scholarly journals Prediction of prokaryotic transposases from protein features with machine learning approaches

2021 ◽  
Vol 7 (7) ◽  
Author(s):  
Qian Wang ◽  
Jun Ye ◽  
Teng Xu ◽  
Ning Zhou ◽  
Zhongqiu Lu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Antibiotic Resistance ◽  
Mutual Information ◽  
Ensemble Classifier ◽  
Training Dataset ◽  
Command Line ◽  
Learning Approaches ◽  
Command Line Tool ◽  
Selection Operator ◽  
Insight Into

Identification of prokaryotic transposases (Tnps) not only gives insight into the spread of antibiotic resistance and virulence but the process of DNA movement. This study aimed to develop a classifier for predicting Tnps in bacteria and archaea using machine learning (ML) approaches. We extracted a total of 2751 protein features from the training dataset including 14852 Tnps and 14852 controls, and selected 75 features as predictive signatures using the combined mutual information and least absolute shrinkage and selection operator algorithms. By aggregating these signatures, an ensemble classifier that integrated a collection of individual ML-based classifiers, was developed to identify Tnps. Further validation revealed that this classifier achieved good performance with an average AUC of 0.955, and met or exceeded other common methods. Based on this ensemble classifier, a stand-alone command-line tool designated TnpDiscovery was established to maximize the convenience for bioinformaticians and experimental researchers toward Tnp prediction. This study demonstrates the effectiveness of ML approaches in identifying Tnps, facilitating the discovery of novel Tnps in the future.

scholarly journals Mutual Information Scaling for Tensor Network Machine Learning

2021 ◽  
Author(s):  
Ian Convy ◽  
William Huggins ◽  
Haoran Liao ◽  
K Birgitta Whaley
Keyword(s):  
Machine Learning ◽  
Mutual Information ◽  
Many Body ◽  
Learning Tasks ◽  
Tensor Networks ◽  
Tensor Network ◽  
Logistic Regression Algorithm ◽  
Entanglement Structure ◽  
Insight Into ◽  
Specific Learning

Abstract Tensor networks have emerged as promising tools for machine learning, inspired by their widespread use as variational ansatze in quantum many-body physics. It is well known that the success of a given tensor network ansatz depends in part on how well it can reproduce the underlying entanglement structure of the target state, with different network designs favoring different scaling patterns. We demonstrate here how a related correlation analysis can be applied to tensor network machine learning, and explore whether classical data possess correlation scaling patterns similar to those found in quantum states which might indicate the best network to use for a given dataset. We utilize mutual information as measure of correlations in classical data, and show that it can serve as a lower-bound on the entanglement needed for a probabilistic tensor network classifier. We then develop a logistic regression algorithm to estimate the mutual information between bipartitions of data features, and verify its accuracy on a set of Gaussian distributions designed to mimic different correlation patterns. Using this algorithm, we characterize the scaling patterns in the MNIST and Tiny Images datasets, and find clear evidence of boundary-law scaling in the latter. This quantum-inspired classical analysis offers insight into the design of tensor networks which are best suited for specific learning tasks.

scholarly journals Informational and emotional elements in online support groups: a Bayesian approach to large-scale content analysis

2016 ◽  
Vol 23 (3) ◽  
pp. 508-513 ◽  
Author(s):  
Ulrike Deetjen ◽  
John A Powell
Keyword(s):  
Machine Learning ◽  
Support Groups ◽  
Emotional Support ◽  
Large Scale ◽  
Training Dataset ◽  
Online Support ◽  
Online Support Groups ◽  
Learning Approaches ◽  
Physical Conditions ◽  
Irritable Bowel

Objective This research examines the extent to which informational and emotional elements are employed in online support forums for 14 purposively sampled chronic medical conditions and the factors that influence whether posts are of a more informational or emotional nature. Methods Large-scale qualitative data were obtained from Dailystrength.org. Based on a hand-coded training dataset, all posts were classified into informational or emotional using a Bayesian classification algorithm to generalize the findings. Posts that could not be classified with a probability of at least 75% were excluded. Results The overall tendency toward emotional posts differs by condition: mental health (depression, schizophrenia) and Alzheimer’s disease consist of more emotional posts, while informational posts relate more to nonterminal physical conditions (irritable bowel syndrome, diabetes, asthma). There is no gender difference across conditions, although prostate cancer forums are oriented toward informational support, whereas breast cancer forums rather feature emotional support. Across diseases, the best predictors for emotional content are lower age and a higher number of overall posts by the support group member. Discussion The results are in line with previous empirical research and unify empirical findings from single/2-condition research. Limitations include the analytical restriction to predefined categories (informational, emotional) through the chosen machine-learning approach. Conclusion Our findings provide an empirical foundation for building theory on informational versus emotional support across conditions, give insights for practitioners to better understand the role of online support groups for different patients, and show the usefulness of machine-learning approaches to analyze large-scale qualitative health data from online settings.

