A Survey of Data Mining and Deep Learning in Bioinformatics

Abstract

The fields of medicine science and health informatics have made great progress recently and have led to in-depth analytics that is demanded by generation, collection and accumulation of massive data. Meanwhile, we are entering a new period where novel technologies are starting to analyze and explore knowledge from tremendous amount of data, bringing limitless potential for information growth. One fact that cannot be ignored is that the techniques of machine learning and deep learning applications play a more significant role in the success of bioinformatics exploration from biological data point of view, and a linkage is emphasized and established to bridge these two data analytics techniques and bioinformatics in both industry and academia. This survey concentrates on the review of recent researches using data mining and deep learning approaches for analyzing the specific domain knowledge of bioinformatics. The authors give a brief but pithy summarization of numerous data mining algorithms used for preprocessing, classification and clustering as well as various optimized neural network architectures in deep learning methods, and their advantages and disadvantages in the practical applications are also discussed and compared in terms of their industrial usage. It is believed that in this review paper, valuable insights are provided for those who are dedicated to start using data analytics methods in bioinformatics.

A method for multiparametric statistical quantification of the heterogeneity of free Na+ concentration by 19F MR spectroscopy: Proof of principle in silico and in vitro

“…In the past few years “machine learning” procedures have become increasingly popular in the analysis of results from biomedical investigations . These techniques involve data mining algorithms used for preprocessing, classification and clustering, but also neural networks for “deep learning” approaches.…”

“…In the past few years "machine learning" procedures have become increasingly popular in the analysis of results from biomedical investigations. 29,30 These techniques involve data mining algorithms used for preprocessing, classification and clustering, but also neural networks for "deep learning" approaches. In this context, the multiple statistical [Na + ] descriptors generated by our new approach may be integrated with further metabolic, biochemical, biological and imaging parameters for advanced characterization of tissue in vivo by using neural network and other machine learning algorithms to facilitate the discovery of complex patterns relevant to healthy and diseased tissues.…”

A Novel method for IDC Prediction in Breast Cancer Histopathology images using Deep Residual Neural Networks

“…Several deep learning paradigms and data mining techniques are described by K. Lan et al [11] along with data preprocessing and transformation which is also an important part of bioinformatics. Image processing & analysis along with machine learning methods have also been applied to classification of histopathology images.…”

Graph-based description of tertiary lymphoid organs at single-cell level

“…In order to provide reproducible, observer-independent, and quantitative evaluation of immune infiltrates, image analysis approaches could automatically detect regions of interest (ROIs) [20][21][22] and classify cells based on immunohistochemistry [23]. The increasing medical need for evaluation of particular cell types in anatomically or immunologically defined ROIs falls into an era of massive advance in machine learning (ML), with deep learning and pixel-based ML (as opposed to feature-based approaches to ML) holding great promise for identification, classification, and quantitative assessment of relevant patterns in medical images including microscopy [24][25][26][27][28]. Evolutionary algorithms have successfully been developed, and applied to different tasks including hyperspectral remote sensing image analysis, classification in different common benchmark data sets, and brain tumor medical imaging [29][30][31][32].…”