Niloofar Arshadi
Ontario Institute for Cancer Research
9 Papers
53 Citations
Niloofar Arshadi is an academic researcher from Ontario Institute for Cancer Research. The author has contributed to research in topics: Cluster analysis & Feature selection. The author has an hindex of 7, co-authored 9 publications. Previous affiliations of Niloofar Arshadi include University of Toronto.
Chat about Author
Papers
WaveCNV: allele-specific copy number alterations in primary tumors and xenograft models from next-generation sequencing.
Carson Holt,Bojan Losic,Deepa Pai,Zhen Zhao,Quang M. Trinh,Sujata Syam,Niloofar Arshadi,Gun Ho Jang,Johar Ali,Tim Beck,John Douglas Mcpherson,Lakshmi Muthuswamy +11 more
TL;DR: WaveCNV, a software package to identify copy number alterations by detecting breakpoints of CNVs using translation-invariant discrete wavelet transforms and assign digitized copy numbers to each event using next-generation sequencing data, is developed.
24
•Proceedings Article
Feature Selection for Improving Case-Based Classifiers on High-Dimensional Data Sets.
Niloofar Arshadi,Igor Jurisica +1 more
- 01 Jan 2005
TL;DR: It is shown that using logistic regression as a filter FS method outperforms other FS techniques, such as Fisher and t-test, which have been widely used in analyzing biological data sets.
Predictive modeling in case-control single-nucleotide polymorphism studies in the presence of population stratification: a case study using Genetic Analysis Workshop 16 Problem 1 dataset.
TL;DR: This paper applies the gradient-boosting machine predictive model to the rheumatoid arthritis data for predicting the case-control status, and clusters the subjects on the axes of genetic variations, and builds a predictive model separately in each cluster to address population stratification.
An ensemble of case-based classifiers for high-dimensional biological domains
Niloofar Arshadi,Igor Jurisica +1 more
- 23 Aug 2005
TL;DR: The mixture of experts for case-based reasoning (MOE4CBR), where clustering techniques are applied to cluster the case-base into k groups, and each cluster is used as a case- base for the authors' k CBR classifiers, improves the classification accuracy of TA3 and is evaluated on two publicly available data sets on mass-to-charge intensities.
7
Ensembles of case-based reasoning classifiers in high-dimensional biological domains
Niloofar Arshadi,Igor Jurisica +1 more
TL;DR: An ensemble for case‐based reasoning (E4CBR) approach where an ensemble of CBR classifiers is combined with clustering and feature selection, which demonstrates that the aggregation method outperforms the existing CBR aggregation methods.
3