10 Hits in 4.1 sec

Rectified factor networks for biclustering of omics data

Djork-Arné Clevert, Thomas Unterthiner, Gundula Povysil, Sepp Hochreiter
2017 Bioinformatics  
We propose to use the recently introduced unsupervised Deep Learning approach Rectified Factor Networks (RFNs) to overcome the drawbacks of existing biclustering methods.  ...  Factor Analysis for Bicluster Acquisition (FABIA), one of the most successful biclustering methods, is a generative model that represents each bicluster by two sparse membership vectors: one for the samples  ...  Acknowledgement We thank the NVIDIA Corporation for supporting this research with several Titan X GPUs. Funding This work was was funded by the Institute of Bioinformatics.  ... 
doi:10.1093/bioinformatics/btx226 pmid:28881961 pmcid:PMC5870657 fatcat:lg673yjrs5gajmx32ftauplfca

Biclustering of Omics Data using Rectified Factor Networks

Mani Manavalan
2016 Zenodo  
To circumvent the limitations of existing biclustering approaches, we propose using the recently introduced unsupervised Deep Learning algorithm Rectified Factor Networks (RFNs).  ...  One of the most successful biclustering methods, Factor Analysis for Bicluster Acquisition (FABIA), is a generative model in which each bicluster is represented by two sparse membership vectors: one for  ...  The drawbacks of FABIA are addressed by rectified factor networks (RFNs; Clevert et al., 2015) .  ... 
doi:10.5281/zenodo.5633991 fatcat:hnxsuvi5u5a73a32c2hkygqo4q

Deep learning in bioinformatics

Wei Wang, Xin Gao
2019 Methods  
The input information flows through the network as follows: each layer receives input data for each of its neurons, each neuron then executes a simple user-defined function, and then the output of the  ...  The learning process of a neural network is the updating of these connection weights, based on prediction errors made with training data.  ...  Firstly, biclusters specific to a particular type of cancer are detected based on rectified factor networks and ranked according to their associations with general cancers.  ... 
doi:10.1016/j.ymeth.2019.06.006 pmid:31181259 fatcat:n4hy4vim6jcl5o2uvxneqnli7m

Deeper insights into long-term survival heterogeneity of Pancreatic Ductal Adenocarcinoma (PDAC) patients using integrative individual- and group-level transcriptome network analyses [article]

Archana Bhardwaj, Claire Josse, Daniel Van Daele, Christophe Poulet, Marcela Chavez, Ingrid Struman, Kristel Van Steen
2020 bioRxiv   pre-print
Interpretation: Our proposed analytic workflow shows the advantages of combining clinical and omics data as well as individual- and group-level transcriptome profiling.  ...  Furthermore, we applied two gene prioritization approaches: random walk-based Degree-Aware disease gene prioritizing (DADA) method to develop PDAC disease modules; Network-based Integration of Multi-omics  ...  NetICS= Network-based Integration of Multi-omics Data 25 ; DADA= Degree-Aware Algorithms for Network Based Disease Gene Prioritization 13 .  ... 
doi:10.1101/2020.06.01.116194 fatcat:at46sigm3vh4naqy2qinsbmo3i

PathME: pathway based multi-modal sparse autoencoders for clustering of patient-level multi-omics data

Amina Lemsara, Salima Ouadfel, Holger Fröhlich
2020 BMC Bioinformatics  
Several methods have been proposed for unsupervised clustering of multi-omics data.  ...  of omics data.  ...  Acknowledgements We thank the University of Constantine 2 for supporting AL during her stay at the University of Bonn. Authors' contributions AL implemented the code.  ... 
doi:10.1186/s12859-020-3465-2 pmid:32299344 fatcat:4bt45c7y2jgg3e6yb665he72fm

Integrative approaches for analysis of mRNA and microRNA high-throughput data

Petr V. Nazarov, Stephanie Kreis
2021 Computational and Structural Biotechnology Journal  
Advanced sequencing technologies such as RNASeq provide the means for production of massive amounts of data, including transcriptome-wide expression levels of coding RNAs (mRNAs) and non-coding RNAs such  ...  In this review, we will focus on miRNAs as key post-transcriptional regulators summarizing current computational approaches for miRNA:target gene prediction as well as new data-driven methods to tackle  ...  For instance, the rectified factor network-based biclustering for genes, miRNAs and interactions (rfnGMI), presented by Su et al.  ... 
doi:10.1016/j.csbj.2021.01.029 pmid:33680358 pmcid:PMC7895676 fatcat:cr3dllion5dedhr4hmzooge7wm

Single cell analysis of the inner ear sensory organs

Ofer Yizhar-Barnea, Karen B. Avraham
2017 International Journal of Developmental Biology  
These networks may uncover critical elements for trans-differentiation, regeneration and/or reprogramming, providing entry points for therapeutics of deafness and vestibular pathologies.  ...  Single cell analysis of the inner ear sensory organs holds the promise of providing a significant boost in building an omics network that translates into a comprehensive understanding of the mechanisms  ...  We thank Shaked Shivatzki for the immunofluorescent images.  ... 
doi:10.1387/ijdb.160453ka pmid:28621418 pmcid:PMC5709810 fatcat:un6py3ikdfgdxe2mp6itfnsheu

oCEM: Automatic detection and analysis of overlapping co-expressed gene modules

Quang-Huy Nguyen, Duc-Hau Le
2022 BMC Genomics  
Those methods are advanced undoubtedly, but the selection of a certain clustering method for sample- and gene-clustering tasks is separate, in which the latter task is often more complicated.  ...  Conclusions oCEM helped non-technical users easily perform complicated statistical analyses and then gain robust results. oCEM and its applications, along with example data, were freely provided at https  ...  In the future, we will find the way to combine -omics data, possibly including transcription factors, prior to being the input of oCEM.  ... 
doi:10.1186/s12864-021-08072-5 pmid:34998362 pmcid:PMC8742956 fatcat:gubg4xdiwrcfbmpuhi25yfafpq

Cancer Subtype Discovery Using Prognosis-Enhanced Neural Network Classifier in Multigenomic Data

Prasanna Vasudevan, Thangamani Murugesan
2018 Technology in Cancer Research and Treatment  
Conclusion: From the experimental results, the proposed prognosis-enhanced neural network classifier is seen as an alternative, which is full of promise for cancer subtype prediction in genome scale data  ...  The proposed prognosis-enhanced neural network classifier algorithm produces higher accuracy results of 89.2% for 215 samples efficiently.  ...  Similarly, Lee et al 8 utilized a biclustering technique on the correlation matrix to integrate the DNA copy number and gene expression data.  ... 
doi:10.1177/1533033818790509 pmid:30092720 pmcid:PMC6088521 fatcat:cjptqm3gdrgjzc754v3idtpyba

Deep Learning Frameworks for Multi-omics Analyses of the Microbiome in Disease Studies

Derek Reiman
Lastly, I will introduce a deep learning framework for modeling the dynamics of the microbiome community while considering multiple patient characteristics and external factors.  ...  Next, I will present an interpretable deep neural network framework integrating microbiome and metabolome data to predict the entire metabolomic profile from microbiome abundance.  ...  Yang Dai, Jie Liang, Ao Ma, Brian Layden, and Aly Khan -for their unwavering support. I would like to especially thank Dr. Yang Dai for taking me on as a student.  ... 
doi:10.25417/uic.19187285 fatcat:bfaoltxe2jfkhh3oalco4wtwve