Pedro Larrañaga, Borja Calvo, Roberto Santana et al. · Briefings in Bioinformatics · 2006
This article reviews machine learning methods for bioinformatics. It presents modelling methods, such as supervised classification, clustering and probabilistic graphical models for knowledge discovery, as well as deterministic and stochastic heuristics for optimization. Applications in genomics, proteomics, systems biology, evolution and text mining are also shown.
Cited by 858Open access
Huiru Zheng, Jyotsna Talreja Wassan, Haiying Wang · Elsevier eBooks · 2024
Cited by 0
Prof. Dr. Dileep Kumar M., Prof. Dr. Sohit Agarwali, S. R. Jena · Zenodo (CERN European Organization for Nuclear Research) · 2024
Cited by 0Open access
Indrajeet Kumar, Surya Pratap Singh, Shivam Shivam · Bioinformatics · 2021
Cited by 16
Prof. Dr. Dileep Kumar M., Prof. Dr. Sohit Agarwali, S. R. Jena · Zenodo (CERN European Organization for Nuclear Research) · 2024
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Encyclopedia of Database Systems · 2009
Cited by 13
Colin Campbell · 2013
Cited by 16
Young‐Rae Cho, Mingon Kang · Methods · 2020
Cited by 53
2014
Abstract. Bioinformatics is a field of study dealing with methods for storing, retrieving and analyzing gene and protein oriented biological data. High-throughput technologies like DNA sequencing or microarrays allow researchers to obtain large volumes of heterogeneous and mutually interacting data. Analysis and understanding of these data provides a natural application field for machine learning algorithms. At the same time, bioinformatics is a scientific branch of such analytical complexity, data variety and abundance that it motivates further development of specialized learning algorithms such as co-clustering or multiple sequence alignment. This paper provides a brief overview of the topics and works discussed during my talk on machine learning applications in bioinformatics. The talk starts with a preview of fundamental bioinformatics analytical tasks solved by machine learning algorithms mentioning a few success stories. The second part summarizes the recent bioinformatics research carried out in my home research
Cited by 0
Motasem Younis · Zenodo (CERN European Organization for Nuclear Research) · 2026
Highlights Expanded project and data provenance documentation for first-time users. Updated citation and archival metadata affiliation to Mendel University in Brno. Added comprehensive Zenodo software description metadata. Bumped citation metadata version/date for this release. Notes This is a documentation and metadata release; no model logic changes were introduced.
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Younis, Motasem · Zenodo (CERN European Organization for Nuclear Research) · 2026
Highlights Expanded project and data provenance documentation for first-time users. Updated citation and archival metadata affiliation to Mendel University in Brno. Added comprehensive Zenodo software description metadata. Bumped citation metadata version/date for this release. Notes This is a documentation and metadata release; no model logic changes were introduced.
Cited by 0Open access
Li, Yifeng · Scholarship at UWindsor (University of Windsor) · 2014
The meaning of parsimony is twofold in machine learning: either the structure or (and) the parameter of a model can be sparse. Sparse models have many strengths. First, sparsity is an important regularization principle to reduce model complexity and therefore avoid overfitting. Second, in many fields, for example bioinformatics, many high-dimensional data may be generated by a very few number of hidden factors, thus it is more reasonable to use a proper sparse model than a dense model. Third, a sparse model is often easy to interpret. In this dissertation, we investigate the sparse machine learning models and their applications in high-dimensional biological data analysis. We focus our research on five types of sparse models as follows. First, sparse representation is a parsimonious principle that a sample can be approximated by a sparse linear combination of basis vectors. We explore existing sparse representation models and propose our own sparse representation methods for high dimensional biological data analysis. We derive different sparse representation models from a Bayesian perspective. Two generic dictionary learning frameworks are proposed. Also, kernel and supervised dictionary learning approaches are devised. Furthermore, we propose fast active-set and decomposition methods for the optimization of sparse coding models. Second, gene-sample-time data are promising in clinical study, but challenging in computation. We propose sparse tensor decomposition methods and kernel methods for the dimensionality reduction and classification of such data. As the extensions of matrix factorization, tensor decomposition techniques can reduce the dimensionality of the gene-sample-time data dramatically, and the kernel methods can run very efficiently on such data. Third, we explore two sparse regularized linear models for multi-class problems in bioinformatics. Our first method is called the nearest-border classification technique for data with many classes. Our second method is a hierarchical model. It can simultaneously select features and classify samples. Our experiment, on breast tumor subtyping, shows that this model outperforms the one-versus-all strategy in some cases. Fourth, we propose to use spectral clustering approaches for clustering microarray time-series data. The approaches are based on two transformations that have been recently introduced, especially for gene expression time-series data, namely, alignment-based and variation-based transformations. Both transformations have been devised in order to take into account temporal relationships in the data, and have been shown to increase the ability of a clustering method in detecting co-expressed genes. We investigate the performances of these transformations methods, when combined with spectral clustering on two microarray time-series datasets, and discuss their strengths and weaknesses. Our experiments on two well known real-life datasets show the superiority of the alignment-based over the variation-based transformation for finding meaningful groups of co-expressed genes. Fifth, we propose the max-min high-order dynamic Bayesian network (MMHO-DBN) learning algorithm, in order to reconstruct time-delayed gene regulatory networks. Due to the small sample size of the training data and the power-low nature of gene regulatory networks, the structure of the network is restricted by sparsity. We also apply the qualitative probabilistic networks (QPNs) to interpret the interactions learned. Our experiments on both synthetic and real gene expression time-series data show that, MMHO-DBN can obtain better precision than some existing methods, and perform very fast. The QPN analysis can accurately predict types of influences and synergies. Additionally, since many high dimensional biological data are subject to missing values, we survey various strategies for learning models from incomplete data. We extend the existing imputation methods, originally for two-way data, to methods for gene-sample-time data. We also propose a pair-wise weighting method for computing kernel matrices from incomplete data. Computational evaluations show that both approaches work very robustly.
