scholarly journals Exploration of predictive and prognostic alternative splicing signatures in lung adenocarcinoma using machine learning methods

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Qidong Cai ◽  
Boxue He ◽  
Pengfei Zhang ◽  
Zhenyu Zhao ◽  
Xiong Peng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alternative Splicing ◽  
Lung Adenocarcinoma ◽  
Prognostic Model ◽  
Cox Regression ◽  
Machine Learning Algorithms ◽  
The Cancer Genome Atlas ◽  
Sequencing Data ◽  
Learning Methods ◽  
Machine Learning Methods

Abstract Background Alternative splicing (AS) plays critical roles in generating protein diversity and complexity. Dysregulation of AS underlies the initiation and progression of tumors. Machine learning approaches have emerged as efficient tools to identify promising biomarkers. It is meaningful to explore pivotal AS events (ASEs) to deepen understanding and improve prognostic assessments of lung adenocarcinoma (LUAD) via machine learning algorithms. Method RNA sequencing data and AS data were extracted from The Cancer Genome Atlas (TCGA) database and TCGA SpliceSeq database. Using several machine learning methods, we identified 24 pairs of LUAD-related ASEs implicated in splicing switches and a random forest-based classifiers for identifying lymph node metastasis (LNM) consisting of 12 ASEs. Furthermore, we identified key prognosis-related ASEs and established a 16-ASE-based prognostic model to predict overall survival for LUAD patients using Cox regression model, random survival forest analysis, and forward selection model. Bioinformatics analyses were also applied to identify underlying mechanisms and associated upstream splicing factors (SFs). Results Each pair of ASEs was spliced from the same parent gene, and exhibited perfect inverse intrapair correlation (correlation coefficient = − 1). The 12-ASE-based classifier showed robust ability to evaluate LNM status of LUAD patients with the area under the receiver operating characteristic (ROC) curve (AUC) more than 0.7 in fivefold cross-validation. The prognostic model performed well at 1, 3, 5, and 10 years in both the training cohort and internal test cohort. Univariate and multivariate Cox regression indicated the prognostic model could be used as an independent prognostic factor for patients with LUAD. Further analysis revealed correlations between the prognostic model and American Joint Committee on Cancer stage, T stage, N stage, and living status. The splicing network constructed of survival-related SFs and ASEs depicts regulatory relationships between them. Conclusion In summary, our study provides insight into LUAD researches and managements based on these AS biomarkers.

scholarly journals Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Alan Brnabic ◽  
Lisa M. Hess
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Real World ◽  
Learning Algorithms ◽  
External Validation ◽  
Machine Learning Algorithms ◽  
Learning Methods ◽  
Machine Learning Methods

Abstract Background Machine learning is a broad term encompassing a number of methods that allow the investigator to learn from the data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient-provider decision making. Methods This systematic literature review was conducted to identify published observational research of employed machine learning to inform decision making at the patient-provider level. The search strategy was implemented and studies meeting eligibility criteria were evaluated by two independent reviewers. Relevant data related to study design, statistical methods and strengths and limitations were identified; study quality was assessed using a modified version of the Luo checklist. Results A total of 34 publications from January 2014 to September 2020 were identified and evaluated for this review. There were diverse methods, statistical packages and approaches used across identified studies. The most common methods included decision tree and random forest approaches. Most studies applied internal validation but only two conducted external validation. Most studies utilized one algorithm, and only eight studies applied multiple machine learning algorithms to the data. Seven items on the Luo checklist failed to be met by more than 50% of published studies. Conclusions A wide variety of approaches, algorithms, statistical software, and validation strategies were employed in the application of machine learning methods to inform patient-provider decision making. There is a need to ensure that multiple machine learning approaches are used, the model selection strategy is clearly defined, and both internal and external validation are necessary to be sure that decisions for patient care are being made with the highest quality evidence. Future work should routinely employ ensemble methods incorporating multiple machine learning algorithms.

scholarly journals Machine-learning based prediction of Cushing’s syndrome in dogs attending UK primary-care veterinary practice

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Imogen Schofield ◽  
David C. Brodbelt ◽  
Noel Kennedy ◽  
Stijn J. M. Niessen ◽  
David B. Church ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cushing’S Syndrome ◽  
Clinical Decision Making ◽  
Predictive Performance ◽  
Clinical Decision ◽  
Cushing's Syndrome ◽  
Machine Learning Algorithms ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Clinical Records

