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Integrative COVID-19 Biological Network Inference with Probabilistic Core Decomposition [article]

Yang Guo, Fatemeh Esfahani, Xiaojian Shao, Venkatesh Srinivasan, Alex Thomo, Li Xing, Xuekui Zhang
2021 bioRxiv   pre-print
Therefore, we propose a new data analysis pipeline that can efficiently compute core decomposition on the extended network and identify dense subgraphs.  ...  Together with the generated hypotheses, our results provide novel knowledge relevant to COVID-19 for further validation.  ...  In this study, we connect the Biomine network to the small PPI network and we integrate our previously proposed graph peeling algorithm [20] for probabilistic core decomposition and proposed an analysis  ... 
doi:10.1101/2021.06.23.449535 fatcat:d54an2se3nddbpltmx4pqzrltu

Single-cell multi-omics sequencing: application trends, COVID-19, data analysis issues and prospects

Lu Huo, Jiao Jiao Li, Ling Chen, Zuguo Yu, Gyorgy Hutvagner, Jinyan Li
2021 Briefings in Bioinformatics  
This survey overviews recent developments in single-cell multi-omics sequencing, and their applications to understand complex diseases in particular the COVID-19 pandemic.  ...  The survey is concluded with some open questions and opportunities for this extraordinary field.  ...  On the side of single-cell sequencing (not multi-omics sequencing) for COVID-19 research, scRNA-seq has been applied to profile PBMCs from seven patients with COVID-19 and six healthy controls to reveal  ... 
doi:10.1093/bib/bbab229 pmid:34111889 pmcid:PMC8344433 fatcat:n5wva7mjqzfsldiahwzn2cun5e

COVID-19 Modeling: A Review [article]

Longbing Cao, Qing Liu
2021 arXiv   pre-print
It constructs a research landscape of COVID-19 modeling tasks and methods, and further categorizes, summarizes, compares and discusses the related methods and progress of modeling COVID-19 epidemic transmission  ...  The SARS-CoV-2 virus and COVID-19 disease have posed unprecedented and overwhelming demand, challenges and opportunities to domain, model and data driven modeling.  ...  More information about COVID-19 modeling is in  ... 
arXiv:2104.12556v3 fatcat:pj2bketcrveafbjf2m7tx3odxy

Leveraging Structured Biological Knowledge for Counterfactual Inference: a Case Study of Viral Pathogenesis

Jeremy Zucker, Kaushal Paneri, Sara Mohammad-Taheri, Somya Bhargava, Pallavi Kolambkar, Craig Bakker, Jeremy Teuton, Charles Tapley Hoyt, Kristie Oxford, Robert Ness, Olga Vitek
2021 IEEE Transactions on Big Data  
It requires us to represent the system in form of a structural causal model, complete with a causal diagram, probabilistic assumptions on exogenous variables, and functional assignments.  ...  Counterfactual inference is a useful tool for comparing outcomes of interventions on complex systems.  ...  In accordance with the inputs to Algorithm 1, we defined the knowledge base K as the Covid-19 knowledge network automatically assembled from the Covid-19 document corpus using the INDRA workflow.  ... 
doi:10.1109/tbdata.2021.3050680 fatcat:vi2ywg6hxrhwxocppxd7t6yv6u

Handling the COVID‐19 crisis: Toward an agile model‐based systems approach

Olivier de Weck, Daniel Krob, Li Lefei, Pao Chuen Lui, Antoine Rauzy, Xinguo Zhang
2020 Systems Engineering  
Figure 8 describes how such a decision-aid system could be integrated in the high-level COVID-19 environment.  ...  modeling which were presented in the last sectionat each level of the geographic decomposition that gives the systems architecture layers of our COVID-19 decision-aid system.  ... 
doi:10.1002/sys.21557 fatcat:yyw3q5h5l5aehgz42r3c7tlole

Artificial Intelligence in Bulk and Single-Cell RNA-Sequencing Data to Foster Precision Oncology

Marco Del Giudice, Serena Peirone, Sarah Perrone, Francesca Priante, Fabiola Varese, Elisa Tirtei, Franca Fagioli, Matteo Cereda
2021 International Journal of Molecular Sciences  
The COVID-19 pandemic has opened up new possibilities for AI development.  ...  The COVID-19 pandemic has opened up new possibilities for AI development.  ... 
doi:10.3390/ijms22094563 pmid:33925407 fatcat:jr6z6b2d3jgu7e7gato2apdwaa

Data-Driven Methods to Monitor, Model, Forecast and Control Covid-19 Pandemic: Leveraging Data Science, Epidemiology and Control Theory [article]

Teodoro Alamo, D. G. Reina, Pablo Millán
2020 arXiv   pre-print
This document analyzes the role of data-driven methodologies in Covid-19 pandemic.  ...  Each step of the roadmap is detailed through a review of consolidated theoretical results and their potential application in the Covid-19 context.  ...  In [195] , the authors use a feed-forward neural network improved with an interior search algorithm to predict the number of cases of Covid-19.  ... 
arXiv:2006.01731v2 fatcat:nntq6zi4y5fkfays2qgadyio5q

A Survey on Bayesian Deep Learning [article]

