iEnhancer-XG: interpretable sequence-based enhancers and their strength predictor

Abstract Motivation Enhancers are non-coding DNA fragments with high position variability and free scattering. They play an important role in controlling gene expression. As machine learning has become more widely used in identifying enhancers, a number of bioinformatic tools have been developed. Although several models for identifying enhancers and their strengths have been proposed, their accuracy and efficiency have yet to be improved. Results We propose a two-layer predictor called ‘iEnhancer-XG.’ It comprises a one-layer predictor (for identifying enhancers) and a second classifier (for their strength) and uses ‘XGBoost’ as a base classifier and five feature extraction methods, namely, k-Spectrum Profile, Mismatch k-tuple, Subsequence Profile, Position-specific scoring matrix (PSSM) and Pseudo dinucleotide composition (PseDNC). Each method has an independent output. We place the feature vector matrix into the ensemble learning for fusion. This experiment involves the method of ‘SHapley Additive explanations’ to provide interpretability for the previous black box machine learning methods and improve their credibility. The accuracies of the ensemble learning method are 0.811 (first layer) and 0.657 (second layer). The rigorous 10-fold cross-validation confirms that the proposed method is significantly better than existing technologies. Availability and implementation The source code and dataset for the enhancer predictions have been uploaded to https://github.com/jimmyrate/ienhancer-xg. Supplementary information Supplementary data are available at Bioinformatics online.

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Improving the Accuracy for Analyzing Heart Diseases Prediction Based on the Ensemble Method

Complexity ◽

10.1155/2021/6663455 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Xiao-Yan Gao ◽

Abdelmegeid Amin Ali ◽

Hassan Shaban Hassan ◽

Eman M. Anwar

Keyword(s):

Machine Learning ◽

Heart Disease ◽

Ensemble Learning ◽

Heart Diseases ◽

Principal Component ◽

Extraction Methods ◽

Machine Learning Algorithms ◽

Learning Methods ◽

Linear Discriminant ◽

Bagging Ensemble

Heart disease is the deadliest disease and one of leading causes of death worldwide. Machine learning is playing an essential role in the medical side. In this paper, ensemble learning methods are used to enhance the performance of predicting heart disease. Two features of extraction methods: linear discriminant analysis (LDA) and principal component analysis (PCA), are used to select essential features from the dataset. The comparison between machine learning algorithms and ensemble learning methods is applied to selected features. The different methods are used to evaluate models: accuracy, recall, precision, F-measure, and ROC.The results show the bagging ensemble learning method with decision tree has achieved the best performance.

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An Ensemble Learning Approach for Electrocardiogram Sensor Based Human Emotion Recognition

Sensors ◽

10.3390/s19204495 ◽

2019 ◽

Vol 19 (20) ◽

pp. 4495 ◽

Cited By ~ 9

Author(s):

Theekshana Dissanayake ◽

Yasitha Rajapaksha ◽

Roshan Ragel ◽

Isuru Nawinne

Keyword(s):

Machine Learning ◽

Feature Extraction ◽

Emotion Recognition ◽

Ensemble Learning ◽

Classification Accuracy ◽

Extraction Methods ◽

Learning Approach ◽

Human Emotion ◽

Different Types ◽

Human Emotions

Recently, researchers in the area of biosensor based human emotion recognition have used different types of machine learning models for recognizing human emotions. However, most of them still lack the ability to recognize human emotions with higher classification accuracy incorporating a limited number of bio-sensors. In the domain of machine learning, ensemble learning methods have been successfully applied to solve different types of real-world machine learning problems which require improved classification accuracies. Emphasising on that, this research suggests an ensemble learning approach for developing a machine learning model that can recognize four major human emotions namely: anger; sadness; joy; and pleasure incorporating electrocardiogram (ECG) signals. As feature extraction methods, this analysis combines four ECG signal based techniques, namely: heart rate variability; empirical mode decomposition; with-in beat analysis; and frequency spectrum analysis. The first three feature extraction methods are well-known ECG based feature extraction techniques mentioned in the literature, and the fourth technique is a novel method proposed in this study. The machine learning procedure of this investigation evaluates the performance of a set of well-known ensemble learners for emotion classification and further improves the classification results using feature selection as a prior step to ensemble model training. Compared to the best performing single biosensor based model in the literature, the developed ensemble learner has the accuracy gain of 10.77%. Furthermore, the developed model outperforms most of the multiple biosensor based emotion recognition models with a significantly higher classification accuracy gain.