AbstractCushing’s syndrome is an endocrine disease in dogs that negatively impacts upon the quality-of-life of affected animals. Cushing’s syndrome can be a challenging diagnosis to confirm, therefore new methods to aid diagnosis are warranted. Four machine-learning algorithms were applied to predict a future diagnosis of Cushing's syndrome, using structured clinical data from the VetCompass programme in the UK. Dogs suspected of having Cushing's syndrome were included in the analysis and classified based on their final reported diagnosis within their clinical records. Demographic and clinical features available at the point of first suspicion by the attending veterinarian were included within the models. The machine-learning methods were able to classify the recorded Cushing’s syndrome diagnoses, with good predictive performance. The LASSO penalised regression model indicated the best overall performance when applied to the test set with an AUROC = 0.85 (95% CI 0.80–0.89), sensitivity = 0.71, specificity = 0.82, PPV = 0.75 and NPV = 0.78. The findings of our study indicate that machine-learning methods could predict the future diagnosis of a practicing veterinarian. New approaches using these methods could support clinical decision-making and contribute to improved diagnosis of Cushing’s syndrome in dogs.

scholarly journals Acoustic feature-based sentiment analysis of call center data

2017 ◽  
Author(s):  
◽  
Zeshan Peng
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Emotion Recognition ◽  
Sentiment Analysis ◽  
Call Center ◽  
Machine Learning Algorithms ◽  
Language Recognition ◽  
Acoustic Features ◽  
Learning Methods ◽  
Machine Learning Methods

With the advancement of machine learning methods, audio sentiment analysis has become an active research area in recent years. For example, business organizations are interested in persuasion tactics from vocal cues and acoustic measures in speech. A typical approach is to find a set of acoustic features from audio data that can indicate or predict a customer's attitude, opinion, or emotion state. For audio signals, acoustic features have been widely used in many machine learning applications, such as music classification, language recognition, emotion recognition, and so on. For emotion recognition, previous work shows that pitch and speech rate features are important features. This thesis work focuses on determining sentiment from call center audio records, each containing a conversation between a sales representative and a customer. The sentiment of an audio record is considered positive if the conversation ended with an appointment being made, and is negative otherwise. In this project, a data processing and machine learning pipeline for this problem has been developed. It consists of three major steps: 1) an audio record is split into segments by speaker turns; 2) acoustic features are extracted from each segment; and 3) classification models are trained on the acoustic features to predict sentiment. Different set of features have been used and different machine learning methods, including classical machine learning algorithms and deep neural networks, have been implemented in the pipeline. In our deep neural network method, the feature vectors of audio segments are stacked in temporal order into a feature matrix, which is fed into deep convolution neural networks as input. Experimental results based on real data shows that acoustic features, such as Mel frequency cepstral coefficients, timbre and Chroma features, are good indicators for sentiment. Temporal information in an audio record can be captured by deep convolutional neural networks for improved prediction accuracy.

scholarly journals MODIS-FIRMS and ground-truthing based wildfire likelihood mapping of Sikkim Himalaya using machine learning algorithms.

2021 ◽  
Author(s):  
Polash Banerjee
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Tree Cover ◽  
Anthropogenic Factors ◽  
Gradient Boosting ◽  
Support Vector ◽  
Learning Methods ◽  
Sikkim Himalaya ◽  
Environmental Features ◽  
Machine Learning Methods

Abstract Wildfires in limited extent and intensity can be a boon for the forest ecosystem. However, recent episodes of wildfires of 2019 in Australia and Brazil are sad reminders of their heavy ecological and economical costs. Understanding the role of environmental factors in the likelihood of wildfires in a spatial context would be instrumental in mitigating it. In this study, 14 environmental features encompassing meteorological, topographical, ecological, in situ and anthropogenic factors have been considered for preparing the wildfire likelihood map of Sikkim Himalaya. A comparative study on the efficiency of machine learning methods like Generalized Linear Model (GLM), Support Vector Machine (SVM), Random Forest (RF) and Gradient Boosting Model (GBM) has been performed to identify the best performing algorithm in wildfire prediction. The study indicates that all the machine learning methods are good at predicting wildfires. However, RF has outperformed, followed by GBM in the prediction. Also, environmental features like average temperature, average wind speed, proximity to roadways and tree cover percentage are the most important determinants of wildfires in Sikkim Himalaya. This study can be considered as a decision support tool for preparedness, efficient resource allocation and sensitization of people towards mitigation of wildfires in Sikkim.

scholarly journals Predicting lung adenocarcinoma disease progression using methylation-correlated blocks and ensemble machine learning classifiers

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10884
Author(s):  
Xin Yu ◽  
Qian Yang ◽  
Dong Wang ◽  
Zhaoyang Li ◽  
Nianhang Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lung Adenocarcinoma ◽  
Cox Regression ◽  
Characteristic Curve ◽  
The Cancer Genome Atlas ◽  
Support Vector ◽  
Survival Prediction ◽  
Ensemble Model ◽  
Training Set ◽  
Cpg Sites

