A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Filters
Data integration by fuzzy similarity-based hierarchical clustering
2020
BMC Bioinformatics
Preserved Fulltext
From the analysis of scientific literature, it appears to be the first time that a model based on fuzzy logic is used for the agglomeration of multi-omic data. ...
For each view, a dendrogram is obtained by using a hierarchical clustering based on a fuzzy equivalence relation with Łukasiewicz valued fuzzy similarity. ...
SNF creates a fused network of patients using a metric fusion technique and then partitions the data using spectral clustering. ...
doi:10.1186/s12859-020-03567-6
pmid:32838739
fatcat:2jfjqse6vfdlfaxhcfevb2kkcq
MultiGATAE: A Novel Cancer Subtype Identification Method Based on Multi-Omics and Attention Mechanism
2022
Frontiers in Genetics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
We first used similarity network fusion to integrate multi-omics data to construct a similarity graph. ...
The K-means clustering method is applied to the embedding representation to identify cancer subtypes. ...
HL and JL analyzed the data. GZ and ZP wrote the manuscript. CY and JW supervised the whole study process and revised the manuscript. ...
doi:10.3389/fgene.2022.855629
pmid:35391797
pmcid:PMC8979770
fatcat:v5cs2wle6zcxxox6aewnnni2ku
Table of Contents
2021
2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
. . . . . . . . . . . . . . . . . . . 230 Farhan Tanvir, Muhammad Ifte Khairul Islam and Esra Akbas Multi-distance based spectral embedding fusion for clustering single-cell methylation data 238 Qi Tian ...
Nobile Drug-target affinity prediction using applicability domain based on data density . . . . . . . . . . 224 Shunya Sugita and Masahito Ohue Predicting Drug-Drug Interactions Using Meta-path Based Similarities ...
doi:10.1109/cibcb49929.2021.9562932
fatcat:ntr23qdkxva25g3ogqb6jin25i
Multiview learning for understanding functional multiomics
2020
PLoS Computational Biology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Secondly, we explore possible applications to different biological systems, including human diseases (e.g., brain disorders and cancers), plants, and single-cell analysis, and discuss both the benefits ...
In particular, multiview learning is more effective than previous integrative methods for learning data's heterogeneity and revealing cross-talk patterns. ...
The co-regularization term can be correlation-based, in which the 2 embeddings f (i) (x (i) ),f (j) (x (j) ) are maximally correlated, or distance-based, in which the Euclidean distance between the 2 embeddings ...
doi:10.1371/journal.pcbi.1007677
pmid:32240163
pmcid:PMC7117667
fatcat:jqpizdutnrgtnlymfmjhh4qo4q
Dissecting Cellular Heterogeneity Based on Network Denoising of scRNA-seq Using Local Scaling Self-Diffusion
2022
Frontiers in Genetics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is text/html.
Comparing with other single-cell clustering methods, our approach demonstrates much better clustering performance, and cell types identified on colorectal tumors reveal strongly biological interpretability ...
Local scaling infers the self-tuning of cell-to-cell distances that are used to construct cell affinity. ...
HGC: Fast Hierarchical Clustering for Large-Scale Single-Cell Data. ...
doi:10.3389/fgene.2021.811043
pmid:35082838
pmcid:PMC8784844
fatcat:5bjpids4kjezbpzwqkamiecfcu
Unsupervised Multi-Omics Data Integration Methods: A Comprehensive Review
2022
Frontiers in Genetics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
We also briefly review feature selection methods, multi-Omics data sets, and resources/tools that constitute critical components for carrying out the integration. ...
This review aims to provide an overview of multi-Omics data integration methods with different statistical approaches, focusing on unsupervised learning tasks, including disease onset prediction, biomarker ...
Embedding) subtyping com/GaoLabXDU/MSNE) • RWRF (Random Walk with Restart ModE Wen et al. (2021) MiE, DM, GE Disease- • R code (https://github.com/ for multi-dimensional data Fusion) subtyping Sepstar/ ...
doi:10.3389/fgene.2022.854752
pmid:35391796
pmcid:PMC8981526
fatcat:ijmwfu264rbgtaen66tkbr4sgu
Machine Learning and Integrative Analysis of Biomedical Big Data
2019
Genes
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing ...
Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. ...
In multi-omics studies, imputation based on k-nearest neighbors for profiles and genes expression [76] , autocorrelation with cubic interpolation for spectral analysis of time series molecular data [ ...
doi:10.3390/genes10020087
pmid:30696086
pmcid:PMC6410075
fatcat:vopnjgke4fculmr7t3n43ewfiy
Integrative clustering methods of multi-omics data for molecule-based cancer classifications
2016
Quantitative Biology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
Integrative clustering of large-scale multi-omics data is an important way for molecule-based cancer classification. ...
The data heterogeneity and the complexity of inter-omics variations are two major challenges for the integrative clustering analysis. ...
