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Predicting Protein-Protein Interactions from Multimodal Biological Data Sources via Nonnegative Matrix Tri-Factorization
[chapter]
2012
Lecture Notes in Computer Science
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perspective of view -sparse matrix completion, and propose a novel Nonnegative Matrix Tri-Factorization (NMTF) based matrix completion approach to predict new protein interactions from existing protein ...
Through using manifold regularization, we further develop our method to integrate different biological data sources, such as protein sequences, gene expressions, protein structure information, etc. ...
In this paper, we propose a novel Nonnegative Matrix Tri-Factorization (NMTF) [16, 11] based matrix completion approach to predict protein-protein interactions. ...
doi:10.1007/978-3-642-29627-7_33
fatcat:a6jyptgflrhafmla7cnrlepcda
Predicting Protein–Protein Interactions from Multimodal Biological Data Sources via Nonnegative Matrix Tri-Factorization
2013
Journal of Computational Biology
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perspective of view -sparse matrix completion, and propose a novel Nonnegative Matrix Tri-Factorization (NMTF) based matrix completion approach to predict new protein interactions from existing protein ...
Through using manifold regularization, we further develop our method to integrate different biological data sources, such as protein sequences, gene expressions, protein structure information, etc. ...
In this paper, we propose a novel Nonnegative Matrix Tri-Factorization (NMTF) [16, 11] based matrix completion approach to predict protein-protein interactions. ...
doi:10.1089/cmb.2012.0273
pmid:23509857
fatcat:oslke4sttzgudfl5oquym4ueja
RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach
2017
BMC Bioinformatics
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We apply multimodal deep learning to integrate different sources of data to predict RNA-protein binding sites on RNAs. ...
It first extracts different representations of different sources of data from CLIP-seq data, which are subsequently integrated using multimodal deep learning to predict RNA-protein binding sites. ...
hidden modules from non-overlapping features for RNA-protein interactions. ...
doi:10.1186/s12859-017-1561-8
pmid:28245811
pmcid:PMC5331642
fatcat:rkvkv7jbyzbf7gvzpis5rbmiou
Multiview learning for understanding functional multiomics
2020
PLoS Computational Biology
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Although it has been applied to various contexts, such as computer vision and speech recognition, multiview learning has not yet been widely applied to biological data-specifically, multiomics data. ...
Recent high-throughput techniques, such as next-generation sequencing, have generated a wide variety of multiomics datasets that enable the identification of biological functions and mechanisms via multiple ...
Biological variables can be continuously or discretely measurable or categorical and may originate from various sources that render them multimodal (rather than Gaussian). ...
doi:10.1371/journal.pcbi.1007677
pmid:32240163
pmcid:PMC7117667
fatcat:jqpizdutnrgtnlymfmjhh4qo4q
RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach
[article]
2016
bioRxiv
pre-print
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Although many automatic tools have been developed to predict the RNA-protein binding sites from the rapidly growing multi-resource data, e.g. from sequence, structure data etc, their different domain specific ...
RNA plays important roles in cells through the interactions with proteins known as the RNA-binding proteins (RBP) and their binding motifs can provide crucial understanding of the post-transcriptional ...
We apply multimodal deep learning to integrate different sources of data to predict RNA-protein binding sites on RNAs. ...
doi:10.1101/085191
fatcat:n4f57quji5hzrpkxazihwmxhpm
NetQuilt: Deep Multispecies Network-based Protein Function Prediction using Homology-informed Network Similarity
[article]
2020
bioRxiv
pre-print
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In order to supply this context, network-based methods use protein-protein interaction (PPI) networks as a source of information for inferring protein function and have demonstrated promising results in ...
interaction networks. ...
The first step calculates functional similarity between proteins using a weighted sum of scores from a nonnegative matrix tri-factorization of all considered PPI networks and sequence similarity. ...
doi:10.1101/2020.07.30.227611
fatcat:hlson6rstfcnfdvu2v4zz3meii
A Detailed Catalogue of Multi-Omics Methodologies for Identification of Putative Biomarkers and Causal Molecular Networks in Translational Cancer Research
2021
International Journal of Molecular Sciences
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workflows for gaining biological knowledge from multi-omics data, and to comprehensively understand the significantly perturbed biological entities in distinct cancer types. ...
Additionally, the bioinformatics tools Multi-Omics Factor Analysis (MOFA) and netDX will be tested using omics data from public cancer resources, to assess their overall robustness, provide reproducible ...
Methods include integrative non-negative matrix factorization (iNMF) by algorithms such as Wishbone [95] or LIGER [96] , coupled nonnegative matrix factorization (coupleNMF) [97] , group factor analysis ...
doi:10.3390/ijms22062822
pmid:33802234
pmcid:PMC8000236
fatcat:rvwrq77kanh55lp2y3vrp54dqi
First proteomic analysis of the role of lysine acetylation in extensive functions in Solenopsis invicta
2020
PLoS ONE
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In the cellular component, acetylated proteins were enriched in the cytoplasmic part, mitochondrial matrix, and cytosolic ribosome. ...
