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22 papers · ranked by Valyu relevance

01
PPubMed1 May 2017
Integrative clustering of multi-level ‘omic data based on non-negative matrix factorization algorithm Integrative clustering using NMF
Prabhakar Chalise, Brooke L. Fridley, Shyamal D Peddada
Integrative analyses of high-throughput ‘omic data, such as DNA methylation, DNA copy number alteration, mRNA and protein expression levels, have created unprecedented opportunities to understand the molecular basis of human disease. In particular, integrative analyses have been the cornerstone in the study of cancer…
95relevance
02
PPubMed16 Oct 2017
Clusternomics: Integrative context-dependent clustering for heterogeneous datasets Clusternomics: Integrative context-dependent clustering for heterogeneous datasets
Evelina Gabasova, John Reid, Lorenz Wernisch, Quaid Morris
Integrative clustering is used to identify groups of samples by jointly analysing multiple datasets describing the same set of biological samples, such as gene expression, copy number, methylation etc. Most existing algorithms for integrative clustering assume that there is a shared consistent set of clusters across…
91relevance
03
BbioRxiv17 May 2017
Clusternomics: Integrative Context-Dependent Clustering for Heterogeneous Datasets
Evelina Gabasova, John Reid, Lorenz Wernisch
Integrative clustering is used to identify groups of samples by jointly analysing multiple datasets describing the same set of biological samples, such as gene expression, copy number, methylation etc. Most existing algorithms for integrative clustering assume that there is a shared consistent set of clusters across…
91relevance
04
AarXiv2014
Integrative clustering of high-dimensional data with joint and individual clusters, with an application to the Metabric study
Kristoffer H. Hellton, Magne Thoresen
When measuring a range of different genomic, epigenomic, transcriptomic and other variables, an integrative approach to analysis can strengthen inference and give new insights. This is also the case when clustering patient samples, and several integrative cluster procedures have been proposed. Common for these…
89relevance
05
PPubMed27 Jun 2020
Multiple kernel learning for integrative consensus clustering of omic datasets
Alessandra Cabassi, Paul D W Kirk, Jinbo Xu
Thanks to technological advances, both the availability and the diversity of omic datasets have hugely increased in recent years (). These datasets provide information on multiple levels of biological systems, going from the genomic and epigenomic level, to gene and protein expression level, up to the metabolomic…
88relevance
06
PPubMed4 Jan 2019
Integrative Clustering in Mass Spectrometry Imaging for Enhanced Patient Stratification
Benjamin Balluff, Achim Buck, Marta Martin‐Lorenzo, Frédéric Dewez + 4 more
Clustering algorithms are powerful data analysis methods to reveal the inherent relation of objects in a high-dimensional feature space. Large, high-dimensional datasets are now characteristic of most biomedical research owing to the widespread use of next-generation sequencing and high-throughput mass spectrometry.…
86relevance
07
AarXiv1 Mar 2013
Sparse integrative clustering of multiple omics data sets
Ronglai Shen, Sijian Wang, Qianxing Mo
High resolution microarrays and second-generation sequencing platforms are powerful tools to investigate genome-wide alterations in DNA copy number, methylation and gene expression associated with a disease. An integrated genomic profiling approach measures multiple omics data types simultaneously in the same set of…
86relevance
08
AarXiv19 Jun 2020
Multiple kernel learning for integrative consensus clustering of omic datasets
Alessandra Cabassi, Paul Kirk
Summary: Diverse applications – particularly in tumour subtyping – have demonstrated the importance of integrative clustering techniques for combining information from multiple data sources. Cluster Of Clusters Analysis (COCA) is one such approach that has been widely applied in the context of tumour subtyping.…
85relevance
09
BbioRxiv17 Jan 2020
A hierarchical clustering and data fusion approach for disease subtype discovery
Bastian Pfeifer, Michael G. Schimek
Recent advances in multi-omics clustering methods enable a more fine-tuned separation of cancer patients into clinical relevant clusters. These advancements have the potential to provide a deeper understanding of cancer progression and may facilitate the treatment of cancer patients. Here, we present a simple…
85relevance
10
BbioRxiv7 Aug 2018
A Bayesian Two-Way Latent Structure Model for Genomic Data Integration Reveals Few Pan-Genomic Cluster Subtypes in a Breast Cancer Cohort
David M. Swanson, Tonje Lien, Helga Bergholtz, Therese Sørlie + 1 more
Unsupervised clustering is important in disease subtyping, among having other genomic applications. As genomic data has become more multifaceted, how to cluster across data sources for more precise subtyping is an ever more important area of research. Many of the methods proposed so far, including iCluster and Cluster…
85relevance
11
AarXiv2020
Kernel learning approaches for summarising and combining posterior similarity matrices
Alessandra Cabassi, Sylvia Richardson, Paul Kirk
Summary: When using Markov chain Monte Carlo (MCMC) algorithms to perform inference for Bayesian clustering models, such as mixture models, the output is typically a sample of clusterings (partitions) drawn from the posterior distribution. In practice, a key challenge is how to summarise this output. Here we build upon…
