scholarly journals Metastatic Site Prediction in Breast Cancer using Omics Knowledge Graph and Pattern Mining with Kirchhoff’s Law Traversal

2020 ◽  
Author(s):  
Alokkumar Jha ◽  
Yasar Khan ◽  
Ratnesh Sahay ◽  
Mathieu d’Aquin
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Pattern Mining ◽  
Knowledge Graph ◽  
Omics Data ◽  
Metastatic Site ◽  
Decision Network ◽  
Knowledge Graphs ◽  
Metastatic Sites ◽  
Kirchhoff's Law

AbstractPrediction of metastatic sites from the primary site of origin is a impugn task in breast cancer (BRCA). Multi-dimensionality of such metastatic sites - bone, lung, kidney, and brain, using large-scale multi-dimensional Poly-Omics (Transcriptomics, Proteomics and Metabolomics) data of various type, for example, CNV (Copy number variation), GE (Gene expression), DNA methylation, path-ways, and drugs with clinical associations makes classification of metastasis a multi-faceted challenge. In this paper, we have approached the above problem in three steps; 1) Applied Linked data and semantic web to build Poly-Omics data as knowledge graphs and termed them as cancer decision network; 2) Reduced the dimensionality of data using Graph Pattern Mining and explained gene rewiring in cancer decision network by first time using Kirchhoff’s law for knowledge or any graph traversal; 3) Established ruled based modeling to understand the essential -Omics data from poly-Omics for breast cancer progression 4) Predicted the disease’s metastatic site using Kirchhoff’s knowledge graphs as a hidden layer in the graph convolution neural network(GCNN). The features (genes) extracted by applying Kirchhoff’s law on knowledge graphs are used to predict disease relapse site with 91.9% AUC (Area Under Curve) and performed detailed evaluation against the state-of-the-art approaches. The novelty of our approach is in the creation of RDF knowledge graphs from the poly-omics, such as the drug, disease, target(gene/protein), pathways and application of Kirchhoff’s law on knowledge graph to and the first approach to predict metastatic site from the primary tumor. Further, we have applied the rule-based knowledge graph using graph convolution neural network for metastasis site prediction makes the even classification novel.

scholarly journals Research of Personalized Recommendation Technology Based on Knowledge Graphs

2021 ◽  
Vol 11 (15) ◽  
pp. 7104
Author(s):  
Xu Yang ◽  
Ziyi Huan ◽  
Yisong Zhai ◽  
Ting Lin
Keyword(s):  
Neural Network ◽  
Hot Spot ◽  
Experimental Results ◽  
Graph Representation ◽  
Personalized Recommendation ◽  
Knowledge Graph ◽  
Learning Technology ◽  
Data Set ◽  
Model Based ◽  
Knowledge Graphs

Nowadays, personalized recommendation based on knowledge graphs has become a hot spot for researchers due to its good recommendation effect. In this paper, we researched personalized recommendation based on knowledge graphs. First of all, we study the knowledge graphs’ construction method and complete the construction of the movie knowledge graphs. Furthermore, we use Neo4j graph database to store the movie data and vividly display it. Then, the classical translation model TransE algorithm in knowledge graph representation learning technology is studied in this paper, and we improved the algorithm through a cross-training method by using the information of the neighboring feature structures of the entities in the knowledge graph. Furthermore, the negative sampling process of TransE algorithm is improved. The experimental results show that the improved TransE model can more accurately vectorize entities and relations. Finally, this paper constructs a recommendation model by combining knowledge graphs with ranking learning and neural network. We propose the Bayesian personalized recommendation model based on knowledge graphs (KG-BPR) and the neural network recommendation model based on knowledge graphs(KG-NN). The semantic information of entities and relations in knowledge graphs is embedded into vector space by using improved TransE method, and we compare the results. The item entity vectors containing external knowledge information are integrated into the BPR model and neural network, respectively, which make up for the lack of knowledge information of the item itself. Finally, the experimental analysis is carried out on MovieLens-1M data set. The experimental results show that the two recommendation models proposed in this paper can effectively improve the accuracy, recall, F1 value and MAP value of recommendation.