Altogether, 2387 Kac sites were tested in 992 proteins. The prediction of subcellular localization indicated that most identified proteins were located in the cytoplasm, mitochondria, and nucleus. ...
Predicting protein-protein interactions from multimodal biological data sources via nonnegative matrix trifactorization. ...
doi:10.1371/journal.pone.0243787
pmid:33326466
pmcid:PMC7743978
fatcat:v2hqzygeqfeebdzepymwcouvui
Bayesian Hybrid Matrix Factorisation for Data Integration
[article]
2017
arXiv
pre-print
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We introduce a novel Bayesian hybrid matrix factorisation model (HMF) for data integration, based on combining multiple matrix factorisation methods, that can be used for in- and out-of-matrix prediction ...
We apply our method on two biological applications, and extensively compare it to state-of-the-art machine learning and matrix factorisation models. ...
Predicting drug-target interactions from
chemical and genomic kernels using Bayesian matrix
factorization. Bioinformatics, 28(18):2304-10, Sept.
2012.
M. Gönen and S. Kaski. ...
arXiv:1704.04962v1
fatcat:d6zrxn4sondbtcllmxd2k2wcxi
Deep learning applications in single-cell omics data analysis
[article]
2021
bioRxiv
pre-print
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Deep learning (DL) is a branch of machine learning (ML) capable of extracting high-level features from raw inputs in multiple stages. ...
Single-cell (SC) omics are often high-dimensional, sparse, and complex, making DL techniques ideal for analyzing and processing such data. ...
as well, resulting in additional performance gains.CNNC goes beyond previous approaches for predicting TF-gene and protein-protein interactions and predicting the pathway of a regulator-target gene pair.CNNC ...
doi:10.1101/2021.11.26.470166
fatcat:3bmpecoza5dedbmwm62jwhfm4e
A long isoform of GIV/Girdin contains a PDZ binding module that regulates localization and G-protein binding
2021
Journal of Biological Chemistry
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However, how such interaction affects protein function is difficult to predict and must be solved empirically. ...
Here we describe a long isoform of the guanine nucleotide exchange factor GIV/Girdin (CCDC88A) that we named GIV-L, which is conserved throughout evolution, from invertebrates to vertebrates, and contains ...
Annotation of protein subcellular localization was taken from the Nonnegative Matrix Factorization method used in HCM (43). ...
doi:10.1016/j.jbc.2021.100493
pmid:33675748
pmcid:PMC8042451
fatcat:24tqfhmrmjgf5ckdcariohchzi
Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry
2019
Mass spectrometry reviews (Print)
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To provide a view across the various IMS modalities, we have attempted to include examples from a range of approaches including matrix assisted laser desorption/ionization, desorption electrospray ionization ...
In this work, we review unsupervised machine learning methods for exploratory analysis of IMS data, with particular focus on (a) factorization, (b) clustering, and (c) manifold learning. ...
Source: Trindade et al. (2017), Figure 4. Reproduced with permission of Elsevier. NMF, nonnegative matrix factorization; SIMS, secondary ion mass spectrometry. ...
doi:10.1002/mas.21602
pmid:31602691
fatcat:lxna3i7wyvbn3lefbkafizdthu
Deep Learning in Spatial Transcriptomics: A Survey of Deep Learning Methods for Spatially-Resolved Transcriptomics
[article]
2022
bioRxiv
pre-print
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Having both expression profiles and tissue organization enables researchers to better understand cellular interactions and heterogeneity, providing insight into complex biological processes that would ...
The data generated by ST technologies are inherently noisy, high-dimensional, sparse, and multi-modal (including histological images, count matrices, etc.), thus requiring specialized computational tools ...
DL models learn such complicated interactions from raw data, further utilizing ST data in studying CCI. ...
doi:10.1101/2022.02.28.482392
fatcat:mw5vz673urbv5egvwbpvflitoi
27th Annual Computational Neuroscience Meeting (CNS*2018): Part One
2018
BMC Neuroscience
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Acknowledgement Neural Engineering System Design (NESD) program from the Defense Advanced Research Projects Agency (DARPA). ...
Acknowledgements We acknowledge support from MINECO/FEDER DPI2015-65833-P, TIN2014-54580-R, TIN2017-84452-R (http://www.minec o.gob.es/) and ONRG grant N62909-14-1-N279. ...
A combination of nonnegative matrix factorization (NMF) and sparse coding, NSC allows populations of neurons to collectively encode high-dimensional stimuli spaces using a compressed, sparse, and parts-based ...
doi:10.1186/s12868-018-0452-x
pmid:30373544
pmcid:PMC6205781
fatcat:xv7pgbp76zbdfksl545xof2vzy
Design of large polyphase filters in the Quadratic Residue Number System
2010
2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers
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and the nonnegative matrix factorization for factors containing amplitude basis or source gains. ...
algorithm to estimate both the sources and the mixing matrix (and thus the whole data) from the compressed measures. ...
doi:10.1109/acssc.2010.5757589
fatcat:ccxnu5owr5fyrcjcqukumerueq
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