Cited by 6Open access
Alioune Ngom, Luis Rueda, Yifeng Li · 2014
The meaning of parsimony is twofold in machine learning: either the structure or (and) the parameter of a model can be sparse. Sparse models have many strengths. First, sparsity is an important regularization principle to reduce model complexity and therefore avoid overfitting. Second, in many fields, for example bioinformatics, many high-dimensional data may be generated by a very few number of hidden factors, thus it is more reasonable to use a proper sparse model than a dense model. Third, a sparse model is often easy to interpret. In this dissertation, we investigate the sparse machine learning models and their applications in high-dimensional biological data analysis. We focus our research on five types of sparse models as follows.
First, sparse representation is a parsimonious principle that a sample can be approximated by a sparse linear combination of basis vectors. We explore existing sparse representation models and propose our own sparse representation methods for high dimensional biological data analysis. We derive different sparse representation models from a Bayesian perspective. Two generic dictionary learning frameworks are proposed. Also, kernel and supervised dictionary learning approaches are devised. Furthermore, we propose fast active-set and decomposition methods for the optimization of sparse coding models.
Second, gene-sample-time data are promising in clinical study, but challenging in computation. We propose sparse tensor decomposition methods and kernel methods for the dimensionality reduction and classification of such data. As the extensions of matrix factorization, tensor decomposition techniques can reduce the dimensionality of the gene-sample-time data dramatically, and the kernel methods can run very efficiently on such data.
Third, we explore two sparse regularized linear models for multi-class problems in bioinformatics. Our first method is called the nearest-border classification technique for data with many classes. Our second method is a hierarchical model. It can simultaneously select features and classify samples. Our experiment, on breast tumor subtyping, shows that this model outperforms the one-versus-all strategy in some cases.
Fourth, we propose to use spectral clustering approaches for clustering microarray time-series data. The approaches are based on two transformations that have been recently introduced, especially for gene expression time-series data, namely, alignment-based and variation-based transformations. Both transformations have been devised in order to take into account temporal relationships in the data, and have been shown to increase the ability of a clustering method in detecting co-expressed genes. We investigate the performances of these transformations methods, when combined with spectral clustering on two microarray time-series datasets, and discuss their strengths and weaknesses. Our experiments on two well known real-life datasets show the superiority of the alignment-based over the variation-based transformation for finding meaningful groups of co-expressed genes.
Fifth, we propose the max-min high-order dynamic Bayesian network (MMHO-DBN) learning algorithm, in order to reconstruct time-delayed gene regulatory networks. Due to the small sample size of the training data and the power-low nature of gene regulatory networks, the structure of the network is restricted by sparsity. We also apply the qualitative probabilistic networks (QPNs) to interpret the interactions learned. Our experiments on both synthetic and real gene expression time-series data show that, MMHO-DBN can obtain better precision than some existing methods, and perform very fast. The QPN analysis can accurately predict types of influences and synergies.
Additionally, since many high dimensional biological data are subject to missing values, we survey various strategies for learning models from incomplete data. We extend the existing imputation methods, originally for two-way data, to methods for gene-sample-time data. We also propose a pair-wise weighting method for computing kernel matrices from incomplete data. Computational evaluations show that both approaches work very robustly.
Cited by 14
K. Jayanthi, D. Mahesh · International Journal of Innovative Technology and Exploring Engineering · 2019
The complexity of biological data has emerged the need of powerful and modern data analysis with computer tools and techniques. 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 by experience and construct a model for future output cases. This paper have discussed about the various biological data need to be processed with bioinformatics and techniques which are used in machine learning for achieving the analysis of biological data for creating model for them, and also some of the applications of machine learning in bioinformatics explained briefly.
