dblp:conf/his/JuniorTC08 fatcat:3hefg2v4tzfu5njfxjnog5jdoe

Improved Classification of Lung Cancer Tumors Based on Structural and Physicochemical Properties of Proteins Using Data Mining Models

R. Geetha Ramani, Shomona Gracia Jacob, Vladimir N. Uversky
2013 PLoS ONE  
Precise categorization of oncogenic genes causing SCLC and NSCLC based on the structural and physicochemical properties of their protein sequences is expected to unravel the functionality of proteins that  ...  This research work was focused on designing a computational strategy to predict the class of lung cancer tumors from the structural and physicochemical properties (1497 attributes) of protein sequences  ...  Acknowledgments The authors wish to thank the Academic Editor and the kind Reviewers for their candid and constructive comments, which was very effective in strengthening the presentation of this research  ... 
doi:10.1371/journal.pone.0058772 pmid:23505559 pmcid:PMC3591381 fatcat:bgfobk5xivfuncxe23khw27psm

Graph pyramids for protein function prediction

Tushar Sandhan, Youngjun Yoo, Jin Young Choi, Sun Kim
2015 BMC Medical Genomics  
With each correctly classified test sequence, the fast incremental learning ability of the proposed method further enhances the training model.  ...  Thus pattern recognition from nucleic acid sequences is an important affair for protein function prediction.  ...  Acknowledgements Publication of this article has been funded by Next-Generation Information Computing Development Program through the National Research  ... 
doi:10.1186/1755-8794-8-s2-s12 pmid:26044522 pmcid:PMC4460595 fatcat:qdhmmaf7s5bubcsczljvxn77ny

Keeping up with the genomes: efficient learning of our increasing knowledge of the tree of life [article]

Zhengqiao Zhao, Alexandru Cristian, Gail L Rosen
2019 bioRxiv   pre-print
The rich literature of "incremental learning" addresses the need to update an existing classifier to accommodate new data without sacrificing much accuracy compared to retraining the classifier with all  ...  The proof-of-concept naïve Bayes implementation, when updated yearly, now runs in ¼ of the non-incremental time with no accuracy loss.  ...  These results demonstrate the necessity of incremental learning for metagenomic taxonomic classification.  ... 
doi:10.1101/758755 fatcat:mmqtwoohwzbchfm3ksikb3fpai

Streaming chunk incremental learning for class-wise data stream classification with fast learning speed and low structural complexity

Prem Junsawang, Suphakant Phimoltares, Chidchanok Lursinsap, Paweł Pławiak
2019 PLoS ONE  
This paper proposed a method for further enhancing the capability of discard-after-learn concept for streaming data-chunk environment in terms of low computational time and neural space complexities.  ...  The newly proposed method, named streaming chunk incremental learning (SCIL), increases the plasticity and the adaptabilty of the network structure according to the distribution of incoming data and their  ...  One promising solution for continuous streaming data classification is of incremental learning methods.  ... 
doi:10.1371/journal.pone.0220624 pmid:31498787 pmcid:PMC6733468 fatcat:ohohbz7twjbb3bseuxhfr5lese

Building a Knowledge-Base for Protein Function Prediction using Multistrategy Learning

Takashi Ishikawa, Shigeki Mitaku, Takao Terano, Takatsugu Hirokawa, Makiko Suwa, Boon-Chien Seah
1995 Genome Informatics Series  
Conventional techniques for protein function prediction using similarities of amino acid sequences enable us to only classify the protein functions into function groups.  ...  By "functional feature", we mean a feature of an amino acid sequence characterizing the function of a protein with the amino acid sequence.  ...  Let the generated rules be classification rules for the given class. Applying the above procedure of Inductive inference for all classes to learn, we obtain a set of classification rules.  ... 
doi:10.11234/gi1990.6.39 fatcat:q4lrabmdjbeibggvmruuz3h26u

Keeping up with the genomes: efficient learning of our increasing knowledge of the tree of life

Zhengqiao Zhao, Alexandru Cristian, Gail Rosen
2020 BMC Bioinformatics  
It is a computational challenge for current metagenomic classifiers to keep up with the pace of training data generated from genome sequencing projects, such as the exponentially-growing NCBI RefSeq bacterial  ...  The rich literature of "incremental learning" addresses the need to update an existing classifier to accommodate new data without sacrificing much accuracy compared to retraining the classifier with all  ...  These results demonstrate the necessity of incremental learning for metagenomic taxonomic classification.  ... 
doi:10.1186/s12859-020-03744-7 pmid:32957925 pmcid:PMC7507296 fatcat:m57e2wezzvb75jcj3jypgfnm54

Protein Sequence Classification with Improved Extreme Learning Machine Algorithms

Jiuwen Cao, Lianglin Xiong
2014 BioMed Research International  
The optimal pruned ELM is first employed for protein sequence classification in this paper.  ...  Therefore, it is urgent and necessary to build an efficient protein sequence classification system. In this paper, we study the performance of protein sequence classification using SLFNs.  ...  Conflict of Interests The authors declare that there is no conflict of interests regarding the publication of this paper. Acknowledgments  ... 
doi:10.1155/2014/103054 pmid:24795876 pmcid:PMC3985160 fatcat:i6h66m7cvvda3nq2niccr4c4xm

Integrative data mining: the new direction in bioinformatics

P. Bertone, M. Gerstein
2001 IEEE Engineering in Medicine and Biology Magazine  
Supervised learning techniques appear to be ideal for this type of functional classification of microarray targets, where sets of positive and negative examples can be compiled from genomic sequence annotations  ...  The current landscape of biological databases includes large public archives, such as GenBank, DDBJ, and EMBL for nucleic acid sequences [1] ; PIR and SWISS-PROT for protein sequences [2] ; and the Protein  ...  Since then he has received a number of young investigator awards (e.g., from the Navy and Keck foundations) and has published appreciably in biological science journals (80 in total).  ... 
doi:10.1109/51.940042 pmid:11494767 fatcat:kh4u7xxslve4ddmnkwurjmayp4

Identifying Membrane Protein Types Based on Lifelong Learning With Dynamically Scalable Networks

Weizhong Lu, Jiawei Shen, Yu Zhang, Hongjie Wu, Yuqing Qian, Xiaoyi Chen, Qiming Fu
2022 Frontiers in Genetics  
The model extends the application area of lifelong learning and provides new ideas for multiple classification problems in bioinformatics.  ...  The current methods for predicting proteins are usually based on machine learning, but further improvements in prediction effectiveness and accuracy are needed.  ...  of protein classification.  ... 
doi:10.3389/fgene.2021.834488 pmid:35371189 pmcid:PMC8964460 fatcat:26a5wnqsjza4fh4gceygigvgza

ROSEFW-RF: The winner algorithm for the ECBDL'14 big data competition: An extremely imbalanced big data bioinformatics problem

Isaac Triguero, Sara del Río, Victoria López, Jaume Bacardit, José M. Benítez, Francisco Herrera
2015 Knowledge-Based Systems  
Learning under these circumstances, known as imbalanced big data classification, may not be straightforward for most of the standard machine learning methods.  ...  The rapid advances in biotechnology are allowing us to obtain and store large quantities of data about cells, proteins, genes, etc, that should be processed.  ...  Protein Structure Prediction and Contact Map Proteins are crucial molecules for the function of all aspects of life. Proteins are constructed as a sequence of amino acids.  ... 
doi:10.1016/j.knosys.2015.05.027 fatcat:iwuatu7hrbcrbcfgaczzz52fiy
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