The Experts below are selected from a list of 799893 Experts worldwide ranked by ideXlab platform
Jure Leskovec - One of the best experts on this subject based on the ideXlab platform.
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embedding logical queries on knowledge graphs
Neural Information Processing Systems, 2018Co-Authors: William L Hamilto, Payal Ajaj, Marinka Zitnik, Da Jurafsky, Jure LeskovecAbstract:Learning low-dimensional embeddings of knowledge graphs is a powerful approach used to predict unobserved or missing edges between entities. However, an open challenge in this area is developing techniques that can go beyond simple edge prediction and handle more complex logical queries, which might involve multiple unobserved edges, entities, and variables. For instance, given an incomplete biological knowledge graph, we might want to predict "em what drugs are likely to target proteins involved with both diseases X and Y?" -- a query that requires reasoning about all possible proteins that might interact with diseases X and Y. Here we introduce a framework to efficiently make predictions about conjunctive logical queries -- a flexible but tractable subset of first-order logic -- on incomplete knowledge graphs. In our approach, we embed graph nodes in a low-dimensional space and represent logical operators as learned geometric operations (e.g., translation, rotation) in this embedding space. By performing logical operations within a low-dimensional embedding space, our approach achieves a time complexity that is linear in the number of query variables, compared to the exponential complexity required by a naive enumeration-based approach. We demonstrate the utility of this framework in two application studies on real-world datasets with millions of relations: predicting logical relationships in a network of drug-gene-disease interactions and in a Graph-Based Representation of social interactions derived from a popular web forum.
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embedding logical queries on knowledge graphs
arXiv: Social and Information Networks, 2018Co-Authors: William L Hamilto, Payal Ajaj, Marinka Zitnik, Da Jurafsky, Jure LeskovecAbstract:Learning low-dimensional embeddings of knowledge graphs is a powerful approach used to predict unobserved or missing edges between entities. However, an open challenge in this area is developing techniques that can go beyond simple edge prediction and handle more complex logical queries, which might involve multiple unobserved edges, entities, and variables. For instance, given an incomplete biological knowledge graph, we might want to predict "em what drugs are likely to target proteins involved with both diseases X and Y?" -- a query that requires reasoning about all possible proteins that {\em might} interact with diseases X and Y. Here we introduce a framework to efficiently make predictions about conjunctive logical queries -- a flexible but tractable subset of first-order logic -- on incomplete knowledge graphs. In our approach, we embed graph nodes in a low-dimensional space and represent logical operators as learned geometric operations (e.g., translation, rotation) in this embedding space. By performing logical operations within a low-dimensional embedding space, our approach achieves a time complexity that is linear in the number of query variables, compared to the exponential complexity required by a naive enumeration-based approach. We demonstrate the utility of this framework in two application studies on real-world datasets with millions of relations: predicting logical relationships in a network of drug-gene-disease interactions and in a Graph-Based Representation of social interactions derived from a popular web forum.
Pascal Frossard - One of the best experts on this subject based on the ideXlab platform.
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Graph-Based Representation for multiview image geometry
IEEE Transactions on Image Processing, 2015Co-Authors: Thomas Maugey, Antonio Ortega, Pascal FrossardAbstract:In this paper, we propose a new geometry Representation method for multiview image sets. Our approach relies on graphs to describe the multiview geometry information in a compact and controllable way. The links of the graph connect pixels in different images and describe the proximity between pixels in 3D space. These connections are dependent on the geometry of the scene and provide the right amount of information that is necessary for coding and reconstructing multiple views. Our multiview image Representation is very compact and adapts the transmitted geometry information as a function of the complexity of the prediction performed at the decoder side. To achieve this, our Graph-Based Representation (GBR) carefully selects the amount of geometry information needed before coding. This is in contrast with depth coding, which directly compresses with losses the original geometry signal, thus making it difficult to quantify the impact of coding errors on geometry-based interpolation. We present the principles of this GBR and we build an efficient coding algorithm to represent it. We compare our GBR approach to classical depth compression methods and compare their respective view synthesis qualities as a function of the compactness of the geometry description. We show that GBR can achieve significant gains in geometry coding rate over depth-based schemes operating at similar quality. Experimental results demonstrate the potential of this new Representation.
