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Relevant bibliographies by topics / Committee k-NN classification based on gene expression data

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Academic literature on the topic 'Committee k-NN classification based on gene expression data'

Author: Grafiati

Published: 4 June 2021

Last updated: 2 February 2022

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Journal articles on the topic "Committee k-NN classification based on gene expression data"

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Giacopelli, Brian, Min Wang, Ada C. Cleary, et al. "DNA Methylation-Based Classification Highlights the Role of the JAK-STAT Pathway in Acute Myeloid Leukemia." Blood 134, Supplement_1 (2019): 1413. http://dx.doi.org/10.1182/blood-2019-126212.

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Background Acute myeloid leukemia (AML) is a clinically and molecularly heterogeneous disease with poor survival. Recurrent genetic aberrations, such as chromosomal rearrangements and gene mutations, are currently used for patient classification and prognosis, and form the basis of our current understanding of pathogenic mechanisms. However, these markers incompletely predict disease behavior and outcomes. Alterations in DNA methylation patterns are a major hallmark of cancer and recent studies have demonstrated differential global DNA methylation patterns among AML patients. Here we sought to

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Yang, C. S., K. C. Wu, C. H. Yang, and L. Y. Chuang. "Correlation-based Gene Selection and Classification Using Taguchi-BPSO." Methods of Information in Medicine 49, no. 03 (2010): 254–68. http://dx.doi.org/10.3414/me09-01-0010.

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Summary Background: Microarray data with reference to gene expression profiles have provided some valuable results related to a variety of problems, and contributed to advances in clinical medicine. Microarray data characteristically have a high dimension and small sample size, which makes it difficult for a general classification method to obtain correct data for classification. However, not every gene is potentially relevant for distinguishing the sample class. Thus, in order to analyze gene expression profiles correctly, feature (gene) selection is crucial for the classification process, an

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Pineda, Estela, Anna Esteve-Codina, Maria Martinez-Garcia, et al. "Glioblastoma gene expression subtypes and correlation with clinical, molecular and immunohistochemical characteristics in a homogenously treated cohort: GLIOCAT project." Journal of Clinical Oncology 37, no. 15_suppl (2019): 2029. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.2029.

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2029 Background: Glioblastoma (GBM) gene expression subtypes have been described in last years, data in homogeneously treated patients is lacking. Methods: Clinical, molecular and immunohistochemistry (IHC) analysis from patients with newly diagnosed GBM homogeneously treated with standard radiochemotherapy were studied. Samples were classified based on the expression profiles into three different subtypes (classical, mesenchymal, proneural) using Support Vector Machine (SVM), the K-nearest neighbor (K-NN) and the single sample Gene Set Enrichment Analysis (ssGSEA) classification algorithms pr

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Saligan, Leorey N., Juan Luis Fernández-Martínez, Enrique J. deAndrés-Galiana, and Stephen Sonis. "Supervised Classification by Filter Methods and Recursive Feature Elimination Predicts Risk of Radiotherapy-Related Fatigue in Patients with Prostate Cancer." Cancer Informatics 13 (January 2014): CIN.S19745. http://dx.doi.org/10.4137/cin.s19745.

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Background Fatigue is a common side effect of cancer (CA) treatment. We used a novel analytical method to identify and validate a specific gene cluster that is predictive of fatigue risk in prostate cancer patients (PCP) treated with radiotherapy (RT). Methods A total of 44 PCP were categorized into high-fatigue (HF) and low-fatigue (LF) cohorts based on fatigue score change from baseline to RT completion. Fold-change differential and Fisher's linear discriminant analyses (LDA) from 27 subjects with gene expression data at baseline and RT completion generated a reduced base of most discriminat

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Amin, Samir B., Stephane Minvielle, Bret Hanlon, et al. "Gene Expression Profile Alone Is Inadequate In Predicting Complete Responses In Multiple Myeloma." Blood 116, no. 21 (2010): 306. http://dx.doi.org/10.1182/blood.v116.21.306.306.

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Abstract Abstract 306 Current therapy for multiple myeloma (MM) remains empiric. With advancement in the understanding of its molecular basis, newer therapies are emerging faster than ever with increasing the difficulty in the selection of treatment regime to maximize response and minimize the rising cost of therapy. In recent years, treatment response prediction using gene expression profiling is being evaluated to identify expression signature that can classify patients likely to benefit from chemotherapy, e.g., there are several multi-gene expression assays available to predict treatment re

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Stein, Caleb K., Faith E. Davies, Christoph Heuck, et al. "Molecular Subtyping and Risk Stratification for the Classification of Myeloma." Blood 126, no. 23 (2015): 4173. http://dx.doi.org/10.1182/blood.v126.23.4173.4173.

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Abstract Introduction Over the last 15 years gene expression profiling (GEP) has been used to define myeloma molecular subgroups and to determine clinical prognosis. Two major molecular subgroup classifications have been used: the UAMS which determines 7 subgroups and the TC classification based on the presence of IgH translocations and expression of D group cyclins. For prognosis, although a number of different GEP signatures have been defined, the widely used GEP70 identifies 15% of patients with high risk (HR) disease who have a median PFS and OS of 1.75 and 2.83 years. An ideal classificat

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Padella, Antonella, Giovanni Marconi, Giorgia Simonetti, et al. "Higher Expression of PALB2 Predict Poor Prognosis in AML Patients and Identifies Potential Targets of Synthetic Lethal Therapies." Blood 132, Supplement 1 (2018): 1507. http://dx.doi.org/10.1182/blood-2018-99-118210.

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Abstract Background - Partner and localizer of BRCA2 (PALB2) plays a key role in the DNA damage repair (DDR). Genomic alterations of DDR genes rarely occur in AML, while their deregulation at transcriptional level is a known mechanism exploited by leukemic cells in order to sustain the high genetic instability and to continue proliferation. Aim - We aimed to characterize the role of PALB2 in AML by investigating its expression levels and its prognostic value, in order to evaluate its potential as target of therapies based on a synthetic lethality approaches. Methods - Gene expression profiling

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Kim, Bong-Hyun, Kijin Yu, and Peter C. W. Lee. "Cancer classification of single-cell gene expression data by neural network." Bioinformatics, October 11, 2019. http://dx.doi.org/10.1093/bioinformatics/btz772.

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Abstract Motivation Cancer classification based on gene expression profiles has provided insight on the causes of cancer and cancer treatment. Recently, machine learning-based approaches have been attempted in downstream cancer analysis to address the large differences in gene expression values, as determined by single-cell RNA sequencing (scRNA-seq). Results We designed cancer classifiers that can identify 21 types of cancers and normal tissues based on bulk RNA-seq as well as scRNA-seq data. Training was performed with 7398 cancer samples and 640 normal samples from 21 tumors and normal tiss

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"Gene based Disease Prediction using Pattern Similarity based Classification." International Journal of Innovative Technology and Exploring Engineering 8, no. 11 (2019): 3223–27. http://dx.doi.org/10.35940/ijitee.k2524.0981119.

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In today’s biology research in a single experiment scientist can simultaneously measure the expression of levels of thousands of genes. The molecular level of the cell is represented in gene expression profile. And it helps for medical diagnosis tools. For addressing the fundamental harms which helps to diagnosis and discovery gene expression data along with diseases classification is included. Monitoring of large number of gene expressions is possible because of this DNAmicroarray technique. Using this large quantity of gene data, experts are trying to find the probability of disease classifi

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Dissertations / Theses on the topic "Committee k-NN classification based on gene expression data"

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Dhawan, Manik. "Application of Committee k-NN Classifiers for Gene Expression Profile Classification." University of Akron / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=akron1227547457.

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