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Predicting Protein-Protein Interactions from Multimodal Biological Data Sources via Nonnegative Matrix Tri-Factorization [chapter]

Hua Wang, Heng Huang, Chris Ding, Feiping Nie
2012 Lecture Notes in Computer Science  
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

Hua Wang, Heng Huang, Chris Ding, Feiping Nie
2013 Journal of Computational Biology  
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

Xiaoyong Pan, Hong-Bin Shen
2017 BMC Bioinformatics  
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

Nam D Nguyen, Daifeng Wang
2020 PLoS Computational Biology  
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]

Xiaoyong Pan, Hong-Bin Shen
2016 bioRxiv   pre-print
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]

Meet Barot, Vladimir Gligorijevic, Kyunghyun Cho, Richard Bonneau
2020 bioRxiv   pre-print
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

Efstathios Iason Vlachavas, Jonas Bohn, Frank Ückert, Sylvia Nürnberg
2021 International Journal of Molecular Sciences  
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

Jingwen Ye, Jun Li
2020 PLoS ONE  
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]

Thomas Brouwer, Pietro Lió
2017 arXiv   pre-print
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]

Nafiseh Erfanian, A. Ali Heydari, Pablo Ianez, Afshin Derakhshani, Mohammad Ghasemigol, Mohsen Farahpour, Saeed Nasseri, Hossein Safarpour, Amirhossein Sahebkar
2021 bioRxiv   pre-print
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

Jason Ear, Amer Ali Abd El-Hafeez, Suchismita Roy, Tony Ngo, Navin Rajapakse, Julie Choi, Soni Khandelwal, Majid Ghassemian, Luke McCaffrey, Irina Kufareva, Debashis Sahoo, Pradipta Ghosh
2021 Journal of Biological Chemistry  
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

Nico Verbeeck, Richard M. Caprioli, Raf Van de Plas
2019 Mass spectrometry reviews (Print)  
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]

A. Ali Heydari, Suzanne S. Sindi
2022 bioRxiv   pre-print
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  
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

Gian Carlo Cardarilli, Alberto Nannarelli, Yann Oster, Massimo Petricca, Marco Re
2010 2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers  
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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