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Machine learning in bioinformatics

Pedro Larrañaga, Borja Calvo, Roberto Santana, Concha Bielza, Josu Galdiano, Iñaki Inza, José A. Lozano, Rubén Armañanzas, Guzmán Santafé, Aritz Pérez, Victor Robles
2006 Briefings in Bioinformatics  
This article reviews machine learning methods for bioinformatics.  ...  Supervised classification, clustering and probabilistic graphical models for bioinformatics are reviewed.  ...  Special issues in journals [28] [29] [30] have also been published covering machine learning topics in bioinformatics.  ... 
doi:10.1093/bib/bbk007 pmid:16761367 fatcat:4oss26occvhkjnetcr3sesnkcu

Need of Machine Learning in Bioinformatics

2019 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
creating model for them, and also some of the applications of machine learning in bioinformatics explained briefly  ...  In order to satisfy the requirements of various processes in the biological data machine learning can provide different kinds of learning algorithms and this helps the computer can automatically learn  ...  Bioinformatics Applications Machine learning algorithms can address various problems in bioinformatics.  ... 
doi:10.35940/ijitee.k1903.0981119 fatcat:7sbeaemvxfe5tppsuab26lmv7q

Machine Learning: An Indispensable Tool in Bioinformatics [chapter]

Iñaki Inza, Borja Calvo, Rubén Armañanzas, Endika Bengoetxea, Pedro Larrañaga, José A. Lozano
2009 Msphere  
The use of machine learning techniques has been extended to a wide spectrum of bioinformatics applications.  ...  In order to fulfill these requirements, the machine learning discipline has become an everyday tool in bio-laboratories.  ...  Groups 2007-2012 (IT-242-07) programs (Basque Government), the TIN2005-03824 and Consolider Ingenio 2010 -CSD2007-00018 projects (Spanish Ministry of Education and Science), and the COMBIOMED network in  ... 
doi:10.1007/978-1-60327-194-3_2 pmid:19957143 fatcat:gu4mkicphnezxho7inqda62f2m

Probabilistic models and machine learning in structural bioinformatics

Thomas Hamelryck
2009 Statistical Methods in Medical Research  
Recently, probabilistic models and machine learning methods based on Bayesian principles are providing efficient and rigorous solutions to challenging problems that were long regarded as intractable.  ...  Classic problems in structural bioinformatics include the prediction of protein and RNA structure from sequence, the design of artificial proteins or enzymes, and the automated analysis and comparison  ...  Conclusions From the developments highlighted above, it should be clear that probabilistic models and machine learning methods based on Bayesian principles are leading to substantial progress, and more  ... 
doi:10.1177/0962280208099492 pmid:19153168 fatcat:odf5rfjtw5cp5mmzv6l7qxethu

Artificial Intelligence, Machine Learning and Deep Learning in Ion Channel Bioinformatics

Md. Ashrafuzzaman
2021 Membranes  
In these focused research areas, the use of artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms and associated models have been found very popular.  ...  This review aims at explaining the ways we may use the bioinformatics techniques and thus draw a few lines across the avenue to let the ion channel features appear clearer.  ...  Acknowledgments: The grant is acknowledged in the above mentioned funding section. Conflicts of Interest: The author declares no conflict of interest.  ... 
doi:10.3390/membranes11090672 pmid:34564489 fatcat:coprc5dm7bgfdmhudd55tmr7zm

Big Data Analytics in Bioinformatics: A Machine Learning Perspective [article]

Hirak Kashyap, Hasin Afzal Ahmed, Nazrul Hoque, Swarup Roy, Dhruba Kumar Bhattacharyya
2015 arXiv   pre-print
Bioinformatics research is characterized by voluminous and incremental datasets and complex data analytics methods. The machine learning methods used in bioinformatics are iterative and parallel.  ...  In the recent years, parallel, incremental, and multi-view machine learning algorithms have been proposed.  ...  ACKNOWLEDGMENTS The authors would like to thank the Ministry of HRD, Govt. of India for funding as a Centre of Excellence with thrust area in Machine Learning Research and Big Data Analytics for the period  ... 
arXiv:1506.05101v1 fatcat:oix7d5hecbfgthzhepznwyi6fm

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.  ...  Fig. 1 : 1 Human Learning Vs. Machine learning III. Artificial Intelligence vs. Machine Learning vs. Deep Learning Fig. 2 : 2 AI vs. ML vs.  ... 
doi:10.26782/jmcms.spl.7/2020.02.00006 fatcat:u5hkuxgamrfw3heyd5xznjnoru

Applications of Support Vector Machines in Bioinformatics and Network Security [chapter]