84relevance
12
BbioRxiv19 Oct 2020
Consensus clustering applied to multi-omic disease subtyping
Galadriel Brière, Élodie Darbo, Patricia Thébault, Raluca Uricaru
Facing the diversity of omic data and the difficulty of selecting one result over all those produced by several methods, consensus strategies have the potential to reconcile multiple inputs and to produce robust results. Here, we introduce ClustOmics, a generic consensus clustering tool that we use in the context of…
84relevance
13
AarXiv2019
Integrative Generalized Convex Clustering Optimization and Feature Selection for Mixed Multi-View Data
Minjie Wang, Genevera I. Allen
In mixed multi-view data, multiple sets of diverse features are measured on the same set of samples. By integrating all available data sources, we seek to discover common group structure among the samples that may be hidden in individualistic cluster analyses of a single data-view. While several techniques for such…
83relevance
14
BbioRxiv25 Feb 2019
A Novel Integrative Multiomics Method Reveals a Hypoxia-Related Subgroup of Breast Cancer with Significantly Decreased Survival
Maryam Pouryahya, Jung Hun Oh, Pedram Javanmard, James C. Mathews + 3 more
The remarkable growth of multi-platform genomic profiles has led to the multiomics data integration challenge. The effective integration of such data provides a comprehensive view of the molecular complexity of cancer tumors and can significantly improve clinical out-come predictions. In this study, we present a novel…
81relevance
15
PPubMed19 May 2014
A formal concept analysis approach to consensus clustering of multi-experiment expression data
Anna Hristoskova, Veselka Boeva, Elena Tsiporkova
Background Presently, with the increasing number and complexity of available gene expression datasets, the combination of data from multiple microarray studies addressing a similar biological question is gaining importance. The analysis and integration of multiple datasets are expected to yield more reliable and robust…
79relevance
16
AarXiv1 Sep 2012
Integrative Model-based clustering of microarray methylation and expression data
Matthías Kormáksson, James G. Booth, María E. Figueroa, Ari Melnick
> In many fields, researchers are interested in large and complex biological processes. Two important examples are gene expression and DNA methylation in genetics. One key problem is to identify aberrant patterns of these processes and discover biologically distinct groups. In this article we develop a model-based…
79relevance
17
CchemRxiv21 Oct 2024
ULaMDyn: Enhancing Excited-State Dynamics Analysis Through Streamlined Unsupervised Learning
Authors not listed
The analysis of nonadiabatic molecular dynamics (NAMD) data presents significant challenges due to its high dimensionality and complexity. To address these issues, we introduce ULaMDyn, a Python-based, open-source package designed to automate the unsupervised analysis of large datasets generated by NAMD simulations.…
69relevance
18
CchemRxiv15 Apr 2022
AIMSim: An Accessible Cheminformatics Platform for Similarity Operations on Chemicals Datasets
Himaghna Bhattacharjee, Jackson Burns, Dionisios Vlachos
The recent advances in deep learning, generative modeling, and statistical learning have ushered in a renewed interest in traditional cheminformatics tools and methods. Quantifying molecular similarity is essential in molecular generative modeling, exploratory molecular synthesis campaigns, and drug-discovery…
66relevance
19
CchemRxiv9 Aug 2021
Self-supervised Clustering of Mass Spectrometry Imaging Data Using Contrastive Learning
Hang Hu, Jyothsna Padmakumar Bindu, Julia Laskin
Mass spectrometry imaging (MSI) is widely used for the label-free molecular mapping of biological samples. The identification of co-localized molecules in MSI data is crucial to the understanding of biochemical pathways. However, complex MSI data are too large for manual annotation but too small for training deep…
65relevance
20
CchemRxiv17 Jan 2025
Unsupervised machine learning-based image recognition of raw IR spectra: Toward chemist-like chemical structural classification and beyond numerical data
Authors not listed
Recent advances in artificial intelligence have significantly improved spectral data analysis. In this study, we used unsupervised machine learning to classify chemical compounds based on infrared (IR) spectral images, without relying on prior chemical knowledge. The potential of machine learning for chemical…
62relevance
21
CchemRxiv8 Aug 2024
An Open-Source Implementation of the Scaffold Identification and Naming System (SCINS) and Example Applications
Kamen Petrov, Andreas Bender
Organizing and partitioning sets of chemical structures is of considerable practical significance e.g. in compound library analysis and the post-processing of screening hit lists. Approaches such as unsupervised clustering are computationally demanding and dataset-dependent; on the other hand, rule-based methods, such…
60relevance
22
CchemRxiv30 Jul 2025
Perspectives in Computational Mass Spectrometry: Recent Developments and Key Challenges
Authors not listed
Mass spectrometry (MS) is a cornerstone technology in modern molecular biology, powering diverse applications across proteomics, metabolomics, lipidomics, glycomics, and beyond. As the field continues to evolve, rapid advancements in instrumentation, acquisition strategies, machine learning, and scalable computing have…
60relevance

