Using machine learning tool in classification of breast cancer

2017 ◽  
pp. 3-8 ◽  
Author(s):  
Layla Abdel-Ilah ◽  
Hana Šahinbegović
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Learning Tool ◽  
Machine Learning Tool

scholarly journals Classification of apatite structures via topological data analysis: a framework for a ‘Materials Barcode’ representation of structure maps

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Scott Broderick ◽  
Ruhil Dongol ◽  
Tianmu Zhang ◽  
Krishna Rajan
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Crystal Chemistry ◽  
Persistent Homology ◽  
Hierarchical Classification ◽  
Topological Data Analysis ◽  
Learning Tool ◽  
Coordination Polyhedra ◽  
Machine Learning Tool ◽  
Topological Data

AbstractThis paper introduces the use of topological data analysis (TDA) as an unsupervised machine learning tool to uncover classification criteria in complex inorganic crystal chemistries. Using the apatite chemistry as a template, we track through the use of persistent homology the topological connectivity of input crystal chemistry descriptors on defining similarity between different stoichiometries of apatites. It is shown that TDA automatically identifies a hierarchical classification scheme within apatites based on the commonality of the number of discrete coordination polyhedra that constitute the structural building units common among the compounds. This information is presented in the form of a visualization scheme of a barcode of homology classifications, where the persistence of similarity between compounds is tracked. Unlike traditional perspectives of structure maps, this new “Materials Barcode” schema serves as an automated exploratory machine learning tool that can uncover structural associations from crystal chemistry databases, as well as to achieve a more nuanced insight into what defines similarity among homologous compounds.

scholarly journals Breast Cancer Type Classification Using Machine Learning

2021 ◽  
Vol 11 (2) ◽  
pp. 61
Author(s):  
Jiande Wu ◽  
Chindo Hicks
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Machine Learning ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Genomic Research ◽  
Support Vector ◽  
Cancer Type ◽  
Classification Models

Background: Breast cancer is a heterogeneous disease defined by molecular types and subtypes. Advances in genomic research have enabled use of precision medicine in clinical management of breast cancer. A critical unmet medical need is distinguishing triple negative breast cancer, the most aggressive and lethal form of breast cancer, from non-triple negative breast cancer. Here we propose use of a machine learning (ML) approach for classification of triple negative breast cancer and non-triple negative breast cancer patients using gene expression data. Methods: We performed analysis of RNA-Sequence data from 110 triple negative and 992 non-triple negative breast cancer tumor samples from The Cancer Genome Atlas to select the features (genes) used in the development and validation of the classification models. We evaluated four different classification models including Support Vector Machines, K-nearest neighbor, Naïve Bayes and Decision tree using features selected at different threshold levels to train the models for classifying the two types of breast cancer. For performance evaluation and validation, the proposed methods were applied to independent gene expression datasets. Results: Among the four ML algorithms evaluated, the Support Vector Machine algorithm was able to classify breast cancer more accurately into triple negative and non-triple negative breast cancer and had less misclassification errors than the other three algorithms evaluated. Conclusions: The prediction results show that ML algorithms are efficient and can be used for classification of breast cancer into triple negative and non-triple negative breast cancer types.

scholarly journals An open-source k-mer based machine learning tool for fast and accurate subtyping of HIV-1 genomes

PLoS ONE ◽  
2018 ◽  
Vol 13 (11) ◽  
pp. e0206409 ◽  
Author(s):  
Stephen Solis-Reyes ◽  
Mariano Avino ◽  
Art Poon ◽  
Lila Kari
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Learning Tool ◽  
Hiv 1 ◽  
Machine Learning Tool

scholarly journals Researching the Research: Applying Machine Learning Techniques to Dissertation Classification

2020 ◽  
Vol 2 (4) ◽  
Author(s):  
Suzanna Schmeelk
Keyword(s):  
Machine Learning ◽  
Full Text ◽  
Doctoral Program ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Full Time ◽  
Learning Techniques ◽  
Manual Classification ◽  
Machine Learning Tool

This research examines industry-based dissertation research in a doctoral computing program through the lens of machine learning algorithms to understand topics explored by senior and experienced full-time working professionals (EFWPs).  Our research categorizes dissertation by both their abstracts and by their full-text using the Graplab Create library from Apple’s Turi. We also compare the dissertation categorizations using IBM’s Watson Discovery deep machine learning tool.  Our research provides perspectives on the practicality of the manual classification of technical documents; and, it provides insights into the: (1) categories of academic work created by EFWPs in a Computing doctoral program, (2) viability of automated categorization versus human abstraction, and (3) differences in categorization algorithms.

Ansatz zur Zustandsbeschreibung technischer Bauteile

2021 ◽  
Vol 2021 (2) ◽  
pp. 35-38
Author(s):  
Lukas Egbert ◽  
◽  
Anton Zitnikov ◽  
Thorsten Tietjen ◽  
Klaus-Dieter Thoben
Keyword(s):  
Machine Learning ◽  
Predictive Maintenance ◽  
Learning Tool ◽  
Machine Learning Tool

Dieser Beitrag beschreibt eine Herangehensweise für eine Predictive Maintenance-Lösung, bei der die Bauteilabnutzung technischer Systeme mithilfe eines Sensorik-Toolkits erfasst und mittels eines Prognosetools überwacht wird. Die Sensorik als auch das Prognosetool müssen flexibel ausgelegt sein, damit sie zielführend an unterschiedlichen technischen Systemen einsetzbar sind. Die Zustandsbestimmung der Bauteile erfolgt dabei nicht kontinuierlich, sondern basierend auf zeitdiskreten Messungen. Anhand der aufgenommenen Daten wird über ein Prognosemodell die wahrscheinliche Restnutzungsdauer der Bauteile prognostiziert. Für die Erstellung der Prognose dient ein Machine Learning Tool, welches mit historischen Abnutzungsverläufen trainiert wird. Die Trainingsdaten werden durch statistische Versuche erfasst, in denen die Einflussgrößen und charakteristische Verläufe verschiedener Abnutzungsarten identifiziert werden. Als Grundlage für diesen Beitrag dienen Untersuchungen an einem Rolltor, die im Rahmen des „LongLife“ Projekts durchgeführt wurden, das vom Bundesministerium für Bildung und Forschung gefördert wird. Das Forschungsvorhaben ist der Fördermaßnahme „Ressourceneffiziente Kreislaufwirtschaft – Innovative Produktkreisläufe“ (ReziProK) zugeordnet und hat eine Laufzeit von 36 Monaten.

Bringing machine learning closer to non-experts: proposal of a user-friendly machine learning tool in the healthcare domain

2021 ◽  
Author(s):  
Andrea Vázquez-Ingelmo ◽  
Julia Alonso-Sánchez ◽  
Alicia García-Holgado ◽  
Francisco José García Peñalvo ◽  
Jesús Sampedro-Gómez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Tool ◽  
User Friendly ◽  
Machine Learning Tool
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