Advanced Machine Learning Approaches in Cancer Prognosis

2021 ◽  
Keyword(s):  
Machine Learning ◽  
Cancer Prognosis ◽  
Learning Approaches

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.

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.

Multiple vehicles detection and tracking for intelligent transport systems using machine learning approaches

2019 ◽  
Vol 70 (3) ◽  
pp. 214-224
Author(s):  
Bui Ngoc Dung ◽  
Manh Dzung Lai ◽  
Tran Vu Hieu ◽  
Nguyen Binh T. H.
Keyword(s):  
Machine Learning ◽  
Gaussian Mixture ◽  
Research Field ◽  
Transport Systems ◽  
Learning Approaches ◽  
Subtraction Method ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Detection And Tracking ◽  
Multiple Vehicles

Video surveillance is emerging research field of intelligent transport systems. This paper presents some techniques which use machine learning and computer vision in vehicles detection and tracking. Firstly the machine learning approaches using Haar-like features and Ada-Boost algorithm for vehicle detection are presented. Secondly approaches to detect vehicles using the background subtraction method based on Gaussian Mixture Model and to track vehicles using optical flow and multiple Kalman filters were given. The method takes advantages of distinguish and tracking multiple vehicles individually. The experimental results demonstrate high accurately of the method.

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

DETECTION OF ANOMALY BASED APPLICATION LAYER DDoS ATTACKS USING MACHINE LEARNING APPROACHES

2016 ◽  
Vol 4 (2) ◽  
pp. 6
Author(s):  
VANI NIDHI M.S.P.S. ◽  
PRASAD K. MUNIVARA ◽  
◽  
Keyword(s):  
Machine Learning ◽  
Learning Approaches ◽  
Ddos Attacks ◽  
Application Layer

Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach

2019 ◽  
Author(s):  
Oskar Flygare ◽  
Jesper Enander ◽  
Erik Andersson ◽  
Brjánn Ljótsson ◽  
Volen Z Ivanov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forests ◽  
Clinical Utility ◽  
Body Dysmorphic Disorder ◽  
Prediction Models ◽  
Behavioral Therapy ◽  
Learning Approach ◽  
Learning Approaches ◽  
Machine Learning Approach

**Background:** Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in prediction models. **Methods:** This study used a random forests machine learning approach to test if it is possible to reliably predict remission from BDD in a sample of 88 individuals that had received internet-delivered cognitive behavioral therapy for BDD. The random forest models were compared to traditional logistic regression analyses. **Results:** Random forests correctly identified 78% of participants as remitters or non-remitters at post-treatment. The accuracy of prediction was lower in subsequent follow-ups (68%, 66% and 61% correctly classified at 3-, 12- and 24-month follow-ups, respectively). Depressive symptoms, treatment credibility, working alliance, and initial severity of BDD were among the most important predictors at the beginning of treatment. By contrast, the logistic regression models did not identify consistent and strong predictors of remission from BDD. **Conclusions:** The results provide initial support for the clinical utility of machine learning approaches in the prediction of outcomes of patients with BDD. **Trial registration:** ClinicalTrials.gov ID: NCT02010619.

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