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A stacking ensemble learning framework for genomic prediction

10.21203/rs.3.rs-52592/v1 ◽

2020 ◽

Author(s):

Mang Liang ◽

Tianpeng Chang ◽

Bingxing An ◽

Xinghai Duan ◽

Lili Du ◽

...

Keyword(s):

Machine Learning ◽

Ensemble Learning ◽

Genomic Prediction ◽

Milk Fat ◽

Prediction Accuracy ◽

Support Vector ◽

Fat Percentage ◽

Prediction Ability ◽

Learning Framework ◽

Better Than

Abstract Background: Machine learning (ML) is perhaps the most useful for the interpretation of large genomic datasets. However, the performance of a single machine learning method in genomic selection (GS) was unsatisfactory in existing research. To improve the genomic predictions, we constructed a stacking ensemble learning framework (SELF) integrated three machine learning methods to predict genomic estimated breeding values (GEBVs). Results: We evaluated the prediction ability of SELF by three real datasets and compared the prediction accuracy of SELF, base learners, GBLUP and BayesB. For each trait, SELF performed better than base learners, which included support vector regression (SVR), kernel ridge regression (KRR) and elastic net (ENET). The prediction accuracy of SELF had an average 7.70% improvement compared with GBLUP in three datasets. Except for the milk fat percentage (MFP) traits of the German Holstein dairy cattle dataset, SELF more robust than BayesB in the remaining traits.Conclusions: In this study, we utilized a stacking ensemble learning framework (SELF) to genomic prediction and it performed much better than GBLUP and BayesB in three real datasets with different genetic architecture. Therefore, we believed SEFL had the potential to be promoted to estimate GEBVs in other animals and plants.

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A Novel Unsupervised Machine Learning-Based Method for Chatter Detection in the Milling of Thin-Walled Parts

Sensors ◽

10.3390/s21175779 ◽

2021 ◽

Vol 21 (17) ◽

pp. 5779

Author(s):

Runqiong Wang ◽

Qinghua Song ◽

Zhanqiang Liu ◽

Haifeng Ma ◽

Munish Kumar Gupta ◽

...

Keyword(s):

Machine Learning ◽

Feature Extraction ◽

Learning Algorithms ◽

Bending Moment ◽

Extraction Methods ◽

Machine Learning Algorithms ◽

Chatter Detection ◽

Unsupervised Machine Learning ◽

Milling Chatter ◽

Better Than

Data-driven chatter detection techniques avoid complex physical modeling and provide the basis for industrial applications of cutting process monitoring. Among them, feature extraction is the key step of chatter detection, which can compensate for the accuracy disadvantage of machine learning algorithms to some extent if the extracted features are highly correlated with the milling condition. However, the classification accuracy of the current feature extraction methods is not satisfactory, and a combination of multiple features is required to identify the chatter. This limits the development of unsupervised machine learning algorithms for chattering detection, which further affects the application in practical processing. In this paper, the fractal feature of the signal is extracted by structure function method (SFM) for the first time, which solves the problem that the features are easily affected by process parameters. Milling chatter is identified based on k-means algorithm, which avoids the complex process of training model, and the judgment method of milling chatter is also discussed. The proposed method can achieve 94.4% identification accuracy by using only one single signal feature, which is better than other feature extraction methods, and even better than some supervised machine learning algorithms. Moreover, experiments show that chatter will affect the distribution of cutting bending moment, and it is not reliable to monitor tool wear through the polar plot of the bending moment. This provides a theoretical basis for the application of unsupervised machine learning algorithms in chatter detection.

