Results 81 to 90 of about 1,547,567 (330)
Tumor mutational burden as a determinant of metastatic dissemination patterns
Molecular Oncology, EarlyView.This study performed a comprehensive analysis of genomic data to elucidate whether metastasis in certain organs share genetic characteristics regardless of cancer type. No robust mutational patterns were identified across different metastatic locations and cancer types.
Eduardo Candeal +4 more
wiley +1 more source
Efficient Optimization of Performance Measures by Classifier Adaptation [PDF]
, 2012 In practical applications, machine learning algorithms are often needed to learn classifiers that optimize domain specific performance measures. Previously, the research has focused on learning the needed classifier in isolation, yet learning nonlinear ...
Li, Nan, Tsang, Ivor W., Zhou, Zhi-Hua
core +1 more source
Italian National Conference on Sensors, 2020 Efficient fault diagnosis of electrical and mechanical anomalies in induction motors (IMs) is challenging but necessary to ensure safety and economical operation in industries.
Rafia Nishat Toma +2 more
semanticscholar +1 more source
The pneumonia severity index: assessment and comparison to popular machine learning classifiers
medRxiv, 2021 Pneumonia is the top communicable cause of death worldwide. Accurate prognostication of patient severity with Community Acquired Pneumonia (CAP) allows better patient care and hospital management.
Dawei Wang, Deanna Willis, Y. Yih
semanticscholar +1 more source
Subtype‐specific enhancer RNAs define transcriptional regulators and prognosis in breast cancers
Molecular Oncology, EarlyView.This study employed machine learning methodologies to perform the subtype‐specific classification of RNA‐seq data sets, which are mapped on enhancers from TCGA‐derived breast cancer patients. Their integration with gene expression (referred to as ProxCReAM eRNAs) and chromatin accessibility profiles has the potential to identify lineage‐specific and ...
Aamena Y. Patel +6 more
wiley +1 more source
Detecting and Isolating Adversarial Attacks Using Characteristics of the Surrogate Model Framework
Applied Sciences, 2023 The paper introduces a novel framework for detecting adversarial attacks on machine learning models that classify tabular data. Its purpose is to provide a robust method for the monitoring and continuous auditing of machine learning models for the ...
Piotr Biczyk, Łukasz Wawrowski
doaj +1 more source
Solving the Conjugacy Decision Problem via Machine Learning
, 2018 Machine learning and pattern recognition techniques have been successfully applied to algorithmic problems in free groups. In this paper, we seek to extend these techniques to finitely presented non-free groups, with a particular emphasis on polycyclic ...
Gryak, Jonathan +2 more
core +1 more source
Using multiple classifiers for predicting the risk of endovascular aortic aneurysm repair re-intervention through hybrid feature selection. [PDF]
, 2017 Feature selection is essential in medical area; however, its process becomes complicated with the presence of censoring which is the unique character of survival analysis.
Attallah, O +7 more
core +2 more sources
Molecular Oncology, EarlyView.Aldehyde dehydrogenase 1A1 (ALDH1A1) is a cancer stem cell marker in several malignancies. We established a novel epithelial cell line from rectal adenocarcinoma with unique overexpression of this enzyme. Genetic attenuation of ALDH1A1 led to increased invasive capacity and metastatic potential, the inhibition of proliferation activity, and ultimately ...
Martina Poturnajova +25 more
wiley +1 more source
Isoelastic Agents and Wealth Updates in Machine Learning Markets [PDF]
, 2012 Recently, prediction markets have shown considerable promise for developing flexible mechanisms for machine learning. In this paper, agents with isoelastic utilities are considered. It is shown that the costs associated with homogeneous markets of agents
Geras, Krzysztof +2 more
core +2 more sources