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Article ; Online: Comment on "Pressure enhancement in carbon nanopores: a major confinement effect" by Y. Long, J. C. Palmer, B. Coasne, M. Sliwinska-Bartkowiak and K. E. Gubbins, Phys. Chem. Chem. Phys., 2011, 13, 17163.

van Dijk, David

Physical chemistry chemical physics : PCCP

2020 Volume 22, Issue 17, Page(s) 9824–9825

Abstract: In an article from Phys. Chem. Chem. Phys., 2011, 13, 17163, it is claimed that the microscopic local pressure is very high in a certain family of physical systems and that this phenomenon explains previously reported phase equilibrium and chemical ... ...

Abstract	In an article from Phys. Chem. Chem. Phys., 2011, 13, 17163, it is claimed that the microscopic local pressure is very high in a certain family of physical systems and that this phenomenon explains previously reported phase equilibrium and chemical reaction equilibrium data. The results provided in the article are based on two arbitrary choices. Thus, the results are arbitrary, and the conclusions appear to be unjustified.
Language	English
Publishing date	2020-04-27
Publishing country	England
Document type	Journal Article
ZDB-ID	1476244-4
ISSN	1463-9084 ; 1463-9076
ISSN (online)	1463-9084
ISSN	1463-9076
DOI	10.1039/c9cp02890k
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A machine learning method for the identification and characterization of novel COVID-19 drug targets.

Schultz, Bruce / DeLong, Lauren Nicole / Masny, Aliaksandr / Lentzen, Manuel / Raschka, Tamara / van Dijk, David / Zaliani, Andrea / Fröhlich, Holger

Scientific reports

2023 Volume 13, Issue 1, Page(s) 7159

Abstract: In addition to vaccines, the World Health Organization sees novel medications as an urgent matter to fight the ongoing COVID-19 pandemic. One possible strategy is to identify target proteins, for which a perturbation by an existing compound is likely to ... ...

Abstract	In addition to vaccines, the World Health Organization sees novel medications as an urgent matter to fight the ongoing COVID-19 pandemic. One possible strategy is to identify target proteins, for which a perturbation by an existing compound is likely to benefit COVID-19 patients. In order to contribute to this effort, we present GuiltyTargets-COVID-19 ( https://guiltytargets-covid.eu/ ), a machine learning supported web tool to identify novel candidate drug targets. Using six bulk and three single cell RNA-Seq datasets, together with a lung tissue specific protein-protein interaction network, we demonstrate that GuiltyTargets-COVID-19 is capable of (i) prioritizing meaningful target candidates and assessing their druggability, (ii) unraveling their linkage to known disease mechanisms, (iii) mapping ligands from the ChEMBL database to the identified targets, and (iv) pointing out potential side effects in the case that the mapped ligands correspond to approved drugs. Our example analyses identified 4 potential drug targets from the datasets: AKT3 from both the bulk and single cell RNA-Seq data as well as AKT2, MLKL, and MAPK11 in the single cell experiments. Altogether, we believe that our web tool will facilitate future target identification and drug development for COVID-19, notably in a cell type and tissue specific manner.
MeSH term(s)	Humans ; COVID-19 ; Ligands ; Pandemics ; Machine Learning ; Proteins/metabolism
Chemical Substances	Ligands ; Proteins
Language	English
Publishing date	2023-05-03
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-34287-5
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Article ; Online: scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles.

Zhu, Biqing / Wang, Yuge / Ku, Li-Ting / van Dijk, David / Zhang, Le / Hafler, David A / Zhao, Hongyu

Genome biology

2023 Volume 24, Issue 1, Page(s) 292

Abstract: Many deep learning-based methods have been proposed to handle complex single-cell data. Deep learning approaches may also prove useful to jointly analyze single-cell RNA sequencing (scRNA-seq) and single-cell T cell receptor sequencing (scTCR-seq) data ... ...