Results for Integrative clustering

Integrative clustering of multi-level ‘omic data based on non-negative matrix factorization algorithm Integrative clustering using NMF

Clusternomics: Integrative context-dependent clustering for heterogeneous datasets Clusternomics: Integrative context-dependent clustering for heterogeneous datasets

Clusternomics: Integrative Context-Dependent Clustering for Heterogeneous Datasets

Integrative clustering of high-dimensional data with joint and individual clusters, with an application to the Metabric study

Multiple kernel learning for integrative consensus clustering of omic datasets

Integrative Clustering in Mass Spectrometry Imaging for Enhanced Patient Stratification

Sparse integrative clustering of multiple omics data sets

Multiple kernel learning for integrative consensus clustering of omic datasets

A hierarchical clustering and data fusion approach for disease subtype discovery

A Bayesian Two-Way Latent Structure Model for Genomic Data Integration Reveals Few Pan-Genomic Cluster Subtypes in a Breast Cancer Cohort

Kernel learning approaches for summarising and combining posterior similarity matrices

Consensus clustering applied to multi-omic disease subtyping

Integrative Generalized Convex Clustering Optimization and Feature Selection for Mixed Multi-View Data

A Novel Integrative Multiomics Method Reveals a Hypoxia-Related Subgroup of Breast Cancer with Significantly Decreased Survival

A formal concept analysis approach to consensus clustering of multi-experiment expression data

Integrative Model-based clustering of microarray methylation and expression data

ULaMDyn: Enhancing Excited-State Dynamics Analysis Through Streamlined Unsupervised Learning

AIMSim: An Accessible Cheminformatics Platform for Similarity Operations on Chemicals Datasets

Self-supervised Clustering of Mass Spectrometry Imaging Data Using Contrastive Learning

Unsupervised machine learning-based image recognition of raw IR spectra: Toward chemist-like chemical structural classification and beyond numerical data

An Open-Source Implementation of the Scaffold Identification and Naming System (SCINS) and Example Applications

Perspectives in Computational Mass Spectrometry: Recent Developments and Key Challenges

Integrative clustering of multi-level ‘omic data based on non-negative matrix factorization algorithm Integrative clustering using NMF

Clusternomics: Integrative context-dependent clustering for heterogeneous datasets Clusternomics: Integrative context-dependent clustering for heterogeneous datasets

Clusternomics: Integrative Context-Dependent Clustering for Heterogeneous Datasets

Integrative clustering of high-dimensional data with joint and individual clusters, with an application to the Metabric study

Multiple kernel learning for integrative consensus clustering of omic datasets

Integrative Clustering in Mass Spectrometry Imaging for Enhanced Patient Stratification

Sparse integrative clustering of multiple omics data sets

Multiple kernel learning for integrative consensus clustering of omic datasets

A hierarchical clustering and data fusion approach for disease subtype discovery

A Bayesian Two-Way Latent Structure Model for Genomic Data Integration Reveals Few Pan-Genomic Cluster Subtypes in a Breast Cancer Cohort

Kernel learning approaches for summarising and combining posterior similarity matrices

Consensus clustering applied to multi-omic disease subtyping

Integrative Generalized Convex Clustering Optimization and Feature Selection for Mixed Multi-View Data

A Novel Integrative Multiomics Method Reveals a Hypoxia-Related Subgroup of Breast Cancer with Significantly Decreased Survival

A formal concept analysis approach to consensus clustering of multi-experiment expression data

Integrative Model-based clustering of microarray methylation and expression data

ULaMDyn: Enhancing Excited-State Dynamics Analysis Through Streamlined Unsupervised Learning

AIMSim: An Accessible Cheminformatics Platform for Similarity Operations on Chemicals Datasets

Self-supervised Clustering of Mass Spectrometry Imaging Data Using Contrastive Learning

Unsupervised machine learning-based image recognition of raw IR spectra: Toward chemist-like chemical structural classification and beyond numerical data

An Open-Source Implementation of the Scaffold Identification and Naming System (SCINS) and Example Applications

Perspectives in Computational Mass Spectrometry: Recent Developments and Key Challenges