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Cancer Subtyping via Embedded Unsupervised Learning on Transcriptomics Data
[article]
2022
arXiv
pre-print
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Specifically, we bypass the strong Gaussianity assumption that typically exists but fails in the unsupervised learning subtyping literature due to small-sized samples by vector quantization. ...
Cancer is one of the deadliest diseases worldwide. Accurate diagnosis and classification of cancer subtypes are indispensable for effective clinical treatment. ...
CONCLUSION In this study, we proposed a novel unsupervised learning framework for accurately identifying cancer subtypes. ...
arXiv:2204.02278v1
fatcat:ok7il23z7bhhpbinvwbscjzmci
Embedding of Genes Using Cancer Gene Expression Data: Biological Relevance and Potential Application on Biomarker Discovery
2019
Frontiers in Genetics
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We found that embedding can extract biologically relevant information from The Cancer Genome Atlas (TCGA) gene expression dataset by learning a vector representation through gene co-occurrence. ...
The resulting embedding matrices mined from TCGA gene expression data are interactively explorable online (http://bit.ly/tcga-embedding-cancer) and could serve as an informative reference for gene relatedness ...
FIGURE 5 | 5 Identification of potential related genes for immune checkpoint blockade therapy responsiveness. ...
doi:10.3389/fgene.2018.00682
pmid:30662451
pmcid:PMC6329279
fatcat:al45ierfxvczrk77kzvfwiv4aq
Identification of topological features in renal tumor microenvironment associated with patient survival
2017
Bioinformatics
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We apply this pipeline to the only publicly available large histopathology image dataset for a cohort of 190 patients with papillary renal cell carcinoma obtained from The Cancer Genome Atlas project. ...
Results: We propose a novel bioimage informatics pipeline for automatically characterizing the topological organization of different cell patterns in the tumor microenvironment. ...
Acknowledgements The Ohio Supercomputer Center provided support for computing for this project. ...
doi:10.1093/bioinformatics/btx723
pmid:29136101
fatcat:nj2wyidqjndnfjomiwysqwbg7m
A Novel Lightweight Deep Learning-Based Histopathological Image Classification Model for IoMT
2021
Neural Processing Letters
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However, most deep learning-based image classifying models are bulk in size and are inappropriate for use in IoT based imaging devices. ...
This paper presents a novel lightweight deep learning-based model "ReducedFireNet", for auto-classification of histopathological images. ...
In recent times authors in [15] presented an extreme learning machine (ELM) model for the prognosis of breast cancer. ...
doi:10.1007/s11063-021-10555-1
pmid:34121912
pmcid:PMC8185315
fatcat:ki6ra3xjznbq5cg4bw6p6wsuue
Spectral-spatial feature-based neural network method for acute lymphoblastic leukemia cell identification via microscopic hyperspectral imaging technology
2017
Biomedical Optics Express
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A marker-based learning vector quantization (MLVQ) neural network is proposed to perform identification with the integrated features. ...
Normalization and encoding method is applied for spectral feature extraction and the support vector machine-recursive feature elimination (SVM-RFE) algorithm is presented for spatial feature determination ...
In view of the large scale of hyperspectral blood images, the learning vector quantization (LVQ) classifier performs better with fewer parameters and a simpler structure. ...
doi:10.1364/boe.8.003017
pmid:28663923
pmcid:PMC5480446
fatcat:7jo2zbfxj5gzhbfuxeuurhepxu
Identification of sarcomatoid differentiation in renal cell carcinoma by machine learning on multiparametric MRI
2021
Scientific Reports
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We utilized unsupervised self-organizing map (SOM) and supervised Learning Vector Quantizer (LVQ) machine learning to classify RCC tumors on T2-weighted, non-contrast T1-weighted fat-saturated, contrast-enhanced ...
We have demonstrated a combined SOM-LVQ machine learning approach that is suitable for analysis of limited mpMRI datasets for the task of differential diagnosis. ...
Learning vector quantization classifier (LVQ). ...
doi:10.1038/s41598-021-83271-4
pmid:33589715
pmcid:PMC7884398
fatcat:ih3es2wrmbekjoqcp6xde7tjty
A Novel Approach for Classifying Gene Expression Data using Topic Modeling
2017
Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics - ACM-BCB '17
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Overall, our results project LDA as a promising approach for classification of tissue types based on gene expression data in cancer studies. ...
LDA has been recently applied for clustering and exploring genomic data but not for classification and prediction. ...
and Hierarchical Clustering for unsupervised learning. ...
doi:10.1145/3107411.3107483
dblp:conf/bcb/KhoYRS17
fatcat:yrfs56fdtfccpejpnkfqmbayga
Classification of hematologic malignancies using texton signatures
2007
Pattern Analysis and Applications
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We propose to use support vector machines on the texton histogram based cell representation and achieve major improvement over the commonly used classification methods in texture research. ...