scholarly journals A post-method condition analysis of using ensemble machine learning for cancer prognosis and diagnosis: a systematic review

2019 ◽  
Author(s):  
Leila Mirsadeghi ◽  
Ali Mohammad Banaei-Moghaddam ◽  
Seyed Reza Beh-Afarin ◽  
Reza Haji Hosseini ◽  
Kaveh Kavousi
Keyword(s):  
Machine Learning ◽  
Empirical Studies ◽  
Ensemble Methods ◽  
Feature Space ◽  
Ensemble Classifier ◽  
Cancer Prognosis ◽  
Learning Approaches ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Different Types

Abstract Background: Ensemble methods are supervised learning approaches that integrate different types of data or multiple individual classifiers. It has been shown that these methods can improve professional performance.Methods: This study is an attempt to provide an in-depth review on 45 most relevant articles and aims to introduce 42 ensemble classifier (EC) machine learning methods used for the detection of 18 different types of cancer. Compared to other types of cancer, breast cancer, and the 22 ensemble methods introduced for its identification, is extensively investigated. The purpose of this study is to identify, map, and analyze the current academic discourse on EC machine learning methods in order to: 1. identify overarching themes emerging from empirical studies as regards EC methods, 2. determine their input data and decision-making strategies, and 3. evaluate relevant statistical procedures.Results: By comparing various approaches, we can introduce Relevance Vector Machine (RVM)-based ensemble learning method that can provide optimal solutions for problems such as curse the dimensionality and high-dimensionality of feature space without missing data values.Conclusions: To obtain robust performance and achieve better results, it is tactfully suggested to use multi-omics data integration, which has demonstrated to identify cancers and their subtypes more efficiently.

scholarly journals A Post-Method Condition Analysis of Using Ensemble Machine Learning for Cancer Prognosis and Diagnosis: a systematic review

2019 ◽  
Author(s):  
Kaveh Kavousi ◽  
Leila Mirsadeghi ◽  
Reza Haji Hosseini ◽  
Ali Mohammad Banaei-Moghaddam ◽  
Seyed Reza Beh-Afarin
Keyword(s):  
Machine Learning ◽  
Empirical Studies ◽  
Ensemble Methods ◽  
Feature Space ◽  
Ensemble Classifier ◽  
Cancer Prognosis ◽  
Learning Approaches ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Different Types

Abstract Background Ensemble methods are supervised learning approaches that integrate different types of data or multiple individual classifiers. It has been shown that these methods can improve professional performance. Methods This study is an attempt to provide an in-depth review on 45 most relevant articles and aims to introduce 42 ensemble classifier (EC) machine learning methods used for the detection of 18 different types of cancer. Compared to other types of cancer, breast cancer, and the 22 ensemble methods introduced for its identification, is extensively investigated. The purpose of this study was to identify, map, and analyze the current academic discourse on EC machine learning methods in order to: 1. identify overarching themes emerging from empirical studies regarding EC methods, 2. determine their input data and decision-making strategies, and 3. evaluate relevant statistical procedures. Results By comparing various approaches, we can introduce Relevance Vector Machine (RVM)-based ensemble learning method that can provide optimal solutions for problems such as curse the dimensionality and high-dimensionality of feature space without missing data values. Conclusions To obtain robust performance and achieve better results, it is tactfully suggested to use multi-omics data integration, which has demonstrated to identify cancers and their subtypes more efficiently.

Learning and Explaining the Impact of Enterprises’ Organizational Quality on their Economic Results

2012 ◽  
pp. 228-248 ◽  
Author(s):  
Marko Pregeljc ◽  
Erik Štrumbelj ◽  
Miran Mihelcic ◽  
Igor Kononenko
Keyword(s):  
Machine Learning ◽  
Learning Approaches ◽  
Data Set ◽  
Economic Interpretation ◽  
Organizational Quality ◽  
Social Units ◽  
The Impact ◽  
Insight Into ◽  
General Explanation

The authors employed traditional and novel machine learning to improve insight into the connections between the quality of an organization of enterprises as a type of formal social units and the results of enterprises’ performance in this chapter. The analyzed data set contains 72 Slovenian enterprises’ economic results across four years and indicators of their organizational quality. The authors hypothesize that a causal relationship exists between the latter and the former. In the first part of a two-part process, they use several classification algorithms to study these relationships and to evaluate how accurately they predict the target economic results. However, the most successful models were often very complex and difficult to interpret, especially for non-technical users. Therefore, in the second part, the authors take advantage of a novel general explanation method that can be used to explain the influence of individual features on the model’s prediction. Results show that traditional machine-learning approaches are successful at modeling the dependency relationship. Furthermore, the explanation of the influence of the input features on the predicted economic results provides insights that have a meaningful economic interpretation.

