Multiprobabilistic Venn Predictors with Logistic Regression

Autor: Usha Menon, Zhiyuan Luo, Rainer Cramer, Ali Tiss, Volodya Vovk, Mike Waterfield, Ian Jacobs, Stephane Camuzeaux, John F. Timms, Brian Burford, Dmitry Devetyarov, Ilia Nouretdinov, Celia Smith, Aleksandra Gentry-Maharaj, Alexander Gammerman, Alexey Ya. Chervonenkis, Rachel Hallett

Publikováno v: IFIP Advances in Information and Communication Technology
8th International Conference on Artificial Intelligence Applications and Innovations (AIAI)
8th International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2012, Halkidiki, Greece. pp.224-233, ⟨10.1007/978-3-642-33412-2_23⟩
IFIP Advances in Information and Communication Technology ISBN: 9783642334115
AIAI (2)

Part 4: First Conformal Prediction and Its Applications Workshop (COPA 2012); International audience; This paper describes the methodology of providing multiprobability predictions for proteomic mass spectrometry data. The methodology is based on a n

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f318ee1d5ca2fe97c458de4f017ca7ef
https://hal.archives-ouvertes.fr/hal-01523062/file/978-3-642-33412-2_23_Chapter.pdf

Highly accurate detection of ovarian cancer using CA125 but limited improvement with serum matrix-assisted laser desorption/ionization time-of-flight mass spectrometry profiling

Autor: Ali, Tiss, John F, Timms, Celia, Smith, Dmitry, Devetyarov, Aleksandra, Gentry-Maharaj, Stephane, Camuzeaux, Brian, Burford, Ilia, Nouretdinov, Jeremy, Ford, Zhiyuan, Luo, Ian, Jacobs, Usha, Menon, Alex, Gammerman, Rainer, Cramer

Publikováno v: International journal of gynecological cancer : official journal of the International Gynecological Cancer Society. 20(9)

Our objective was to test the performance of CA125 in classifying serum samples from a cohort of malignant and benign ovarian cancers and age-matched healthy controls and to assess whether combining information from matrix-assisted laser desorption/i

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=pmid________::afdd99c5a9b6f2e4f6329f9931f3012d
https://pubmed.ncbi.nlm.nih.gov/21370595

Prediction with Confidence Based on a Random Forest Classifier

Autor: Dmitry Devetyarov, Ilia Nouretdinov

Publikováno v: IFIP Advances in Information and Communication Technology ISBN: 9783642162381
AIAI

Conformal predictors represent a new flexible framework that outputs region predictions with a guaranteed error rate. Efficiency of such predictions depends on the nonconformity measure that underlies the predictor. In this work we designed new nonco

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::84441162d7ce9446b84bfbf11b71d9f1
https://doi.org/10.1007/978-3-642-16239-8_8

Machine learning analysis of proteomics data for early diagnosis

Autor: Dmitry, Devetyarov

Publikováno v: Studies in health technology and informatics. 150

Ovarian cancer (OC) can usually clinically be diagnosed only at the late stages of the disease. We show that the combination of the biomarker antigen CA125 with proteomic mass spectra data and applying newly developed machine learning algorithms, may

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=pmid________::71300b17a94859b9af55dead03663789
https://pubmed.ncbi.nlm.nih.gov/19745415

Conformal Region Classification with Instance-Transfer Boosting.

Autor: Zhou, Shuang, Smirnov, Evgueni Nikolaevich, Peeters, Ralf

Publikováno v: International Journal on Artificial Intelligence Tools; Dec2015, Vol. 24 Issue 6, p-1, 25p