Abstract	Many deep learning-based methods have been proposed to handle complex single-cell data. Deep learning approaches may also prove useful to jointly analyze single-cell RNA sequencing (scRNA-seq) and single-cell T cell receptor sequencing (scTCR-seq) data for novel discoveries. We developed scNAT, a deep learning method that integrates paired scRNA-seq and scTCR-seq data to represent data in a unified latent space for downstream analysis. We demonstrate that scNAT is capable of removing batch effects, and identifying cell clusters and a T cell migration trajectory from blood to cerebrospinal fluid in multiple sclerosis.
MeSH term(s)	Humans ; Deep Learning ; Cell Movement ; Multiple Sclerosis/genetics ; RNA ; Single-Cell Analysis ; Sequence Analysis, RNA ; Gene Expression Profiling ; Cluster Analysis
Chemical Substances	RNA (63231-63-0)
Language	English
Publishing date	2023-12-18
Publishing country	England
Document type	Journal Article
ZDB-ID	2040529-7
ISSN	1474-760X ; 1474-760X
ISSN (online)	1474-760X
ISSN	1474-760X
DOI	10.1186/s13059-023-03129-y
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Book ; Online: Continuous Spatiotemporal Transformers

Fonseca, Antonio H. de O. / Zappala, Emanuele / Caro, Josue Ortega / van Dijk, David

2023

Abstract: Modeling spatiotemporal dynamical systems is a fundamental challenge in machine learning. Transformer models have been very successful in NLP and computer vision where they provide interpretable representations of data. However, a limitation of ... ...

Abstract	Modeling spatiotemporal dynamical systems is a fundamental challenge in machine learning. Transformer models have been very successful in NLP and computer vision where they provide interpretable representations of data. However, a limitation of transformers in modeling continuous dynamical systems is that they are fundamentally discrete time and space models and thus have no guarantees regarding continuous sampling. To address this challenge, we present the Continuous Spatiotemporal Transformer (CST), a new transformer architecture that is designed for the modeling of continuous systems. This new framework guarantees a continuous and smooth output via optimization in Sobolev space. We benchmark CST against traditional transformers as well as other spatiotemporal dynamics modeling methods and achieve superior performance in a number of tasks on synthetic and real systems, including learning brain dynamics from calcium imaging data.
Keywords	Computer Science - Machine Learning ; Computer Science - Computer Vision and Pattern Recognition
Subject code	006
Publishing date	2023-01-30
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Operator Learning Meets Numerical Analysis

Zappala, Emanuele / Levine, Daniel / He, Sizhuang / Rizvi, Syed / Levy, Sacha / van Dijk, David

Improving Neural Networks through Iterative Methods

2023

Abstract: Deep neural networks, despite their success in numerous applications, often function without established theoretical foundations. In this paper, we bridge this gap by drawing parallels between deep learning and classical numerical analysis. By framing ... ...

Abstract	Deep neural networks, despite their success in numerous applications, often function without established theoretical foundations. In this paper, we bridge this gap by drawing parallels between deep learning and classical numerical analysis. By framing neural networks as operators with fixed points representing desired solutions, we develop a theoretical framework grounded in iterative methods for operator equations. Under defined conditions, we present convergence proofs based on fixed point theory. We demonstrate that popular architectures, such as diffusion models and AlphaFold, inherently employ iterative operator learning. Empirical assessments highlight that performing iterations through network operators improves performance. We also introduce an iterative graph neural network, PIGN, that further demonstrates benefits of iterations. Our work aims to enhance the understanding of deep learning by merging insights from numerical analysis, potentially guiding the design of future networks with clearer theoretical underpinnings and improved performance. Comment: 27 pages (13+14). 8 Figures and 5 tables. Comments are welcome!
Keywords	Computer Science - Machine Learning ; Mathematics - Numerical Analysis
Subject code	518
Publishing date	2023-10-02
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: LEARNING GENERAL TRANSFORMATIONS OF DATA FOR OUT-OF-SAMPLE EXTENSIONS.

Amodio, Matthew / van Dijk, David / Wolf, Guy / Krishnaswamy, Smita

IEEE International Workshop on Machine Learning for Signal Processing : [proceedings]. IEEE International Workshop on Machine Learning for Signal Processing

2020 Volume 2020

Abstract: While generative models such as GANs have been successful at mapping from noise to specific distributions of data, or more generally from one distribution of data to another, they cannot isolate the transformation that is occurring and apply it to a new ... ...

Abstract	While generative models such as GANs have been successful at mapping from noise to specific distributions of data, or more generally from one distribution of data to another, they cannot isolate the transformation that is occurring and apply it to a new distribution not seen in training. Thus, they memorize the domain of the transformation, and cannot generalize the transformation
Language	English
Publishing date	2020-10-20
Publishing country	United States
Document type	Journal Article
ISSN	2161-0363
ISSN	2161-0363
DOI	10.1109/mlsp49062.2020.9231660
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Article ; Online: TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics.

Tong, Alexander / Huang, Jessie / Wolf, Guy / van Dijk, David / Krishnaswamy, Smita

Proceedings of machine learning research

2021 Volume 119, Page(s) 9526–9536

Abstract: It is increasingly common to encounter data from dynamic processes captured by static cross-sectional measurements over time, particularly in biomedical settings. Recent attempts to model individual trajectories from this data use optimal transport to ... ...