scholarly journals MOVIE RECOMMENDATION USING KNOWLEDGE GRAPH

2020 ◽  
pp. 99-103
Author(s):  
Navin Tatyaba Gopal ◽  
Anish Raj Khobragade
Keyword(s):  
Neural Network ◽  
Recommender Systems ◽  
Weighted Graph ◽  
Cold Start ◽  
Structured Data ◽  
Label Propagation ◽  
Knowledge Graph ◽  
Movie Recommendation ◽  
Knowledge Graphs ◽  
Prior Application

The Knowledge graphs (KGs) catches structured data and relationships among a bunch of entities and items. Generally, constitute an attractive origin of information that can advance the recommender systems. But, present methodologies of this area depend on manual element thus don’t permit for start to end training. This article proposes, Knowledge Graph along with Label Smoothness (KG-LS) to offer better suggestions for the recommender Systems. Our methodology processes user-specific entities by prior application of a function capability that recognizes key KG-relationships for a specific user. In this manner, we change the KG in a specific-user weighted graph followed by application of a graph neural network to process customized entity embedding. To give better preliminary predisposition, label smoothness comes into picture, which places items in the KG which probably going to have identical user significant names/scores. Use of, label smoothness gives regularization above the edge weights thus; we demonstrate that it is comparable to a label propagation plan on the graph. Additionally building-up a productive usage that symbolizes solid adaptability concerning the size of knowledge graph. Experimentation on 4 datasets shows that our strategy beats best in class baselines. This process likewise accomplishes solid execution in cold start situations where user-entity communications remain meager.

scholarly journals Relational Graph Neural Network with Hierarchical Attention for Knowledge Graph Completion

2020 ◽  
Vol 34 (05) ◽  
pp. 9612-9619
Author(s):  
Zhao Zhang ◽  
Fuzhen Zhuang ◽  
Hengshu Zhu ◽  
Zhiping Shi ◽  
Hui Xiong ◽  
...  
Keyword(s):  
Neural Network ◽  
Missing Values ◽  
State Of The Art ◽  
Attention Mechanism ◽  
Knowledge Graph ◽  
Incomplete Knowledge ◽  
Neighborhood Information ◽  
Local Neighborhood ◽  
Proposed Model ◽  
Knowledge Graphs

The rapid proliferation of knowledge graphs (KGs) has changed the paradigm for various AI-related applications. Despite their large sizes, modern KGs are far from complete and comprehensive. This has motivated the research in knowledge graph completion (KGC), which aims to infer missing values in incomplete knowledge triples. However, most existing KGC models treat the triples in KGs independently without leveraging the inherent and valuable information from the local neighborhood surrounding an entity. To this end, we propose a Relational Graph neural network with Hierarchical ATtention (RGHAT) for the KGC task. The proposed model is equipped with a two-level attention mechanism: (i) the first level is the relation-level attention, which is inspired by the intuition that different relations have different weights for indicating an entity; (ii) the second level is the entity-level attention, which enables our model to highlight the importance of different neighboring entities under the same relation. The hierarchical attention mechanism makes our model more effective to utilize the neighborhood information of an entity. Finally, we extensively validate the superiority of RGHAT against various state-of-the-art baselines.