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Exploring the Use of Machine Learning to Automate the Qualitative Coding of Church-related Tweets

Fieldwork in Religion ◽

10.1558/firn.40610 ◽

2020 ◽

Vol 14 (2) ◽

pp. 140-159

Author(s):

Anthony-Paul Cooper ◽

Emmanuel Awuni Kolog ◽

Erkki Sutinen

Keyword(s):

Machine Learning ◽

Online Community ◽

High Volume ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Social Media Data ◽

Twitter Data ◽

Resource Intensity ◽

Media Data ◽

Better Than

This article builds on previous research around the exploration of the content of church-related tweets. It does so by exploring whether the qualitative thematic coding of such tweets can, in part, be automated by the use of machine learning. It compares three supervised machine learning algorithms to understand how useful each algorithm is at a classification task, based on a dataset of human-coded church-related tweets. The study finds that one such algorithm, Naïve-Bayes, performs better than the other algorithms considered, returning Precision, Recall and F-measure values which each exceed an acceptable threshold of 70%. This has far-reaching consequences at a time where the high volume of social media data, in this case, Twitter data, means that the resource-intensity of manual coding approaches can act as a barrier to understanding how the online community interacts with, and talks about, church. The findings presented in this article offer a way forward for scholars of digital theology to better understand the content of online church discourse.

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Identification of Anti-cancer Peptides Based on Multi-classifier System

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207322666191203141102 ◽

2020 ◽

Vol 22 (10) ◽

pp. 694-704 ◽

Cited By ~ 2

Author(s):

Wanben Zhong ◽

Bineng Zhong ◽

Hongbo Zhang ◽

Ziyi Chen ◽

Yan Chen

Keyword(s):

Machine Learning ◽

Side Effect ◽

Learning Models ◽

Normal Cells ◽

Classifier System ◽

Prediction Rate ◽

Anti Cancer ◽

Feature Information ◽

Machine Learning Models ◽

Better Than

Aim and Objective: Cancer is one of the deadliest diseases, taking the lives of millions every year. Traditional methods of treating cancer are expensive and toxic to normal cells. Fortunately, anti-cancer peptides (ACPs) can eliminate this side effect. However, the identification and development of new anti Materials and Methods: In our study, a multi-classifier system was used, combined with multiple machine learning models, to predict anti-cancer peptides. These individual learners are composed of different feature information and algorithms, and form a multi-classifier system by voting. Results and Conclusion: The experiments show that the overall prediction rate of each individual learner is above 80% and the overall accuracy of multi-classifier system for anti-cancer peptides prediction can reach 95.93%, which is better than the existing prediction model.

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Computing Possible Futures

10.1093/oso/9780198846420.001.0001 ◽

2019 ◽

Cited By ~ 2

Author(s):

William B. Rouse

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Computational Modeling ◽

Mental Model ◽

Data Analytics ◽

Computational Models ◽

Senior Managers ◽

Interactive Visualizations ◽

Use Of Models ◽

Better Than

This book discusses the use of models and interactive visualizations to explore designs of systems and policies in determining whether such designs would be effective. Executives and senior managers are very interested in what “data analytics” can do for them and, quite recently, what the prospects are for artificial intelligence and machine learning. They want to understand and then invest wisely. They are reasonably skeptical, having experienced overselling and under-delivery. They ask about reasonable and realistic expectations. Their concern is with the futurity of decisions they are currently entertaining. They cannot fully address this concern empirically. Thus, they need some way to make predictions. The problem is that one rarely can predict exactly what will happen, only what might happen. To overcome this limitation, executives can be provided predictions of possible futures and the conditions under which each scenario is likely to emerge. Models can help them to understand these possible futures. Most executives find such candor refreshing, perhaps even liberating. Their job becomes one of imagining and designing a portfolio of possible futures, assisted by interactive computational models. Understanding and managing uncertainty is central to their job. Indeed, doing this better than competitors is a hallmark of success. This book is intended to help them understand what fundamentally needs to be done, why it needs to be done, and how to do it. The hope is that readers will discuss this book and develop a “shared mental model” of computational modeling in the process, which will greatly enhance their chances of success.

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BloodGen3Module: Blood transcriptional module repertoire analysis and visualization using R

Bioinformatics ◽

10.1093/bioinformatics/btab121 ◽

2021 ◽

Author(s):

Darawan Rinchai ◽

Jessica Roelands ◽

Mohammed Toufiq ◽

Wouter Hendrickx ◽

Matthew C Altman ◽

...

