scholarly journals New Ensemble Machine Learning Method for Classification and Prediction on Gene Expression Data

2008 ◽  
pp. 982-989 ◽  
Author(s):  
Ching Wei Wang
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Gene Expression Data ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Experimental Results ◽  
Machine Learning Algorithm ◽  
Expression Data ◽  
Ensemble Machine Learning

One of the most active areas of research in supervised machine learning has been to study methods for constructing good ensembles of classifiers. The main discovery is that the ensemble classifier often performs much better than single classifiers that make them up. Recent researches (Dettling, 2004, Tan & Gilbert, 2003) have confirmed the utility of ensemble machine learning algorithms for gene expression analysis. The motivation of this work is to investigate a suitable machine learning algorithm for classification and prediction on gene expression data. The research starts with analyzing the behavior and weaknesses of three popular ensemble machine learning methods—Bagging, Boosting, and Arcing—followed by presentation of a new ensemble machine learning algorithm. The proposed method is evaluated with the existing ensemble machine learning algorithms over 12 gene expression datasets (Alon et al., 1999; Armstrong et al., 2002; Ash et al., 2000; Catherine et al., 2003; Dinesh et al., 2002; Gavin et al., 2002; Golub et al., 1999; Scott et al., 2002; van ’t Veer et al., 2002; Yeoh et al., 2002; Zembutsu et al., 2002). The experimental results show that the proposed algorithm greatly outperforms existing methods, achieving high accuracy in classification. The outline of this chapter is as follows: Ensemble machine learning approach and three popular ensembles (i.e., Bagging, Boosting, and Arcing) are introduced first in the Background section; second, the analyses on existing ensembles, details of the proposed algorithm, and experimental results are presented in Method section, followed by discussions on the future trends and conclusion.

scholarly journals PEAK Predicts Gene Regulatory Network Linkages during Sea Urchin Development with High Sensitivity from Gene Expression Data Alone

2021 ◽  
Author(s):  
Jingyi Zhang ◽  
Farhan Ibrahim ◽  
Doaa Altarawy ◽  
Lenwood S Heath ◽  
Sarah Tulin
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Sea Urchin ◽  
Gene Expression Data ◽  
Regulatory Network ◽  
Gene Networks ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Gene Interactions ◽  
Expression Data

Abstract BackgroundGene regulatory network (GRN) inference can now take advantage of powerful machine learning algorithms to predict the entire landscape of gene-to-gene interactions with the potential to complement traditional experimental methods in building gene networks. However, the dynamical nature of embryonic development -- representing the time-dependent interactions between thousands of transcription factors, signaling molecules, and effector genes -- is one of the most challenging arenas for GRN prediction. ResultsIn this work, we show that successful GRN predictions for developmental systems from gene expression data alone can be obtained with the Priors Enriched Absent Knowledge (PEAK) network inference algorithm. PEAK is a noise-robust method that models gene expression dynamics via ordinary differential equations and selects the best network based on information-theoretic criteria coupled with the machine learning algorithm Elastic net. We test our GRN prediction methodology using two gene expression data sets for the purple sea urchin (S. purpuratus) and cross-check our results against existing GRN models that have been constructed and validated by over 30 years of experimental results. Our results found a remarkably high degree of sensitivity in identifying known gene interactions in the network (maximum 76.32%). We also generated 838 novel predictions for interactions that have not yet been described, which provide a resource for researchers to use to further complete the sea urchin GRN. ConclusionsGRN predictions that match known gene interactions can be produced using gene expression data alone from developmental time series experiments.

scholarly journals A Robust Procedure for Machine Learning Algorithms Using Gene Expression Data

2021 ◽  
Vol 12 (2) ◽  
pp. 2422-2439
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Gene Expression Data ◽  
Learning Algorithms ◽  
Simulated Data ◽  
Cancer Classification ◽  
Machine Learning Algorithms ◽  
Expression Data ◽  
Traditional Procedure

Cancer classification is one of the main objectives for analyzing big biological datasets. Machine learning algorithms (MLAs) have been extensively used to accomplish this task. Several popular MLAs are available in the literature to classify new samples into normal or cancer populations. Nevertheless, most of them often yield lower accuracies in the presence of outliers, which leads to incorrect classification of samples. Hence, in this study, we present a robust approach for the efficient and precise classification of samples using noisy GEDs. We examine the performance of the proposed procedure in a comparison of the five popular traditional MLAs (SVM, LDA, KNN, Naïve Bayes, Random forest) using both simulated and real gene expression data analysis. We also considered several rates of outliers (10%, 20%, and 50%). The results obtained from simulated data confirm that the traditional MLAs produce better results through our proposed procedure in the presence of outliers using the proposed modified datasets. The further transcriptome analysis found the significant involvement of these extra features in cancer diseases. The results indicated the performance improvement of the traditional MLAs with our proposed procedure. Hence, we propose to apply the proposed procedure instead of the traditional procedure for cancer classification.

scholarly journals Performance evaluation of different machine learning algorithms in presence of outliers using gene expression data

2019 ◽  
Vol 28 ◽  
pp. 69-80
Author(s):  
M Shahjaman ◽  
MM Rashid ◽  
MI Asifuzzaman ◽  
H Akter ◽  
SMS Islam ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Gene Expression Data ◽  
Naive Bayes ◽  
Learning Algorithms ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Expression Data ◽  
Data Generating Process