Hao Wang, Dit-Yan Yeung
2021 arXiv   pre-print
For higher-level inference, however, probabilistic graphical models with their Bayesian nature are still more powerful and flexible.  ...  In recent years, Bayesian deep learning has emerged as a unified probabilistic framework to tightly integrate deep learning and Bayesian models.  ...  With the principled integration in Bayesian deep learning, the perception task and inference task are regarded as a whole and can benefit from each other.  ... 
arXiv:1604.01662v4 fatcat:xuorcc2c3bhpnenw6oec72migi

A A Survey of the Link Prediction on Static and Temporal Knowledge Graph

Thanh Le, Hoang Nguyen, Bac Le
2021 Research and Development on Information and Communication Technology  
TuckER [77] is a tensor model which is based on Tucker decomposition. The core idea is to transform the tensor into a smaller core tensor -called W and several other vectors.  ...  Time Series These models apply methods used in predicting changes over time, such as predicting stock prices and COVID-19 deaths to predict changes in graph links.  ... 
doi:10.32913/mic-ict-research.v2021.n2.972 fatcat:vuvve5rzsbfgzpz5ax3sbqqxli

2021 Index IEEE Transactions on Industrial Informatics Vol. 17

2021 IEEE Transactions on Industrial Informatics  
., +, TII May 2021 3497-3507 COVID-19 Guest Editorial:Advanced Deep Learning Techniques for COVID-19.  ...  Prediction of COVID-19 Chest CT Scans.  ... 
doi:10.1109/tii.2021.3138206 fatcat:ulsazxgmpfdmlivigjqgyl7zre

A review of uncertainty quantification in deep learning: Techniques, applications and challenges

Moloud Abdar, Farhad Pourpanah, Sadiq Hussain, Dana Rezazadegan, Li Liu, Mohammad Ghavamzadeh, Paul Fieguth, Xiaochun Cao, Abbas Khosravi, U. Rajendra Acharya, Vladimir Makarenkov, Saeid Nahavandi
2021 Information Fusion  
recent advances in UQ methods used in deep learning, investigates the application of these methods in reinforcement learning, and highlights fundamental research challenges and directions associated with  ...  [174] devised a novel method called probabilistic ensembles with trajectory sampling that integrated sampling-based uncertainty propagation with a UA deep network dynamics approach.  ...  [325] devised a segmentation network with a conditional variational autoencoder (cVAE), termed hierarchical probabilistic U-Net, that applied a hierarchical latent space decomposition.  ... 
doi:10.1016/j.inffus.2021.05.008 fatcat:yschhguyxbfntftj6jv4dgywxm

Domain-specific Knowledge Graphs: A survey [article]

Bilal Abu-Salih
2021 arXiv   pre-print
Further, in conjunction with several limitations and deficiencies, various domain-specific KG construction approaches are far from perfect.  ...  Healthcare Recently, Healthcare sector has gained much attention, particularly with coronavirus 2019 (COVID-19) pandemic continues to rattle the world.  ...  Knowledge graphs continue to dominate as a distinctive form of data representation and knowledge inference, and a core activity for several industrial applications.  ... 
arXiv:2011.00235v3 fatcat:oc2loewqdjfgvlapy4kmult5li

Learning with Capsules: A Survey [article]

Fabio De Sousa Ribeiro, Kevin Duarte, Miles Everett, Georgios Leontidis, Mubarak Shah
2022 arXiv   pre-print
To that end, we start with an introduction to the fundamental concepts and motivations behind capsule networks, such as equivariant inference in computer vision.  ...  Capsule networks were proposed as an alternative approach to Convolutional Neural Networks (CNNs) for learning object-centric representations, which can be leveraged for improved generalization and sample  ...  Utilising a standard dynamic routing [14] architecture to classify either positive or negative for Covid-19, COVID-CAPS is able to achieve an accuracy of 95.7%.  ... 
arXiv:2206.02664v1 fatcat:auiy6oo5tbfghkppfyxysjiyty

Social Network Analysis: A Survey on Measure, Structure, Language Information Analysis, Privacy, and Applications

Shashank Sheshar Singh, Vishal Srivastava, Ajay Kumar, Shailendra Tiwari, Dilbag Singh, Heung-No Lee
2022 ACM Transactions on Asian and Low-Resource Language Information Processing  
This detailed study has started with the basics of network representation, structure, and measures. Our primary focus is on SNA applications with state-of-the-art techniques.  ...  Social network analysis (SNA) is a paramount technique supporting understanding social relationships and networks.  ...  For example, applying query on Google with text "Covid 19", and Google will provide a set of articles related to Covid 19.  ... 
doi:10.1145/3539732 fatcat:t4q2qkf3obcmplnzkwzvfq5ga4

Randomized Algorithms for Scientific Computing (RASC) [article]

Aydin Buluc, Tamara G. Kolda, Stefan M. Wild, Mihai Anitescu, Anthony DeGennaro, John Jakeman, Chandrika Kamath, Ramakrishnan Kannan, Miles E. Lopes, Per-Gunnar Martinsson, Kary Myers, Jelani Nelson (+7 others)
2021 arXiv   pre-print
Leveraging randomization methods for probabilistic inference with streaming data.  ...  n(n − 1)/2a Figure 19 : Morris's Probabilistic Counter [115] practice, to wanting to use low-memory data analytics in applications such as network traffic monitoring and databases [115, 116, 113,  ... 
arXiv:2104.11079v2 fatcat:qwwowtufzvbfjaiotx733eexxe
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