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Full-Text Articles in Medicine and Health Sciences
Fuzzy Mathematical Models For The Analysis Of Fuzzy Systems With Application To Liver Disorders, R.W. W. Hndoosh
Fuzzy Mathematical Models For The Analysis Of Fuzzy Systems With Application To Liver Disorders, R.W. W. Hndoosh
R. W. Hndoosh
The main objective of this model is to focus on how to use the model of fuzzy system to solve fuzzy mathematics problems. Some mathematical models based on fuzzy set theory, fuzzy systems and neural network techniques seem very well suited for typical technical problems. We have proposed an extension model of a fuzzy system to N-dimension, using Mamdani's minimum implication, the minimum inference system, and the singleton fuzzifier with the center average defuzzifier. Here construct two different models namely a fuzzy inference system and an adaptive fuzzy system using neural network. We have extended the theorem for accuracy of …
Fuzzy Mathematical Model For Detection Of Lung Cancer Using A Multi-Nfclass With Confusion Fuzzy Matrix For Accuracy, R.W. W. Hndoosh
Fuzzy Mathematical Model For Detection Of Lung Cancer Using A Multi-Nfclass With Confusion Fuzzy Matrix For Accuracy, R.W. W. Hndoosh
R. W. Hndoosh
and detection of lung cancer data. This model depends on a generic model of a fuzzy perceptron, which can be used to derive a neural fuzzy system for specific domains. The multi neuron-fuzzy classification (Multi-NFClass) model proposed that uses input, hidden layers, output, and subclasses that have a multitude in each class. This model derives fuzzy rules to classify patterns into a number of crisp classes. Firstly, an attempt is made to describe fuzzy if–then rules, and construction of the fuzzy if–then rule, that are determined by the simple steps when its antecedent fuzzy sets are specified by genetic operations, …
Bayesian Joint Selection Of Genes And Pathways: Applications In Multiple Myeloma Genomics, Lin Zhang, Jeffrey S. Morris, Jiexin Zhang, Robert Orlowski, Veerabhadran Baladandayuthapani
Bayesian Joint Selection Of Genes And Pathways: Applications In Multiple Myeloma Genomics, Lin Zhang, Jeffrey S. Morris, Jiexin Zhang, Robert Orlowski, Veerabhadran Baladandayuthapani
Jeffrey S. Morris
It is well-established that the development of a disease, especially cancer, is a complex process that results from the joint effects of multiple genes involved in various molecular signaling pathways. In this article, we propose methods to discover genes and molecular pathways significantly associ- ated with clinical outcomes in cancer samples. We exploit the natural hierarchal structure of genes related to a given pathway as a group of interacting genes to conduct selection of both pathways and genes. We posit the problem in a hierarchical structured variable selection (HSVS) framework to analyze the corresponding gene expression data. HSVS methods conduct …
Causal Models And Learning From Data: Integrating Causal Modeling And Statistical Estimation, Maya Petersen, M J. Van Der Laan
Causal Models And Learning From Data: Integrating Causal Modeling And Statistical Estimation, Maya Petersen, M J. Van Der Laan
Maya Petersen
No abstract provided.
Dose Expansion Cohorts In Phase I Trials, Alexia Iasonos, John O'Quigley
Dose Expansion Cohorts In Phase I Trials, Alexia Iasonos, John O'Quigley
Alexia Iasonos
A rapidly increasing number of Phase I dose-finding studies, and in particular those based on the standard 3+3 design, frequently prolong the study and include dose expansion cohorts (DEC) with the goal to better characterize the toxicity profiles of experimental agents and to study disease specific cohorts. These trials consist of two phases: the usual dose escalation phase that aims to establish the maximum tolerated dose (MTD) and the dose expansion phase that accrues additional patients, often with different eligibility criteria, and where additional information is being collected. Current protocols typically do not specify whether the MTD will be updated …