Applying the knowledge that methyltransferases and demethylases can modify adjacent cytosine-phosphorothioate-guanine (CpG) sites in the same DNA strand, we found that combining multiple CpGs into a single block may improve cancer diagnosis. However, survival prediction remains a challenge. In this study, we developed a pipeline named “stacked ensemble of machine learning models for methylation-correlated blocks” (EnMCB) that combined Cox regression, support vector regression (SVR), and elastic-net models to construct signatures based on DNA methylation-correlated blocks for lung adenocarcinoma (LUAD) survival prediction. We used methylation profiles from the Cancer Genome Atlas (TCGA) as the training set, and profiles from the Gene Expression Omnibus (GEO) as validation and testing sets. First, we partitioned the genome into blocks of tightly co-methylated CpG sites, which we termed methylation-correlated blocks (MCBs). After partitioning and feature selection, we observed different diagnostic capacities for predicting patient survival across the models. We combined the multiple models into a single stacking ensemble model. The stacking ensemble model based on the top-ranked block had the area under the receiver operating characteristic curve of 0.622 in the TCGA training set, 0.773 in the validation set, and 0.698 in the testing set. When stratified by clinicopathological risk factors, the risk score predicted by the top-ranked MCB was an independent prognostic factor. Our results showed that our pipeline was a reliable tool that may facilitate MCB selection and survival prediction.

scholarly journals Landslide susceptibility mapping using machine learning for Wenchuan County, Sichuan province, China

2020 ◽  
Vol 198 ◽  
pp. 03023
Author(s):  
Xin Yang ◽  
Rui Liu ◽  
Luyao Li ◽  
Mei Yang ◽  
Yuantao Yang
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Machine Learning Algorithms ◽  
Landslide Susceptibility Mapping ◽  
Support Vector ◽  
Roc Curve Analysis ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Boosted Decision Tree

Landslide susceptibility mapping is a method used to assess the probability and spatial distribution of landslide occurrences. Machine learning methods have been widely used in landslide susceptibility in recent years. In this paper, six popular machine learning algorithms namely logistic regression, multi-layer perceptron, random forests, support vector machine, Adaboost, and gradient boosted decision tree were leveraged to construct landslide susceptibility models with a total of 1365 landslide points and 14 predisposing factors. Subsequently, the landslide susceptibility maps (LSM) were generated by the trained models. LSM shows the main landslide zone is concentrated in the southeastern area of Wenchuan County. The result of ROC curve analysis shows that all models fitted the training datasets and achieved satisfactory results on validation datasets. The results of this paper reveal that machine learning methods are feasible to build robust landslide susceptibility models.

Advanced Machine Learning Methods for Prediction of Fracture Closure Pressure

2021 ◽  
Author(s):  
Mohamed Ibrahim Mohamed ◽  
Dinesh Mehta ◽  
Erdal Ozkan
Keyword(s):  
Machine Learning ◽  
Integrated Approach ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Learning Methods ◽  
Pressure Derivative ◽  
Fracture Geometry ◽  
Machine Learning Methods ◽  
Personal Bias ◽  
Fracture Closure

Abstract Determining the closure pressure is crucial for optimal hydraulic fracturing design and successful execution of fracturing treatment. Historically, the use of diagnostic tests before the main fracturing treatment has significantly advanced to gain more information about the pattern of fracture propagation and fluid performance to optimize the designs. The goal is to inject a small volume of fracturing fluid to breakdown the formation and create small fracture geometry, then once pumping is stopped the pressure decline is analyzed to observe the fracture closure. Many analytical methods such as G-Function, square root of time, etc. have been developed to determine the fracture closure pressure. There are cases in which there is difficulty in determining the fracture closure pressure, as well as personal bias and field experiences make it challenging to interpret the changes in the pressure derivative slope and identify fracture closure. These conditions include: High permeability reservoirs where fracture closure occurs very fast due to the quick fluid leakoff.Extremely low permeability reservoir, which requires a long shut-in time for the fluid to leak off and determine the fracture closure pressure.The non-ideal fluid leak-off behavior under complex conditions. The objective of this study is to apply machine learning methods to implement a predesigned algorithm to execute the required tasks and predict the fracture closure pressure while minimizing the shortcomings in determining the closure pressure for non-ideal or subjective conditions. This paper demonstrates training different supervised machine learning algorithms to help predict fracture closure pressure. The workflow involves using the datasets to train and optimize the models, which subsequently are used to predict the closure pressure of testing data. The output results are then compared with actual results from more than 120 DFIT data points. We further propose an integrated approach to feature selection and dataset processing and study the effects of data processing on the success of the model prediction. The results from this study limit the subjectivity and the need for the experience of personal interpreting the data. We speculate that a linear regression and MLP neural network algorithms can yield high scores in the prediction of fracture closure pressure.