Using a few cell images from each class, the basic texture elements (textons) for the nuclei and cytoplasm are learned, and the cells are represented through texton histograms. ...
The clustering and quantization steps take much longer time using the later methods, e.g., one hour for M8 vs. eight hours for LM. ...
doi:10.1007/s10044-007-0066-x
pmid:19890460
pmcid:PMC2772183
fatcat:k3j4r4zj55aorjbgpu2oe6xfoe
Non-negative Subspace Feature Representation for Few-shot Learning in Medical Imaging
[article]
2024
arXiv
pre-print
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In this paper, we investigate the effectiveness of data-based few-shot learning in medical imaging by exploring different data attribute representations in a low-dimensional space. ...
., the collaborative representation-based dimensionality reduction technique derived from eigenvectors. ...
The insight into SCNMFS formulation resembles vector quantizing since it forces data with the same label to have the same latent representations [29] . ...
arXiv:2404.02656v2
fatcat:qk44dnhymnhlhjvymasdqvxhse
DeepFeature: feature selection in nonimage data using convolutional neural network
2021
Briefings in Bioinformatics
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In comparative tests for cancer type prediction task, DeepFeature simultaneously achieved superior predictive performance and better ability to discover key pathways and biological processes meaningful ...
for this context. ...
For the 1914 gene set, between 405 and 1571 genes per cancer subtype were found. ...
doi:10.1093/bib/bbab297
pmid:34368836
pmcid:PMC8575039
fatcat:l5m6wu5q5bawrbi2nmvxuqhbje
Binary tree-structured vector quantization approach to clustering and visualizing microarray data
2002
Bioinformatics
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Results: Starting with a systematic comparison of the underlying theories behind clustering approaches, we have devised a technique that combines tree-structured vector quantization and partitive k-means ...
Most of the current systems for microarray data analysis use statistical methods, hierarchical clustering, self-organizing maps, support vector machines, or k-means clustering to organize genes or experiments ...
The authors are indebted to Francis Shepherd from the PMH thoracic oncology group and Andrea Jurisicova for helpful comments on the earlier draft of this manuscript. ...
doi:10.1093/bioinformatics/18.suppl_1.s111
pmid:12169538
fatcat:xbkboioj35g7xelavyzqk67nxu
Radiomics in precision medicine for lung cancer
2017
Translational Lung Cancer Research
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This review provides an overview of the radiomics application and its methodology for radiation oncology studies in lung cancer. ...
Radiomics, which is a recent field of research that aims to provide a more quantitative representation of imaging information relating tumor phenotypes to clinical and genotypic endpoints by embedding ...
Lung cancers are classified according to molecular subtypes, predicated on particular genetic alterations that drive and maintain lung tumorigenesis (3) . ...
doi:10.21037/tlcr.2017.09.07
pmid:29218267
pmcid:PMC5709132
fatcat:l5mmagnafjhazjig6hvemnsyoa
AIIMDs: An Integrated Framework of Automatic Idiopathic Inflammatory Myopathy Diagnosis for Muscle
2018
IEEE journal of biomedical and health informatics
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comparative study; and (4) Majority voting based classification to provide decision support for computer aided clinical diagnosis. ...
In order to address these problems, we propose the first complete framework of automatic IIM diagnosis system (AIIMDs) for the management and interpretation of digitized skeletal muscle histopathology ...
PQ-based Search PQ is a type of vector quantization technique that maps feature vectors into short codes for fast similarity search and retrieval [41] . ...
doi:10.1109/jbhi.2017.2694344
pmid:28422672
pmcid:PMC5640501
fatcat:gykrz56dpbcl5igbu5naf3pcbi
A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis
2024
Bioengineering
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Artificial intelligence (AI) techniques, spanning from machine learning and deep learning to large model paradigms, stand poised to significantly augment physicians in rendering more evidence-based decisions ...
, thus presenting a pioneering solution for clinical practice. ...
[58] proposed a comprehensive deep-learning framework for classifying molecular subtypes of breast cancer. The framework utilized copy number alteration and gene expression data from the METABRIC. ...
doi:10.3390/bioengineering11030219
pmid:38534493
pmcid:PMC10967767
fatcat:tdrqch5tinhhbmnk7bkf4ilsv4
Machine Learning and Deep Learning Applications in Multiple Myeloma Diagnosis, Prognosis, and Treatment Selection
2022
Cancers
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Machine learning- and deep learning-based studies are expected to be among the future strategies to challenge this negative-prognosis tumour via the detection of new markers for their prompt discovery ...
Artificial intelligence has recently modified the panorama of oncology investigation thanks to the use of machine learning algorithms and deep learning strategies. ...
However, although ML classifiers demonstrated an acceptable efficacy in discriminating MM bone lesions from those of cancer metastasis, their performance in separating MM from other metastasis subtypes ...
doi:10.3390/cancers14030606
pmid:35158874
pmcid:PMC8833500
fatcat:7hrerpowiffxlchep4vbb2h2si
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