HER2/neu status in primary breast cancer and in metastatic site as detected using FNA.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. e11502-e11502
Author(s):  
Francesco Giotta ◽  
Maria Consilia Asselti ◽  
Stella Petroni ◽  
Onsina Popescu ◽  
Vincenza Rubini ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Proliferative Activity ◽  
Growth Factor Receptor ◽  
Metastatic Breast ◽  
Significant Loss ◽  
Breast Cancer Patients ◽  
Ki 67 ◽  
Metastatic Site ◽  
Her 2 ◽  
Metastatic Sites

e11502 Background: As for hormonal receptor (ER, PgR), also human epidermal growth factor receptor-2 (HER-2) expression in breast cancer primitive tumor (PT) could be different from that of metastatic site (MS). These differences arise some questions about clinically useful information given and accuracy of methods to detect biological features. Fine Needle Aspiration (FNA) of metastatic sites could be an available tool to characterize biologic pattern of lesions, using immunocytochemical and/or molecular assay. The aim of the study is to compare prognostic and predictive factors obtained from PT and corresponding MS. Methods: Thirty-eight consecutive metastatic breast cancer patients underwent FNA on metastatic sites in order to re-evaluate receptor status, proliferative activity and HER-2/Neu amplification. In MS the material was achieved using FNA with a 21-23 G needle and obtaining monolayer and the corresponding cito-inclusion. MS were localized in liver (21), lung (8) and distant lymph-nodes (9). ERs, PgRs and Ki-67 were detected in both PT and MS, in 38 cases by immunochemistry, whereas HER-2/Neu amplification was detected on citoinclusion in 35 evaluable cases by FISH. Results: ERs, PgRs and Ki-67 were detected in both PT and in MS, in 36, 34, 25 out of 38 cases respectively, showing a significant loss of hormonal receptors and a decreased proliferative activity in MS versus PT (t-test p: 0.0195, <0.0001 and 0.0120 respectively). Regarding to HER-2/Neu amplification, 28 out of 35 evaluable cases were not amplified while 6 were amplified both in PT and in MS (Pearson Test: r=0.9 p: <0.0001). Another case, HER/Neu amplified in TP, after therapy with trastuzumab resulted not amplified in MS. Conclusions: According to other authors, our data demonstrated that the lost of HER/Neu amplification in MS is a possible event and that FNA samples of MS are available for HER/Neu detection.

scholarly journals Comprehensive characterization of distinct genetic alterations in metastatic breast cancer across various metastatic sites

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Soojin Cha ◽  
Esak Lee ◽  
Hong-Hee Won
Keyword(s):  
Breast Cancer ◽  
Metastatic Breast Cancer ◽  
Large Scale ◽  
Metastatic Breast ◽  
Genetic Alterations ◽  
Driver Mutations ◽  
Site Specific ◽  
Metastatic Site ◽  
Treatment Naïve ◽  
Metastatic Sites

AbstractMetastasis is the major cause of death in breast cancer patients. Although previous large-scale analyses have identified frequently altered genes specific to metastatic breast cancer (MBC) compared with those in primary breast cancer (PBC), metastatic site-specific altered genes in MBC remain largely uncharacterized. Moreover, large-scale analyses are required owing to the low expected frequency of such alterations, likely caused by tumor heterogeneity and late dissemination of breast cancer. To clarify MBC-specific genetic alterations, we integrated publicly available clinical and mutation data of 261 genes, including MBC drivers, from 4268 MBC and 5217 PBC patients from eight different cohorts. We performed meta-analyses and logistic regression analyses to identify MBC-enriched genetic alterations relative to those in PBC across 15 different metastatic site sets. We identified 11 genes that were more frequently altered in MBC samples from pan-metastatic sites, including four genes (SMARCA4, TSC2, ATRX, and AURKA) which were not identified previously. ARID2 mutations were enriched in treatment-naïve de novo and post-treatment MBC samples, compared with that in treatment-naïve PBC samples. In metastatic site-specific analyses, associations of ESR1 with liver metastasis and RICTOR with bone metastasis were significant, regardless of intrinsic subtypes. Among the 15 metastatic site sets, ESR1 mutations were enriched in the liver and depleted in the lymph nodes, whereas TP53 mutations showed an opposite trend. Seven potential MBC driver mutations showed similar preferential enrichment in specific metastatic sites. This large-scale study identified new MBC genetic alterations according to various metastatic sites and highlights their potential role in breast cancer organotropism.