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graph based Representation and coding of multiview geometry
International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Thomas Maugey, Antonio Ortega, Pascal FrossardAbstract:We propose a new approach for describing the geometry information of multiview image Representations. Rather than transmitting the raw geometry of the scene, under the form of depth information, we build a graph that represents the connections between corresponding pixels in different views in a multiview image set. The graph starts with the reference image and recursively represents the next levels (i.e., images) by storing the new pixels (those that cannot be derived from the previous image) and their connections to the lower level. The decoder uses these connections to recover the multiple images. In addition to being natural and more easily controlled, the proposed Graph-Based Representation can be compressed more efficiently than depth images. This new Representation offers promising perspectives for effective and flexible coding in multiview imaging.
William L Hamilto - One of the best experts on this subject based on the ideXlab platform.
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embedding logical queries on knowledge graphs
Neural Information Processing Systems, 2018Co-Authors: William L Hamilto, Payal Ajaj, Marinka Zitnik, Da Jurafsky, Jure LeskovecAbstract:Learning low-dimensional embeddings of knowledge graphs is a powerful approach used to predict unobserved or missing edges between entities. However, an open challenge in this area is developing techniques that can go beyond simple edge prediction and handle more complex logical queries, which might involve multiple unobserved edges, entities, and variables. For instance, given an incomplete biological knowledge graph, we might want to predict "em what drugs are likely to target proteins involved with both diseases X and Y?" -- a query that requires reasoning about all possible proteins that might interact with diseases X and Y. Here we introduce a framework to efficiently make predictions about conjunctive logical queries -- a flexible but tractable subset of first-order logic -- on incomplete knowledge graphs. In our approach, we embed graph nodes in a low-dimensional space and represent logical operators as learned geometric operations (e.g., translation, rotation) in this embedding space. By performing logical operations within a low-dimensional embedding space, our approach achieves a time complexity that is linear in the number of query variables, compared to the exponential complexity required by a naive enumeration-based approach. We demonstrate the utility of this framework in two application studies on real-world datasets with millions of relations: predicting logical relationships in a network of drug-gene-disease interactions and in a Graph-Based Representation of social interactions derived from a popular web forum.
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embedding logical queries on knowledge graphs
arXiv: Social and Information Networks, 2018Co-Authors: William L Hamilto, Payal Ajaj, Marinka Zitnik, Da Jurafsky, Jure LeskovecAbstract:Learning low-dimensional embeddings of knowledge graphs is a powerful approach used to predict unobserved or missing edges between entities. However, an open challenge in this area is developing techniques that can go beyond simple edge prediction and handle more complex logical queries, which might involve multiple unobserved edges, entities, and variables. For instance, given an incomplete biological knowledge graph, we might want to predict "em what drugs are likely to target proteins involved with both diseases X and Y?" -- a query that requires reasoning about all possible proteins that {\em might} interact with diseases X and Y. Here we introduce a framework to efficiently make predictions about conjunctive logical queries -- a flexible but tractable subset of first-order logic -- on incomplete knowledge graphs. In our approach, we embed graph nodes in a low-dimensional space and represent logical operators as learned geometric operations (e.g., translation, rotation) in this embedding space. By performing logical operations within a low-dimensional embedding space, our approach achieves a time complexity that is linear in the number of query variables, compared to the exponential complexity required by a naive enumeration-based approach. We demonstrate the utility of this framework in two application studies on real-world datasets with millions of relations: predicting logical relationships in a network of drug-gene-disease interactions and in a Graph-Based Representation of social interactions derived from a popular web forum.
Elisabeta G Marai - One of the best experts on this subject based on the ideXlab platform.