Classification of samples into one or more populations is one of the main objectives of gene expression data (GED) analysis. Many machine learning algorithms were employed in several studies to perform this task. However, these studies did not consider the outliers problem. GEDs are often contaminated by outliers due to several steps involve in the data generating process from hybridization of DNA samples to image analysis. Most of the algorithms produce higher false positives and lower accuracies in presence of outliers, particularly for lower number of replicates in the biological conditions. Therefore, in this paper, a comprehensive study has been carried out among five popular machine learning algorithms (SVM, RF, Naïve Bayes, k-NN and LDA) using both simulated and real gene expression datasets, in absence and presence of outliers. Three different rates of outliers (5%, 10% and 50%) and six performance indices (TPR, FPR, TNR, FNR, FDR and AUC) were considered to investigate the performance of five machine learning algorithms. Both simulated and real GED analysis results revealed that SVM produced comparatively better performance than the other four algorithms (RF, Naïve Bayes, k-NN and LDA) for both small-and-large sample sizes. J. bio-sci. 28: 69-80, 2020

scholarly journals Risk Monitoring and Quantitative Results of Various Attributes of Machine Learning Algorithms with a Time Series Data

2019 ◽  
Vol 8 (11) ◽  
pp. 4018-4022
Keyword(s):  
Machine Learning ◽  
Time Series Data ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Machine Learning Algorithm ◽  
Risk Modelling ◽  
Risk Monitoring ◽  
Quantitative Results

The aim of this research is to do risk modelling after analysis of twitter posts based on certain sentiment analysis. In this research we analyze posts of several users or a particular user to check whether they can be cause of concern to the society or not. Every sentiment like happy, sad, anger and other emotions are going to provide scaling of severity in the conclusion of final table on which machine learning algorithm is applied. The data which is put under the machine learning algorithms are been monitored over a period of time and it is related to a particular topic in an area

scholarly journals Significant Impact of Improved Machine Learning Algorithm in The Processes of Large Data Sets

2020 ◽  
pp. 458-467
Author(s):  
Virendra Tiwari ◽  
Balendra Garg ◽  
Uday Prakash Sharma
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Dynamic Environment ◽  
Large Data ◽  
Machine Learning Algorithms ◽  
Streaming Data ◽  
Machine Learning Techniques ◽  
Machine Learning Algorithm ◽  
Learning Mechanisms

The machine learning algorithms are capable of managing multi-dimensional data under the dynamic environment. Despite its so many vital features, there are some challenges to overcome. The machine learning algorithms still requires some additional mechanisms or procedures for predicting a large number of new classes with managing privacy. The deficiencies show the reliable use of a machine learning algorithm relies on human experts because raw data may complicate the learning process which may generate inaccurate results. So the interpretation of outcomes with expertise in machine learning mechanisms is a significant challenge in the machine learning algorithm. The machine learning technique suffers from the issue of high dimensionality, adaptability, distributed computing, scalability, the streaming data, and the duplicity. The main issue of the machine learning algorithm is found its vulnerability to manage errors. Furthermore, machine learning techniques are also found to lack variability. This paper studies how can be reduced the computational complexity of machine learning algorithms by finding how to make predictions using an improved algorithm.

A Surveillance on Machine Learning Algorithms and Its Applications

2020 ◽  
Vol 17 (9) ◽  
pp. 4294-4298
Author(s):  
B. R. Sunil Kumar ◽  
B. S. Siddhartha ◽  
S. N. Shwetha ◽  
K. Arpitha
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Sentiment Analysis ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Algorithm ◽  
Data Set ◽  
Pros And Cons ◽  
Primary Advantage

This paper intends to use distinct machine learning algorithms and exploring its multi-features. The primary advantage of machine learning is, a machine learning algorithm can predict its work automatically by learning what to do with information. This paper reveals the concept of machine learning and its algorithms which can be used for different applications such as health care, sentiment analysis and many more. Sometimes the programmers will get confused which algorithm to apply for their applications. This paper provides an idea related to the algorithm used on the basis of how accurately it fits. Based on the collected data, one of the algorithms can be selected based upon its pros and cons. By considering the data set, the base model is developed, trained and tested. Then the trained model is ready for prediction and can be deployed on the basis of feasibility.

scholarly journals Machine learning algorithms can predict tail biting outbreaks in pigs using feeding behaviour records

2021 ◽  
Author(s):  
Catherine Ollagnier ◽  
Claudia Kasper ◽  
Anna Wallenbeck ◽  
Linda Keeling ◽  
Siavash A Bigdeli
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Real Time ◽  
Feeding Behaviour ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Algorithm ◽  
Tail Biting ◽  
Testing Set

Tail biting is a detrimental behaviour that impacts the welfare and health of pigs. Early detection of tail biting precursor signs allows for preventive measures to be taken, thus avoiding the occurrence of the tail biting event. This study aimed to build a machine-learning algorithm for real time detection of upcoming tail biting outbreaks, using feeding behaviour data recorded by an electronic feeder. Prediction capacities of seven machine learning algorithms (e.g., random forest, neural networks) were evaluated from daily feeding data collected from 65 pens originating from 2 herds of grower-finisher pigs (25-100kg), in which 27 tail biting events occurred. Data were divided into training and testing data, either by randomly splitting data into 75% (training set) and 25% (testing set), or by randomly selecting pens to constitute the testing set. The random forest algorithm was able to predict 70% of the upcoming events with an accuracy of 94%, when predicting events in pens for which it had previous data. The detection of events for unknown pens was less sensitive, and the neural network model was able to detect 14% of the upcoming events with an accuracy of 63%. A machine-learning algorithm based on ongoing data collection should be considered for implementation into automatic feeder systems for real time prediction of tail biting events.

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