Applications of Machine Learning in Genomics and Systems Biology

Liu, Chunmei; Che, Dongsheng; Liu, Xumin; Song, Yinglei

doi:10.1155/2013/587492

Cited by 15 publications

(10 citation statements)

References 93 publications

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“…Bioinformatics has developed rapidly [25, 26] and many biological prediction tools have been built by machine learning approaches [27–31]. Therefore, we developed a machine learning based tool for predicting the flanking gene expression around the T-DNA insertion site to assist researchers in improving the screening efficiency of activated genes.…”

Section: Introductionmentioning

confidence: 99%

EAT-Rice: A predictive model for flanking gene expression of T-DNA insertion activation-tagged rice mutants by machine learning approaches

Liao

Chen

et al. 2019

PLoS Comput Biol

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T-DNA activation-tagging technology is widely used to study rice gene functions. When T-DNA inserts into genome, the flanking gene expression may be altered using CaMV 35S enhancer, but the affected genes still need to be validated by biological experiment. We have developed the EAT-Rice platform to predict the flanking gene expression of T-DNA insertion site in rice mutants. The three kinds of DNA sequences including UPS1K, DISTANCE, and MIDDLE were retrieved to encode and build a forecast model of two-layer machine learning. In the first-layer models, the features nucleotide context (N-gram), cis-regulatory elements (Motif), nucleotide physicochemical properties (NPC), and CG-island (CGI) were used to build SVM models by analysing the concealed information embedded within the three kinds of sequences. Logistic regression was used to estimate the probability of gene activation which as feature-encoding weighting within first-layer model. In the second-layer models, the NaiveBayesUpdateable algorithm was used to integrate these first layer-models, and the system performance was 88.33% on 5-fold cross-validation, and 79.17% on independent-testing finally. In the three kinds of sequences, the model constructed by Middle had the best contribution to the system for identifying the activated genes. The EAT-Rice system provided better performance and gene expression prediction at further distances when compared to the TRIM database. An online server based on EAT-rice is available at http://predictor.nchu.edu.tw/EAT-Rice .

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Section: Introductionmentioning

confidence: 99%

EAT-Rice: A predictive model for flanking gene expression of T-DNA insertion activation-tagged rice mutants by machine learning approaches

Liao

Chen

et al. 2019

PLoS Comput Biol

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“…Machine learning tools developed over the last several decades have significantly advanced the analysis of the vast amount of next generation sequencing and microarray expression data by discovering the biologically relevant patterns (Tarca et al 2007;Liu et al 2013;Neelima and Babu 2017). Previous studies have utilized unsupervised and supervised machine learning techniques on the microarray gene expression data with variable success rates (Vandesompele et al 2002;Libbrecht and Noble 2015).…”

Section: Introductionmentioning

confidence: 99%

Biological classification with RNA-seq data: Can alternatively spliced transcript expression enhance machine learning classifiers?

Johnson

Dhroso

Hughes

et al. 2018

RNA

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RNA sequencing (RNA-seq) is becoming a prevalent approach to quantify gene expression and is expected to gain better insights into a number of biological and biomedical questions compared to DNA microarrays. Most importantly, RNA-seq allows us to quantify expression at the gene or transcript levels. However, leveraging the RNA-seq data requires development of new data mining and analytics methods. Supervised learning methods are commonly used approaches for biological data analysis that have recently gained attention for their applications to RNA-seq data. Here, we assess the utility of supervised learning methods trained on RNA-seq data for a diverse range of biological classification tasks. We hypothesize that the transcript-level expression data are more informative for biological classification tasks than the gene-level expression data. Our large-scale assessment utilizes multiple data sets, organisms, lab groups, and RNA-seq analysis pipelines. Overall, we performed and assessed 61 biological classification problems that leverage three independent RNA-seq data sets and include over 2000 samples that come from multiple organisms, lab groups, and RNA-seq analyses. These 61 problems include predictions of the tissue type, sex, or age of the sample, healthy or cancerous phenotypes, and pathological tumor stages for the samples from the cancerous tissue. For each problem, the performance of three normalization techniques and six machine learning classifiers was explored. We find that for every single classification problem, the transcript-based classifiers outperform or are comparable with gene expression-based methods. The top-performing techniques reached a near perfect classification accuracy, demonstrating the utility of supervised learning for RNA-seq based data analysis.

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“…These data can be used to train an estimator which in turn is able to predict a health condition based on the used features for uncharacterized subjects. Such classification tasks were successfully used to predict new kinase-substrate relationships 3 and many other applications in biological and medical science 27 , 28 . Instant Clue offers several functions to establish an estimator for prediction, based on the scikit-learn library 16 .…”

Section: Resultsmentioning

confidence: 99%

Instant Clue: A Software Suite for Interactive Data Visualization and Analysis

Nolte

MacVicar

Tellkamp

et al. 2018

Sci Rep

191

138

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The development of modern high-throughput instrumentation and improved core facility infrastructures leads to an accumulation of large amounts of scientific data. However, for a majority of scientists the comprehensive analysis and visualization of their data goes beyond their expertise. To reduce this hurdle, we developed a software suite called Instant Clue that helps scientists to visually analyze data and to gain insights into biological processes from their high-dimensional dataset. Instant Clue combines the power of visual and statistical analytics using a straight forward drag & drop approach making the software highly intuitive. Additionally, it offers a comprehensive portfolio of statistical tools for systematic analysis such as dimensional reduction, (un)-supervised learning, clustering, multi-block (omics) integration and curve fitting. Charts can be combined with high flexibility into a main figure template for direct usage in scientific publications. Even though Instant Clue was developed with the omics-sciences in mind, users can analyze any kind of data from low to high dimensional data sets. The open-source software is available for Windows and Mac OS (http://www.instantclue.uni-koeln.de) and is accompanied by a detailed video tutorial series.

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Applications of Machine Learning in Genomics and Systems Biology

Cited by 15 publications

References 93 publications

EAT-Rice: A predictive model for flanking gene expression of T-DNA insertion activation-tagged rice mutants by machine learning approaches

EAT-Rice: A predictive model for flanking gene expression of T-DNA insertion activation-tagged rice mutants by machine learning approaches

Biological classification with RNA-seq data: Can alternatively spliced transcript expression enhance machine learning classifiers?

Instant Clue: A Software Suite for Interactive Data Visualization and Analysis

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