Cited by 4Open access
Md. Siraj-Ud- Doulah · Bioinformatics & Proteomics Open Access Journal · 2019
Bioinformatics is a developing domain that discourses the necessity to achieve and understand the data that in the past era was enormously produced by genomic investigation. This domain signifies the convergence of genomics, biotechnology and involves examination and understanding of data, displaying of biological occurrences, which is one of the most significant field for the study of biological schemes today and expansion of algorithms and statistics. The paper objectives at a relative study of Machine learning algorithms on a breast cancer dataset. The algorithms used for comparison of ANN, ANFIS, ADT, BN, BFT, C4.5, CART, CBR, DT, DESVM, EP, GP, GA, GRU-SVM, IBk, J48, kNN, K*, KMSVM, kNN+ICA, kNNGA, LB, LMT, LR, MDR, MLP, NNge, NB+kNN, NNANF, NB, PART, RBFNN, RF, SVM, SMO, SL, SOM, SR, SMO-SVM, ZeroR– are supervised learning algorithms used extensively for classification purposes and are chosen for their variety. The performance of those algorithms are compared by using the different performance criterions based on confusion matrix such as Recall /Sensitivity, Specificity, Precision, Accuracy, F-Score and Mathews Correlation Coefficient. Based on analysis of this data, Artificial Neural Networks (ANN) is better at classification with 99.5% accuracy and 0.9901 Mathews correlation coefficient among others classifiers.
Cited by 5Open access
Shabroz Alam, Juveriya Israr, Ajay Kumar · Advances in Bioinformatics · 2024
Cited by 10
Inza Cano, Iñaki, Calvo Molinos, Borja, Armañanzas Arnedillo, Ruben et al. · UPM Digital Archive (Technical University of Madrid) · 2010
The increase in the number and complexity of biological databases has raised the need for modern and powerful data analysis tools and techniques. In order to fulfill these requirements, the machine learning discipline has become an everyday tool in bio-laboratories. The use of machine learning techniques has been extended to a wide spectrum of bioinformatics applications. It is broadly used to investigate the underlying mechanisms and interactions between biological molecules in many diseases, and it is an essential tool in any biomarker discovery process. In this chapter, we provide a basic taxonomy of machine learning algorithms, and the characteristics of main data preprocessing, supervised classification, and clustering techniques are shown. Feature selection, classifier evaluation, and two supervised classification topics that have a deep impact on current bioinformatics are presented. We make the interested reader aware of a set of popular web resources, open source software tools, and benchmarking data repositories that are frequently used by the machine learning community.
Cited by 0Open access
Iñaki Inza, Borja Calvo, Rubén Armañanzas et al. · Methods in molecular biology · 2009
The increase in the number and complexity of biological databases has raised the need for modern and powerful data analysis tools and techniques. In order to fulfill these requirements, the machine learning discipline has become an everyday tool in bio-laboratories. The use of machine learning techniques has been extended to a wide spectrum of bioinformatics applications. It is broadly used to investigate the underlying mechanisms and interactions between biological molecules in many diseases, and it is an essential tool in any biomarker discovery process. In this chapter, we provide a basic taxonomy of machine learning algorithms, and the characteristics of main data preprocessing, supervised classification, and clustering techniques are shown. Feature selection, classifier evaluation, and two supervised classification topics that have a deep impact on current bioinformatics are presented. We make the interested reader aware of a set of popular web resources, open source software tools, and benchmarking data repositories that are frequently used by the machine learning community.
Cited by 99
Levon Budagyan A, Ruben Abagyan B, Martin Bishop · 2008
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 the parameters of the method. Model quality measures (accuracies, correlations, etc.) are frequently overly optimistic because the training sets are dominated by particular families and subfamilies. To overcome the bias, the data set is usually reduced by filtering out closely related objects. However, such filtering uses fixed similarity thresholds and ignores a part of the training information. Results: We suggested a novel approach to calculate prediction model quality based on assigning to each data point inverse density weights derived from the postulated distance metric. We demonstrated that our new weighted measures estimate the model generalization better and are consistent with the machine learning theory. The Vapnik-Chervonenkis theorem was reformulated and applied to derive the space-uniform error estimates. Two examples were used to illustrate the advantages of the inverse density weighting. First, we demonstrated on a set with a built-in bias that the unweighted cross-validation procedure leads to an overly optimistic quality estimate, while the density-weighted quality estimates are more realistic. Second, an analytical equation for weighted quality estimates was used to derive an SVM model for signal peptide prediction using a full set of known signal peptides, instead of the usual filtered subset. Contact:
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Diego Mariano, Lucas Moraes dos Santos, Raquel C. de Melo-Minardi · Advances in bioinformatics and biomedical engineering book series · 2024
In recent years, machine learning has revolutionized the world. Indeed, bioinformatics has benefited from machine learning techniques to make new scientific discoveries, such as producing new biotechnological products, discovering new drugs, and understanding the mechanism of action of diseases, among others. In this chapter, you will learn about machine learning methodologies, the types of machine learning, and the traditional algorithms used to build models. You will also learn about some strategies used for the computational modeling of biological problems with a special focus on the representation of structures in the data pre-processing stage.
Cited by 1