Abstract	It is increasingly common to encounter data from dynamic processes captured by static cross-sectional measurements over time, particularly in biomedical settings. Recent attempts to model individual trajectories from this data use optimal transport to create pairwise matchings between time points. However, these methods cannot model continuous dynamics and non-linear paths that entities can take in these systems. To address this issue, we establish a link between continuous normalizing flows and dynamic optimal transport, that allows us to model the expected paths of points over time. Continuous normalizing flows are generally under constrained, as they are allowed to take an arbitrary path from the source to the target distribution. We present
Language	English
Publishing date	2021-08-02
Publishing country	United States
Document type	Journal Article
ISSN	2640-3498
ISSN (online)	2640-3498
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Article ; Online: Human pancreatic tumour organoid-derived factors enhance myogenic differentiation.

Vaes, Rianne D W / van Dijk, David P J / Farshadi, Elham Aïda / Olde Damink, Steven W M / Rensen, Sander S / Langen, Ramon C

Journal of cachexia, sarcopenia and muscle

2022 Volume 13, Issue 2, Page(s) 1302–1313

Abstract: Background: Most patients with pancreatic cancer develop cachexia, which is characterized by progressive muscle loss. The mechanisms underlying muscle loss in cancer cachexia remain elusive. Pancreatic tumour organoids are 3D cell culture models that ... ...

Abstract	Background: Most patients with pancreatic cancer develop cachexia, which is characterized by progressive muscle loss. The mechanisms underlying muscle loss in cancer cachexia remain elusive. Pancreatic tumour organoids are 3D cell culture models that retain key characteristics of the parent tumour. We aimed to investigate the effect of pancreatic tumour organoid-derived factors on processes that determine skeletal muscle mass, including the regulation of muscle protein turnover and myogenesis. Methods: Conditioned medium (CM) was collected from human pancreatic cancer cell lines (PK-45H, PANC-1, PK-1, and KLM-1), pancreatic tumour organoid cultures from a severely cachectic (PANCO-9a) and a non-cachectic patient (PANCO-12a), and a normal pancreas organoid culture. Differentiating C2C12 myoblasts and mature C2C12 myotubes were exposed to CM for 24 h or maintained in control medium. In myotubes, NF-kB activation was monitored using a NF-κB luciferase reporter construct, and mRNA expression of E3-ubiquitin ligases and REDD1 was analysed by RT-qPCR. C2C12 myoblast proliferation and differentiation were monitored by live cell imaging and myogenic markers and myosin heavy chain (MyHC) isoforms were assessed by RT-qPCR. Results: Whereas CM from PK-1 and KLM-1 cells significantly induced NF-κB activation in C2C12 myotubes (PK-1: 3.1-fold, P < 0.001; KLM-1: 2.1-fold, P = 0.01), Atrogin-1/MAFbx and MuRF1 mRNA were only minimally and inconsistently upregulated by the CM of pancreatic cancer cell lines. Similarly, E3-ubiquitin ligases and REDD1 mRNA expression in myotubes were not altered by exposure to pancreatic tumour organoid CM. Compared with the control condition, CM from both PANCO-9a and PANCO-12a tumour organoids increased proliferation of myoblasts, which was accompanied by significant downregulation of the satellite cell marker paired-box 7 (PAX7) (PANCO-9a: -2.1-fold, P < 0.001; PANCO-12a: -2.0-fold, P < 0.001) and myogenic factor 5 (MYF5) (PANCO-9a: -2.1-fold, P < 0.001; PANCO-12a: -1.8-fold, P < 0.001) after 48 h of differentiation. Live cell imaging revealed accelerated alignment and fusion of myoblasts exposed to CM from PANCO-9a and PANCO-12a, which was in line with significantly increased Myomaker mRNA expression levels (PANCO-9a: 2.4-fold, P = 0.001; PANCO-12a: 2.2-fold, P = 0.004). These morphological and transcriptional alterations were accompanied by increased expression of muscle differentiation markers such as MyHC-IIB (PANCO-9a: 2.5-fold, P = 0.04; PANCO-12a: 3.1-fold, P = 0.006). Although the impact of organoid CM on myogenesis was not associated with the cachexia phenotype of the donor patients, it was specific for tumour organoids, as CM of control pancreas organoids did not modulate myogenic fusion. Conclusions: These data show that pancreatic tumour organoid-derived factors alter the kinetics of myogenesis, which may eventually contribute to impaired muscle mass maintenance in cancer cachexia.
MeSH term(s)	Cachexia/metabolism ; Humans ; Muscle Development/physiology ; Myoblasts/metabolism ; Organoids/metabolism ; Pancreatic Neoplasms/metabolism
Language	English
Publishing date	2022-02-11
Publishing country	Germany
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2586864-0
ISSN	2190-6009 ; 2190-5991
ISSN (online)	2190-6009
ISSN	2190-5991
DOI	10.1002/jcsm.12917
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Book ; Online: Gaining Insight into SARS-CoV-2 Infection and COVID-19 Severity Using Self-supervised Edge Features and Graph Neural Networks