Machine Learning Approaches for Sentiment Analysis

2014 ◽  
pp. 193-208 ◽  
Author(s):  
Basant Agarwal ◽  
Namita Mittal
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Machine Learning Algorithms ◽  
Sentiment Classification ◽  
Learning Approaches ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Knowledge Based ◽  
Semantic Orientation

Opinion Mining or Sentiment Analysis is the study that analyzes people's opinions or sentiments from the text towards entities such as products and services. It has always been important to know what other people think. With the rapid growth of availability and popularity of online review sites, blogs', forums', and social networking sites' necessity of analysing and understanding these reviews has arisen. The main approaches for sentiment analysis can be categorized into semantic orientation-based approaches, knowledge-based, and machine-learning algorithms. This chapter surveys the machine learning approaches applied to sentiment analysis-based applications. The main emphasis of this chapter is to discuss the research involved in applying machine learning methods mostly for sentiment classification at document level. Machine learning-based approaches work in the following phases, which are discussed in detail in this chapter for sentiment classification: (1) feature extraction, (2) feature weighting schemes, (3) feature selection, and (4) machine-learning methods. This chapter also discusses the standard free benchmark datasets and evaluation methods for sentiment analysis. The authors conclude the chapter with a comparative study of some state-of-the-art methods for sentiment analysis and some possible future research directions in opinion mining and sentiment analysis.

Machine Learning Approaches for Sentiment Analysis

Big Data ◽  
2016 ◽  
pp. 1917-1933
Author(s):  
Basant Agarwal ◽  
Namita Mittal
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Machine Learning Algorithms ◽  
Sentiment Classification ◽  
Learning Approaches ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Knowledge Based ◽  
Semantic Orientation

Opinion Mining or Sentiment Analysis is the study that analyzes people's opinions or sentiments from the text towards entities such as products and services. It has always been important to know what other people think. With the rapid growth of availability and popularity of online review sites, blogs', forums', and social networking sites' necessity of analysing and understanding these reviews has arisen. The main approaches for sentiment analysis can be categorized into semantic orientation-based approaches, knowledge-based, and machine-learning algorithms. This chapter surveys the machine learning approaches applied to sentiment analysis-based applications. The main emphasis of this chapter is to discuss the research involved in applying machine learning methods mostly for sentiment classification at document level. Machine learning-based approaches work in the following phases, which are discussed in detail in this chapter for sentiment classification: (1) feature extraction, (2) feature weighting schemes, (3) feature selection, and (4) machine-learning methods. This chapter also discusses the standard free benchmark datasets and evaluation methods for sentiment analysis. The authors conclude the chapter with a comparative study of some state-of-the-art methods for sentiment analysis and some possible future research directions in opinion mining and sentiment analysis.

scholarly journals Predicting rice blast disease: machine learning versus process-based models

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
David F. Nettleton ◽  
Dimitrios Katsantonis ◽  
Argyris Kalaitzidis ◽  
Natasa Sarafijanovic-Djukic ◽  
Pau Puigdollers ◽  
...  
Keyword(s):  
Machine Learning ◽  
Rice Blast ◽  
Machine Learning Algorithms ◽  
Rice Blast Disease ◽  
Blast Disease ◽  
Data Driven ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Plant Disease Management ◽  
Process Based Models

Abstract Background In this study, we compared four models for predicting rice blast disease, two operational process-based models (Yoshino and Water Accounting Rice Model (WARM)) and two approaches based on machine learning algorithms (M5Rules and Recurrent Neural Networks (RNN)), the former inducing a rule-based model and the latter building a neural network. In situ telemetry is important to obtain quality in-field data for predictive models and this was a key aspect of the RICE-GUARD project on which this study is based. According to the authors, this is the first time process-based and machine learning modelling approaches for supporting plant disease management are compared. Results Results clearly showed that the models succeeded in providing a warning of rice blast onset and presence, thus representing suitable solutions for preventive remedial actions targeting the mitigation of yield losses and the reduction of fungicide use. All methods gave significant “signals” during the “early warning” period, with a similar level of performance. M5Rules and WARM gave the maximum average normalized scores of 0.80 and 0.77, respectively, whereas Yoshino gave the best score for one site (Kalochori 2015). The best average values of r and r2 and %MAE (Mean Absolute Error) for the machine learning models were 0.70, 0.50 and 0.75, respectively and for the process-based models the corresponding values were 0.59, 0.40 and 0.82. Thus it has been found that the ML models are competitive with the process-based models. This result has relevant implications for the operational use of the models, since most of the available studies are limited to the analysis of the relationship between the model outputs and the incidence of rice blast. Results also showed that machine learning methods approximated the performances of two process-based models used for years in operational contexts. Conclusions Process-based and data-driven models can be used to provide early warnings to anticipate rice blast and detect its presence, thus supporting fungicide applications. Data-driven models derived from machine learning methods are a viable alternative to process-based approaches and – in cases when training datasets are available – offer a potentially greater adaptability to new contexts.

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