A method called SNF (similarity network fusion) first built a patients' similarity network for each dataset, based on the 'distance' of each pair of patients, and then it used a message passing algorithm ...
doi:10.1007/s40484-016-0063-4
fatcat:jz5lz5er5bbuhetj75vr6xbwwa
SADLN: Self-attention based deep learning network of integrating multi-omics data for cancer subtype recognition
2023
Frontiers in Genetics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2023; you can also visit the original URL.
The file type is application/pdf.
The Self-Attention Based Deep Learning Network (SADLN) is an effective method of integrating multi-omics data for cancer subtype recognition. ...
To tackle these problems, we proposed SADLN: A self-attention based deep learning network of integrating multi-omics data for cancer subtype recognition. ...
For example, K-means, LRAcluster, and Spectral clustering all belong to this category. ...
doi:10.3389/fgene.2022.1032768
pmid:36685873
pmcid:PMC9846505
fatcat:v4daewtzurasdmggdqf3c5b3zq
A hierarchical clustering and data fusion approach for disease subtype discovery
[article]
2020
bioRxiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Here, we present a simple hierarchical clustering and data fusion approach, named HC-fused, for the detection of disease subtypes. ...
Unlike other methods, the proposed approach naturally reports on the individual contribution of each single-omic to the data fusion process. ...
Acknowledgments
305 We are grateful to the Kurt und Senta Herrmann-Stiftung, Vaduz, Liechtenstein, for its 306 support. We also thank Luca Vitale and José Antonio Vera-Ramos for helpful discussions. ...
doi:10.1101/2020.01.16.909382
fatcat:xebnqyaltrffhmxrni3ftmzv6m
A Comprehensive Review of Artificial Intelligence Approaches in Omics Data Processing: Evaluating Progress and Challenges
2023
International Journal of Mathematics, Statistics, and Computer Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2024; you can also visit the original URL.
The file type is application/pdf.
Essential components include, for instance, clinical applications and literature collections. Other researchers have faced challenges, and the existing literature highlights them. ...
Challenges with AI, preprocessing, datasets, validation of models, and testbed applications arose when AI was used to analyze omics data. ...
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.59543/ijmscs.v2i.8703
fatcat:t3tn3jnfcffu5odpp6wdo2blai
Multi-view Subspace Clustering Analysis for Aggregating Multiple Heterogeneous Omics Data
2019
Frontiers in Genetics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
., Cancer Cell Line Encyclopedia (CCLE) data, MSCA successfully identifies cell clusters of common aberrations across cancer types. ...
The combinations of molecules responsible for different phenotypes form multiple embedded (expression) subspaces, thus identifying the intrinsic data structure is challenging by regular integration methods ...
Points in two clusters may be mislabeled in a single coordinated space, i.e., Cluster 2 and Cluster 3 for data type 1, Cluster 1 and Cluster 2 for type 2. ...
doi:10.3389/fgene.2019.00744
pmid:31497031
pmcid:PMC6712585
fatcat:3npxnejrsvf6hb4f3jxwx2slyy
Integrative subspace clustering by common and specific decomposition for applications on cancer subtype identification
2019
BMC Medical Genomics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Most existing clustering methods by multi-omics data assume strong consistency among different sources of datasets, and thus may lose efficacy when the consistency is relatively weak. ...
Computational analysis of the multi-omics datasets could potentially reveal deep insights for a given disease. ...
The coreg method extends the single view spectral clustering method by adding a co-regularization term which forces the low embeddings from multiple views to be close. • Similarity network fusion (SNF) ...
doi:10.1186/s12920-019-0633-1
pmid:31874642
pmcid:PMC6929329
fatcat:qhifwgs4jjhfhe6i7pasdap7h4
Navigating the Multiverse: A Hitchhiker's Guide to Selecting Harmonisation Methods for Multimodal Biomedical Data
[article]
2024
medRxiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2024; you can also visit the original URL.
The file type is application/pdf.
Conclusions: This review provides a thorough taxonomy of methods for harmonising multimodal data and introduces a foundational 10-step guide for newcomers to implement a multimodal workflow. ...
This comprehensive taxonomy would furnish a clear guidance and aid in informed decision-making within the progressively intricate realm of biomedical and clinical data analysis, and is imperative for advancing ...
We thank Tyrone Chen for his suggestions regarding concepts of the work done.
Ethics approval and consent to participate: 'Not applicable' Consent for publication: 'Not applicable' ...
doi:10.1101/2024.03.21.24304655
fatcat:v5gdsu6znbgzrjsuja4iwksr7q
A computational framework for complex disease stratification from multiple large-scale datasets
2018
BMC Systems Biology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
We present a framework to plan and generate single and multi-'omics signatures of disease states. ...
Results: We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. ...
but without a data fusion analysis, in contrast to the glioblastoma TCGA dataset, for example [55] . ...
doi:10.1186/s12918-018-0556-z
pmid:29843806
pmcid:PMC5975674
fatcat:2rojsyjblrgmdpvbnvhp7jfpia
« Previous
Showing results 1 — 15 out of 1,559 results