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precision association of lymphatic disease spread with radiation associated toxicity in oropharyngeal squamous carcinomas
Radiotherapy and Oncology, 2021Co-Authors: Andrew Wentzel, Timothy Luciani, Lisanne V Van Dijk, Nicolette Taku, Baher Elgohari, Abdallah S R Mohamed, Guadalupe Canahuate, Clifton D Fuller, David M Vock, Elisabeta G MaraiAbstract:Abstract Purpose To determine whether patient similarity in terms of head and neck cancer spread through lymph nodes correlates significantly with radiation-associated toxicity. Materials and methods 582 head and neck cancer patients received radiotherapy for oropharyngeal cancer (OPC) and had non-metastatic affected lymph nodes in the head and neck. Affected lymph nodes were segmented from pretreatment contrast-enhanced tomography scans and categorized according to consensus guidelines. Similar patients were clustered into 4 groups according to a Graph-Based Representation of disease spread through affected lymph nodes. Correlation between dysphagia-associated symptoms and patient groups was calculated. Results Out of 582 patients, 26% (152) experienced toxicity during a follow up evaluation 6 months after completion of radiotherapy treatment. Patient groups identified by our approach were significantly correlated with dysphagia, feeding tube, and aspiration toxicity (p Discussion Our results suggest that structural geometry-aware characterization of affected lymph nodes can be used to better predict radiation-associated dysphagia at time of diagnosis, and better inform treatment guidelines. Conclusion Our work successfully stratified a patient cohort into similar groups using a structural geometry, graph-encoding of affected lymph nodes in oropharyngeal cancer patients, that were predictive of late radiation-associated dysphagia and toxicity.
Andrew Wentzel - One of the best experts on this subject based on the ideXlab platform.
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precision association of lymphatic disease spread with radiation associated toxicity in oropharyngeal squamous carcinomas
Radiotherapy and Oncology, 2021Co-Authors: Andrew Wentzel, Timothy Luciani, Lisanne V Van Dijk, Nicolette Taku, Baher Elgohari, Abdallah S R Mohamed, Guadalupe Canahuate, Clifton D Fuller, David M Vock, Elisabeta G MaraiAbstract:Abstract Purpose To determine whether patient similarity in terms of head and neck cancer spread through lymph nodes correlates significantly with radiation-associated toxicity. Materials and methods 582 head and neck cancer patients received radiotherapy for oropharyngeal cancer (OPC) and had non-metastatic affected lymph nodes in the head and neck. Affected lymph nodes were segmented from pretreatment contrast-enhanced tomography scans and categorized according to consensus guidelines. Similar patients were clustered into 4 groups according to a Graph-Based Representation of disease spread through affected lymph nodes. Correlation between dysphagia-associated symptoms and patient groups was calculated. Results Out of 582 patients, 26% (152) experienced toxicity during a follow up evaluation 6 months after completion of radiotherapy treatment. Patient groups identified by our approach were significantly correlated with dysphagia, feeding tube, and aspiration toxicity (p Discussion Our results suggest that structural geometry-aware characterization of affected lymph nodes can be used to better predict radiation-associated dysphagia at time of diagnosis, and better inform treatment guidelines. Conclusion Our work successfully stratified a patient cohort into similar groups using a structural geometry, graph-encoding of affected lymph nodes in oropharyngeal cancer patients, that were predictive of late radiation-associated dysphagia and toxicity.
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precision association of lymphatic disease spread with radiation associated toxicity in oropharyngeal squamous carcinomas
medRxiv, 2020Co-Authors: Andrew Wentzel, Timothy Luciani, Lisanne V Van Dijk, Nicolette Taku, Baher Elgohari, Abdallah S R Mohamed, Guadalupe Canahuate, Clifton D Fuller, David M VockAbstract:Purpose: Using a cohort of 582 head and neck cancer patients with nodal disease, we employed clustering over a novel Graph-Based geometrical Representation of lymph node spread in order to identify groups of similar patients. We show that these groups are significantly correlated with radiation-associated dysphagia (RAD), and predictive of late aspiration and feeding tube toxicity. Materials and methods: All patients received radiotherapy for oropharyngeal cancer (OPC) and had non-metastatic affected lymph nodes in the head and neck. Affected lymph nodes were segmented from pretreatment contrast-enhanced tomography scans and categorized according to consensus guidelines. Similar patients were clustered into 4 groups according to a Graph-Based Representation of affected lymph nodes. Correlation between dysphagia associated symptoms and patient groups was calculated. Results: Out of 582 patients, 26% (152) experienced toxicity during a follow up evaluation 6 months after completion of radiotherapy treatment. Patient groups identified by our approach were significantly correlated with dysphagia, feeding tube, and aspiration toxicity (p < .0005). Conclusion: Our work successfully stratified a patient cohort into similar groups using a structural geometry, graph-encoding of affected lymph nodes in OPC patients, that were predictive of late radiation-associated dysphagia. Our results suggest that structural geometry-aware characterization of affected lymph nodes can be used to better predict radiation-associated dysphagia at time of diagnosis, and better inform treatment guidelines.