scholarly journals Machine Learning Leveraging Genomes from Metagenomes Identifies Influential Antibiotic Resistance Genes in the Infant Gut Microbiome

mSystems ◽  
2018 ◽  
Vol 3 (1) ◽  
Author(s):  
Sumayah F. Rahman ◽  
Matthew R. Olm ◽  
Michael J. Morowitz ◽  
Jillian F. Banfield
Keyword(s):  
Machine Learning ◽  
Antibiotic Resistance ◽  
Resistance Genes ◽  
Sequence Data ◽  
Antibiotic Resistance Genes ◽  
Machine Learning Model ◽  
Infant Gut Microbiome ◽  
Microbial Systems ◽  
Gut Microbial Community ◽  
Insight Into

The process of reconstructing genomes from environmental sequence data (genome-resolved metagenomics) allows unique insight into microbial systems. We apply this technique to investigate how the antibiotic resistance genes of bacteria affect their ability to flourish in the gut under various conditions. Our analysis reveals that strain-level selection in formula-fed infants drives enrichment of beta-lactamase genes in the gut resistome. Using genomes from metagenomes, we built a machine learning model to predict how organisms in the gut microbial community respond to perturbation by antibiotics. This may eventually have clinical applications.

scholarly journals Comparing Classical and Modern Machine Learning Techniques for Monitoring Pedestrian Workers in Top-View Construction Site Video Sequences

2020 ◽  
Vol 10 (23) ◽  
pp. 8466
Author(s):  
Marcel Neuhausen ◽  
Dennis Pawlowski ◽  
Markus König
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Kalman Filter ◽  
Safety Management ◽  
Machine Learning Techniques ◽  
Training Dataset ◽  
Learning Approaches ◽  
Construction Sites ◽  
Learning Techniques ◽  
Different Characteristics

Keeping an overview of all ongoing processes on construction sites is almost unfeasible, especially for the construction workers executing their tasks. It is difficult for workers to concentrate on their work while paying attention to other processes. If their workflows in hazardous areas do not run properly, this can lead to dangerous accidents. Tracking pedestrian workers could improve the productivity and safety management on construction sites. For this, vision-based tracking approaches are suitable, but the training and evaluation of such a system requires a large amount of data originating from construction sites. These are rarely available, which complicates deep learning approaches. Thus, we use a small generic dataset and juxtapose a deep learning detector with an approach based on classical machine learning techniques. We identify workers using a YOLOv3 detector and compare its performance with an approach based on a soft cascaded classifier. Afterwards, tracking is done by a Kalman filter. In our experiments, the classical approach outperforms YOLOv3 on the detection task given a small training dataset. However, the Kalman filter is sufficiently robust to compensate for the drawbacks of YOLOv3. We found that both approaches generally yield a satisfying tracking performances but feature different characteristics.

scholarly journals Data Driven Encoding of Structures and Link Predictions in Large XML Document Collections

2011 ◽  
pp. 219-241
Author(s):  
Markus Hagenbuchner ◽  
Chung Tsoi ◽  
Shu Jia Zhang ◽  
Milly Kc
Keyword(s):  
Machine Learning ◽  
Data Driven ◽  
Training Dataset ◽  
Web Pages ◽  
Learning Approaches ◽  
Document Collections ◽  
Related Data ◽  
Xml Documents ◽  
Significant Research ◽  
Clustering Problems

In recent years there have been some significant research towards the ability of processing related data, particularly the relatedness among atomic elements in a structure with those in another structure. A number of approaches have been developed with various degrees of success. This chapter provides an overview of machine learning approaches for the encoding of related atomic elements in one structure with those in other structures. The chapter briefly reviews a number of unsupervised approaches for such data structures which can be used for solving generic classification, regression, and clustering problems. We will apply this approach to a particularly interesting and challenging problem: The prediction of both the number and their locations of the in-links and out-links of a set of XML documents. In this problem, we are given a set of XML pages, which may represent web pages on the Internet, with in-links and out-links. Based on this training dataset, we wish to predict the number and locations of in-links and out-links of a set of XML documents, which are as yet not linked to other existing XML documents. To the best of our knowledge, this is the only known data driven unsupervised machine learning approach for the prediction of in-links and out-links of XML documents.

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