Keyword(s):

Transcript Abundance ◽

R Package ◽

Supplementary Information ◽

Illustrative Case ◽

Bioinformatic Tools ◽

Transcriptional Module ◽

Wide Range ◽

Downstream Analysis ◽

Computing Module ◽

Parallel Workflow

Abstract Motivation We previously described the construction and characterization of generic and reusable blood transcriptional module repertoires. More recently we released a third iteration (“BloodGen3” module repertoire) that comprises 382 functionally annotated gene sets (modules) and encompasses 14,168 transcripts. Custom bioinformatic tools are needed to support downstream analysis, visualization and interpretation relying on such fixed module repertoires. Results We have developed and describe here a R package, BloodGen3Module. The functions of our package permit group comparison analyses to be performed at the module-level, and to display the results as annotated fingerprint grid plots. A parallel workflow for computing module repertoire changes for individual samples rather than groups of samples is also available; these results are displayed as fingerprint heatmaps. An illustrative case is used to demonstrate the steps involved in generating blood transcriptome repertoire fingerprints of septic patients. Taken together, this resource could facilitate the analysis and interpretation of changes in blood transcript abundance observed across a wide range of pathological and physiological states. Availability The BloodGen3Module package and documentation are freely available from Github: https://github.com/Drinchai/BloodGen3Module Supplementary information Supplementary data are available at Bioinformatics online.

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NLOS Multipath Classification of GNSS Signal Correlation Output Using Machine Learning

Sensors ◽

10.3390/s21072503 ◽

2021 ◽

Vol 21 (7) ◽

pp. 2503

Author(s):

Taro Suzuki ◽

Yoshiharu Amano

Keyword(s):

Machine Learning ◽

Satellite System ◽

Training Data ◽

Support Vector ◽

Positioning Errors ◽

Automated Method ◽

Global Navigation Satellite ◽

Better Than ◽

Signal Correlation

This paper proposes a method for detecting non-line-of-sight (NLOS) multipath, which causes large positioning errors in a global navigation satellite system (GNSS). We use GNSS signal correlation output, which is the most primitive GNSS signal processing output, to detect NLOS multipath based on machine learning. The shape of the multi-correlator outputs is distorted due to the NLOS multipath. The features of the shape of the multi-correlator are used to discriminate the NLOS multipath. We implement two supervised learning methods, a support vector machine (SVM) and a neural network (NN), and compare their performance. In addition, we also propose an automated method of collecting training data for LOS and NLOS signals of machine learning. The evaluation of the proposed NLOS detection method in an urban environment confirmed that NN was better than SVM, and 97.7% of NLOS signals were correctly discriminated.

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Machine Learning for the Dynamic Positioning of UAVs for Extended Connectivity

Sensors ◽

10.3390/s21134618 ◽

2021 ◽

Vol 21 (13) ◽

pp. 4618

Author(s):

Francisco Oliveira ◽

Miguel Luís ◽

Susana Sargento

Keyword(s):

Machine Learning ◽

Cellular Networks ◽

Real Data ◽

Emerging Technology ◽

Machine Learning Algorithms ◽

Base Stations ◽

Aerial Vehicle ◽

Positioning Algorithm ◽

The Military ◽

Better Than

Unmanned Aerial Vehicle (UAV) networks are an emerging technology, useful not only for the military, but also for public and civil purposes. Their versatility provides advantages in situations where an existing network cannot support all requirements of its users, either because of an exceptionally big number of users, or because of the failure of one or more ground base stations. Networks of UAVs can reinforce these cellular networks where needed, redirecting the traffic to available ground stations. Using machine learning algorithms to predict overloaded traffic areas, we propose a UAV positioning algorithm responsible for determining suitable positions for the UAVs, with the objective of a more balanced redistribution of traffic, to avoid saturated base stations and decrease the number of users without a connection. The tests performed with real data of user connections through base stations show that, in less restrictive network conditions, the algorithm to dynamically place the UAVs performs significantly better than in more restrictive conditions, reducing significantly the number of users without a connection. We also conclude that the accuracy of the prediction is a very important factor, not only in the reduction of users without a connection, but also on the number of UAVs deployed.

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