Machine Learning In Bioinformatics Research Articles

Applications of Data Mining in the Bioinformatics Field: A Review

The next generation sequencing (NGS) technology generates a large amount of genomic data. Bioinformatics has made it a priority to deal with genomic data in all of its forms. The main focus of this article is on the use of data mining in bioinformatics. The processing flow of bioinformatics data is depicted using a summary of the definition and relevant research contents of bioinformatics. Then emphatically introduces data mining from the perspective of data preprocessing, dimension reduction and statistical machine learning in bioinformatics. The use of data mining in the field of bioinformatics is discussed. It also discusses some of the current obstacles and prospects in bioinformatics data mining.

SMILE: systems metabolomics using interpretable learning and evolution

BackgroundDirect link between metabolism and cell and organism phenotype in health and disease makes metabolomics, a high throughput study of small molecular metabolites, an essential methodology for understanding and diagnosing disease development and progression. Machine learning methods have seen increasing adoptions in metabolomics thanks to their powerful prediction abilities. However, the “black-box” nature of many machine learning models remains a major challenge for wide acceptance and utility as it makes the interpretation of decision process difficult. This challenge is particularly predominant in biomedical research where understanding of the underlying decision making mechanism is essential for insuring safety and gaining new knowledge.ResultsIn this article, we proposed a novel computational framework, Systems Metabolomics using Interpretable Learning and Evolution (SMILE), for supervised metabolomics data analysis. Our methodology uses an evolutionary algorithm to learn interpretable predictive models and to identify the most influential metabolites and their interactions in association with disease. Moreover, we have developed a web application with a graphical user interface that can be used for easy analysis, interpretation and visualization of the results. Performance of the method and utilization of the web interface is shown using metabolomics data for Alzheimer’s disease.ConclusionsSMILE was able to identify several influential metabolites on AD and to provide interpretable predictive models that can be further used for a better understanding of the metabolic background of AD. SMILE addresses the emerging issue of interpretability and explainability in machine learning, and contributes to more transparent and powerful applications of machine learning in bioinformatics.

Literature on Applied Machine Learning in Metagenomic Classification: A Scoping Review.

Simple SummaryTechnological advancements have led to modern DNA sequencing methods, capable of generating large amounts of data describing the microorganisms that live in samples taken from the environment. Metagenomics, the field that studies the different genomes within these samples, is becoming increasingly popular, as it has many real-world applications, such as the discovery of new antibiotics, personalized medicine, forensics, and many more. From a computer science point of view, it is interesting to see how these large volumes of data can be processed efficiently to accurately identify (classify) the microorganisms from the input DNA data. This scoping review aims to give an insight into the existing state of the art computational methods for processing metagenomic data through the prism of machine learning, data science, and big data. We provide an overview of the state of the art metagenomic classification methods, as well as the challenges researchers face when tackling this complex problem. The end goal of this review is to help researchers be up to date with current trends, as well as identify opportunities for further research and improvements.Applied machine learning in bioinformatics is growing as computer science slowly invades all research spheres. With the arrival of modern next-generation DNA sequencing algorithms, metagenomics is becoming an increasingly interesting research field as it finds countless practical applications exploiting the vast amounts of generated data. This study aims to scope the scientific literature in the field of metagenomic classification in the time interval 2008–2019 and provide an evolutionary timeline of data processing and machine learning in this field. This study follows the scoping review methodology and PRISMA guidelines to identify and process the available literature. Natural Language Processing (NLP) is deployed to ensure efficient and exhaustive search of the literary corpus of three large digital libraries: IEEE, PubMed, and Springer. The search is based on keywords and properties looked up using the digital libraries’ search engines. The scoping review results reveal an increasing number of research papers related to metagenomic classification over the past decade. The research is mainly focused on metagenomic classifiers, identifying scope specific metrics for model evaluation, data set sanitization, and dimensionality reduction. Out of all of these subproblems, data preprocessing is the least researched with considerable potential for improvement.

Discovering the Relationship between Heat-Stress Gene Expression and Gene SNPs Features using Rough Set Theory

Over the years of applying machine learning in bioinformatics, we have learned that scientists, working in many areas of life sciences, call for deeper knowledge of the modeled phenomenon than just the information used to classify the objects with a certain quality. As dynamic molecules of gene activities, transcriptome profiling by RNA sequencing (RNA-seq) is becoming increasingly popular, which not only measures gene expression but also structural variations such as mutations and fusion transcripts. Moreover, Single nucleotide polymorphisms (SNPs) are of great potential in genetics, breeding, ecological and evolutionary studies. Rough sets could be successfully employed to tackle various problems such as gene expression clustering and classification. This study provides general guidelines for accurate SNP discovery from RNA-seq data. Those SNPs annotations are used to find relation between their biological features and the differential expression of the genes to which those SNPs belong. Rough sets are utilized to define this kind of relationship into a finite set of rules. Set of (32) generated rules proved good results with strength, certainty and coverage evaluation terms. This strategy is applied to the analysis of SNPs in A. thaliana plant under heat-stress.

Need of Machine Learning In Bioinformatics

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.

Correct machine learning on protein sequences: a peer-reviewing perspective.

Machine learning methods are becoming increasingly popular to predict protein features from sequences. Machine learning in bioinformatics can be powerful but carries also the risk of introducing unexpected biases, which may lead to an overestimation of the performance. This article espouses a set of guidelines to allow both peer reviewers and authors to avoid common machine learning pitfalls. Understanding biology is necessary to produce useful data sets, which have to be large and diverse. Separating the training and test process is imperative to avoid over-selling method performance, which is also dependent on several hidden parameters. A novel predictor has always to be compared with several existing methods, including simple baseline strategies. Using the presented guidelines will help nonspecialists to appreciate the critical issues in machine learning.

Computational intelligence and machine learning in bioinformatics

Computational intelligence and machine learning in bioinformatics

Machine learning in bioinformatics

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.

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

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. The aim of this paper is to give an account of issues affecting the application of machine learning tools, focusing primarily on general aspects of feature and model parameter selection, rather than any single specific algorithm. These aspects are discussed in the context of published bioinformatics studies in leading journals over the last 5 years. We assess to what degree the experience gained by the pattern recognition research community pervades these bioinformatics studies. We finally discuss various critical issues relating to bioinformatic data sets and make a number of recommendations on the proper use of machine learning techniques for bioinformatics research based upon previously published research on machine learning.