Rehan Akbani, Turgay Korkmaz
2010 Application of Machine Learning  
ROC curves are commonly used in Machine Learning to evaluate classifiers, irrespective of the thresholds used.  ...  In contrast to developing a separate module for each attack pattern, we can employ Machine Learning, specifically Support Vector Machines (SVM), to build a flexible and dynamic RS that can be trained to  ...  /books/application-of-machine-learning/applications-of-supportvector-machines-in-bioinformatics-and-network-security  ... 
doi:10.5772/8618 fatcat:musa6hbjpfdf7k6bly2ovr6ewq

Weighted quality estimates in machine learning

L. Budagyan, R. Abagyan
2006 Bioinformatics  
Motivation: Machine learning methods such as neural networks, support vector machines, and other classification and regression methods rely on iterative optimization of the model quality in the space of  ...  We demonstrated that our new weighted measures estimate the model generalization better and are consistent with the machine learning theory.  ...  METHODS Machine learning framework. Generalization error and its estimates. In most cases the popular machine learning techniques fall under a model of deterministic learning.  ... 
doi:10.1093/bioinformatics/btl458 pmid:16935929 fatcat:2rycyl2im5gibf46xobuz3xwvu

Machine learning in bioinformatics: A brief survey and recommendations for practitioners

Harish Bhaskar, David C. Hoyle, Sameer Singh
2006 Computers in Biology and Medicine  
Machine learning is used in a large number of bioinformatics applications and studies.  ...  The application of machine learning techniques in other areas such as pattern recognition has resulted in accumulated experience as to correct and principled approaches for their use.  ...  Machine learning in bioinformatics Machine learning techniques have found widespread application in bioinformatics [1] .  ... 
doi:10.1016/j.compbiomed.2005.09.002 pmid:16226240 fatcat:yx7tvgzlvng3rfx5tbi6cj5loa

Performance measures in evaluating machine learning based bioinformatics predictors for classifications

Yasen Jiao, Pufeng Du
2016 Quantitative Biology  
A number of existing predictors in bioinformatics are classifiers, which is a type of learning machines.  ...  In the next part of this review, we will discuss the commonly used protocols in evaluating machine learning based predictors in bioinformatics.  ... 
doi:10.1007/s40484-016-0081-2 fatcat:vmcgqqwibjbqnil5ipqclct5sy

The Role of Feature Engineering in a Machine-Learning World

Richard Boire
2016 MOJ Proteomics & Bioinformatics  
Machine learning vs. deep learning In our increasing world of Big Data, do we simply trust that the machine is doing the right thing?  ...  But how does deep learning differ from machine learning.  ... 
doi:10.15406/mojpb.2016.04.00117 fatcat:ijqsuvfi55gmbimk3cwf4tzwgq

A Theory of Intrinsic Bias in Biology and its Application in Machine Learning and Bioinformatics [article]

Juan G. Diaz Ochoa
2019 bioRxiv   pre-print
And seldom a less systemic and more cognitive approach is accepted, according to which organisms' sense and try to predict their trajectories in their environment, which is an intrinsic bias in the sampled  ...  AbstractIt is common to consider that a data-intensive strategy is a bias-free way to develop systemic approaches in biology and physiology.  ...  machine learning and artificial intelligence.  ... 
doi:10.1101/595785 fatcat:zufprmma7bekdkeneq4h4vveum

Semantic similarity and machine learning with ontologies

Maxat Kulmanov, Fatima Zohra Smaili, Xin Gao, Robert Hoehndorf
2020 Briefings in Bioinformatics  
Recently, ontologies are increasingly being used to provide background knowledge in similarity-based analysis and machine learning models.  ...  embeddings can exploit the background knowledge in ontologies and how ontologies can provide constraints that improve machine learning models.  ...  an important method for utilizing ontologies in machine learning.  ... 
doi:10.1093/bib/bbaa199 pmid:33049044 pmcid:PMC8293838 fatcat:3mqrjqnggrhdrkvsl6w4odazeu

Corrigendum: Machine Learning for Causal Inference in Biological Networks: Perspectives of This Challenge

Paola Lecca
2022 Frontiers in Bioinformatics  
In the original article, there were some errors. 1) Mistake that was made: verb in the infinitive instead of in the gerund mode, specifically "represent" instead of "representing".  ...  The authors apologize for these errors and state that this does not change the scientific conclusions of the article in any way.  ...  By employing meta-modelling upstream to meta-learning, and meta-learning it-self in place of a direct application of machine-learning, this pipeline extends a typical machine learning approach that generally  ... 
doi:10.3389/fbinf.2022.888273 fatcat:gygvzql4g5h4xhesqhfjbrpo6e
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