Sehanobish, Arijit / Ravindra, Neal G. / van Dijk, David

2020

Abstract: A molecular and cellular understanding of how SARS-CoV-2 variably infects and causes severe COVID-19 remains a bottleneck in developing interventions to end the pandemic. We sought to use deep learning to study the biology of SARS-CoV-2 infection and ... ...

Abstract	A molecular and cellular understanding of how SARS-CoV-2 variably infects and causes severe COVID-19 remains a bottleneck in developing interventions to end the pandemic. We sought to use deep learning to study the biology of SARS-CoV-2 infection and COVID-19 severity by identifying transcriptomic patterns and cell types associated with SARS-CoV-2 infection and COVID-19 severity. To do this, we developed a new approach to generating self-supervised edge features. We propose a model that builds on Graph Attention Networks (GAT), creates edge features using self-supervised learning, and ingests these edge features via a Set Transformer. This model achieves significant improvements in predicting the disease state of individual cells, given their transcriptome. We apply our model to single-cell RNA sequencing datasets of SARS-CoV-2 infected lung organoids and bronchoalveolar lavage fluid samples of patients with COVID-19, achieving state-of-the-art performance on both datasets with our model. We then borrow from the field of explainable AI (XAI) to identify the features (genes) and cell types that discriminate bystander vs. infected cells across time and moderate vs. severe COVID-19 disease. To the best of our knowledge, this represents the first application of deep learning to identifying the molecular and cellular determinants of SARS-CoV-2 infection and COVID-19 severity using single-cell omics data. Comment: To appear at AAAI'21. Previous version (v2) accepted as a spotlight talk at ICML 2020 Workshop on Graph Representation Learning and Beyond (GRL+) and recipient of best paper award for Covid-19 applications. Significant improvements over v2
Keywords	Computer Science - Machine Learning ; Quantitative Biology - Genomics ; Statistics - Machine Learning
Subject code	006
Publishing date	2020-06-23
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Self-supervised edge features for improved Graph Neural Network training

Sehanobish, Arijit / Ravindra, Neal G. / van Dijk, David

2020

Abstract: Graph Neural Networks (GNN) have been extensively used to extract meaningful representations from graph structured data and to perform predictive tasks such as node classification and link prediction. In recent years, there has been a lot of work ... ...

Abstract	Graph Neural Networks (GNN) have been extensively used to extract meaningful representations from graph structured data and to perform predictive tasks such as node classification and link prediction. In recent years, there has been a lot of work incorporating edge features along with node features for prediction tasks. One of the main difficulties in using edge features is that they are often handcrafted, hard to get, specific to a particular domain, and may contain redundant information. In this work, we present a framework for creating new edge features, applicable to any domain, via a combination of self-supervised and unsupervised learning. In addition to this, we use Forman-Ricci curvature as an additional edge feature to encapsulate the local geometry of the graph. We then encode our edge features via a Set Transformer and combine them with node features extracted from popular GNN architectures for node classification in an end-to-end training scheme. We validate our work on three biological datasets comprising of single-cell RNA sequencing data of neurological disease, \textit{in vitro} SARS-CoV-2 infection, and human COVID-19 patients. We demonstrate that our method achieves better performance on node classification tasks over baseline Graph Attention Network (GAT) and Graph Convolutional Network (GCN) models. Furthermore, given the attention mechanism on edge and node features, we are able to interpret the cell types and genes that determine the course and severity of COVID-19, contributing to a growing list of potential disease biomarkers and therapeutic targets. Comment: Comments welcome. arXiv admin note: substantial text overlap with arXiv:2006.12971
Keywords	Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Machine Learning ; Quantitative Biology - Genomics ; Statistics - Machine Learning ; I.2.4 ; J.3 ; covid19
Subject code	006
Publishing date	2020-06-23
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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