Unsupervised clustering
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Optimal integration of transcriptomics data and associated spatial information is essential towards fully exploiting spatial transcriptomics to dissect tissue heterogeneity and map out intercellular communications.
15p vicwell 29-02-2024 5 2 Download
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Ebook "Symbolic data analysis and the SODAS software" includes content: Improved generation of symbolic objects from relational databases, exporting symbolic objects to databases, a statistical metadata model for symbolic objects, editing symbolic data, the normal symbolic form, unsupervised divisive classification, hierarchical and pyramidal clustering, clustering methods in symbolic data analysis, visualizing symbolic data by kohonen maps
478p haojiubujain07 20-09-2023 3 3 Download
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Trong bài viết, dữ liệu được thu thập gồm 306 khách hàng đã từng sử dụng dịch vụ đặt xe công nghệ. Bộ dữ liệu này được phân tích thông qua các phương pháp phân cụm (clustering methods) trong thống kê và học máy không giám sát (unsupervised learning). Các thuật toán đó còn được gọi là K-means và Elbow.
11p viintuit 06-09-2023 9 5 Download
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When studying any specific rare disease, heterogeneity and scarcity of affected individuals has historically hindered investigators from discerning on what to focus to understand and diagnose a disease. New nongenomic methodologies must be developed that identify similarities in seemingly dissimilar conditions.
9p vighostrider 25-05-2023 2 2 Download
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Lecture Introduction to Machine learning and Data mining: Lesson 3. This lesson provides students with content about: unsupervised learning; clustering; basic learning problems; partition-based clustering; evaluation of clustering quality;... Please refer to the detailed content of the lecture!
30p hanlamcoman 26-11-2022 16 5 Download
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Bài giảng Nhập môn Học máy và Khai phá dữ liệu - Chương 4+5: Phân cụm. Chương này cung cấp cho học viên những nội dung về: bài toán học có giám sát (Supervised learning) và bài toán học không giám sát (Unsupervised learning); giải thuật phân cụm; đánh giá chất lượng phân cụm (Clustering quality);... Mời các bạn cùng tham khảo chi tiết nội dung bài giảng!
32p duonghoanglacnhi 07-11-2022 14 7 Download
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With the expanding applications of mass cytometry in medical research, a wide variety of clustering methods, both semi-supervised and unsupervised, have been developed for data analysis. Selecting the optimal clustering method can accelerate the identification of meaningful cell populations.
18p vielonmusk 30-01-2022 43 0 Download
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The ability to discover new cell phenotypes by unsupervised clustering of single-cell transcriptomes has revolutionized biology. Currently, there is no principled way to decide whether a cluster of cells contains meaningful subpopulations that should be further resolved.
24p viarchimedes 26-01-2022 12 0 Download
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The clinical outcome of Philadelphia chromosome-negative B cell acute lymphoblastic leukemia (Phneg B-ALL) varies considerably from one person to another after clinical treatment due to lack of targeted therapies and leukemia’s heterogeneity.
16p vielonmusk 21-01-2022 7 0 Download
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Single cell transcriptomics is critical for understanding cellular heterogeneity and identification of novel cell types. Leveraging the recent advances in single cell RNA sequencing (scRNA-Seq) technology requires novel unsupervised clustering algorithms that are robust to high levels of technical and biological noise and scale to datasets of millions of cells.
15p vitzuyu2711 29-09-2021 6 1 Download
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The rapid development of Next-Generation Sequencing technologies enables sequencing genomes with low cost. The dramatically increasing amount of sequencing data raised crucial needs for efficient compression algorithms.
9p vijeeni2711 24-07-2021 15 0 Download
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The small number of samples and the curse of dimensionality hamper the better application of deep learning techniques for disease classification. Additionally, the performance of clustering-based feature selection algorithms is still far from being satisfactory due to their limitation in using unsupervised learning methods.
17p vijeeni2711 30-06-2021 12 1 Download
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Unsupervised segmentation of multi-spectral images plays an important role in annotating infrared microscopic images and is an essential step in label-free spectral histopathology. In this context, diverse clustering approaches have been utilized and evaluated in order to achieve segmentations of Fourier Transform Infrared (FT-IR) microscopic images that agree with histopathological characterization.
11p viwyoming2711 16-12-2020 8 0 Download
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High-throughput molecular profiling data has been used to improve clinical decision making by stratifying subjects based on their molecular profiles. Unsupervised clustering algorithms can be used for stratification purposes.
9p vikentucky2711 26-11-2020 8 1 Download
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In this study, clustering was performed using a bitmap representation of HIV reverse transcriptase and protease sequences, to produce an unsupervised classification of HIV sequences. The classification will aid our understanding of the interactions between mutations and drug resistance.
23p vikentucky2711 26-11-2020 7 1 Download
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Unsupervised analyses such as clustering are the essential tools required to interpret time-series expression data from microarrays. Several clustering algorithms have been developed to analyze gene expression data. Early methods such as k-means, hierarchical clustering, and self-organizing maps are popular for their simplicity.
17p vikentucky2711 24-11-2020 16 1 Download
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Metagenomics holds great promises for deepening our knowledge of key bacterial driven processes, but metagenome assembly remains problematic, typically resulting in representation biases and discarding significant amounts of non-redundant sequence information.
12p vioklahoma2711 19-11-2020 4 1 Download
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A metagenomic sample is a set of DNA fragments, randomly extracted from multiple cells in an environment, belonging to distinct, often unknown species. Unsupervised metagenomic clustering aims at partitioning a metagenomic sample into sets that approximate taxonomic units, without using reference genomes.
12p vioklahoma2711 19-11-2020 17 2 Download
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A novel pathway-based distance score enhances assessment of disease heterogeneity in gene expression
Distance based unsupervised clustering of gene expression data is commonly used to identify heterogeneity in biologic samples. However, high noise levels in gene expression data and relatively high correlation between genes are often encountered, so traditional distances such as Euclidean distance may not be effective at discriminating the biological differences between samples.
17p viflorida2711 30-10-2020 11 2 Download
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There exists a need for effective and easy-to-use software tools supporting the analysis of complex Electrocorticography (ECoG) data. Understanding how epileptic seizures develop or identifying diagnostic indicators for neurological diseases require the in-depth analysis of neural activity data from ECoG. Such data is multi-scale and is of high spatio-temporal resolution.
15p viflorida2711 30-10-2020 15 2 Download