Breast Cancer Subtype Classification Using Multi-omics Data Integration Based on Neural Network

2020 ◽  
Vol 47 (9) ◽  
pp. 835-841
Author(s):  
Joungmin Choi ◽  
Jiyoung Lee ◽  
Jieun Kim ◽  
Jihyun Kim ◽  
Heejoon Chae
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Data Integration ◽  
Breast Cancer Subtype ◽  
Omics Data ◽  
Subtype Classification ◽  
Cancer Subtype ◽  
Omics Data Integration

scholarly journals A Knowledge-Aware Attentional Reasoning Network for Recommendation

2020 ◽  
Vol 34 (04) ◽  
pp. 6999-7006 ◽  
Author(s):  
Qiannan Zhu ◽  
Xiaofei Zhou ◽  
Jia Wu ◽  
Jianlong Tan ◽  
Li Guo
Keyword(s):  
Neural Network ◽  
Recommendation Systems ◽  
Experimental Results ◽  
Potential User ◽  
Knowledge Graph ◽  
Knowledge Graphs ◽  
Candidate Item

Knowledge-graph-aware recommendation systems have increasingly attracted attention in both industry and academic recently. Many existing knowledge-aware recommendation methods have achieved better performance, which usually perform recommendation by reasoning on the paths between users and items in knowledge graphs. However, they ignore the users' personal clicked history sequences that can better reflect users' preferences within a period of time for recommendation. In this paper, we propose a knowledge-aware attentional reasoning network KARN that incorporates the users' clicked history sequences and path connectivity between users and items for recommendation. The proposed KARN not only develops an attention-based RNN to capture the user's history interests from the user's clicked history sequences, but also a hierarchical attentional neural network to reason on paths between users and items for inferring the potential user intents on items. Based on both user's history interest and potential intent, KARN can predict the clicking probability of the user with respective to a candidate item. We conduct experiment on Amazon review dataset, and the experimental results demonstrate the superiority and effectiveness of our proposed KARN model.

scholarly journals Solicitation of Knowledge Graph Enhanced Neural Network Objects Detection by Sentiment Analysis

2021 ◽  
Vol 04 (11) ◽  
Author(s):  
Yacouba Conde ◽  
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Knowledge Graph ◽  
Machine Learning Technique ◽  
Negative Attitudes ◽  
Learning Technique ◽  
Structured Knowledge ◽  
Knowledge Graphs ◽  
N Gram ◽  
Objects Detection

In the machine learning technique, the knowledge graph is advancing swiftly; however, the basic models are not able to grasp all the affluence of the script that comes from the different personal web graphics, social media, ads, and diaries, etc., ignoring the semantic of the basic text identification. The knowledge graph provides a real way to extract structured knowledge from the texts and desire images of neural network, to expedite their semantics examination. In this study, we propose a new hybrid analytic approach for sentiment evaluation based on knowledge graphs, to identify the polarity of sentiment with positive and negative attitudes in short documents, particularly in 4 chirps. We used the tweets graphs, then the similarity of graph highlighted metrics and algorithm classification pertain sentimentality pre-dictions. This technique facilitates the explicability and clarifies the results in the knowledge graph. Also, we compare our differentiate the embeddings n-gram based on sentiment analysis and the result is indicated that our study can outperform classical n-gram models, with an F1-score of 89% and recall up to 90%.

Development and validation of natural language processing (NLP) algorithm for detection of distant versus local breast cancer recurrence and metastatic site.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 2043-2043
Author(s):  
Yasmin Karimi ◽  
Douglas W. Blayney ◽  
Allison W. Kurian ◽  
Daniel Rubin ◽  
Imon Banerjee
Keyword(s):  
Breast Cancer ◽  
Sensitivity And Specificity ◽  
Language Processing ◽  
Large Scale ◽  
Cancer Recurrence ◽  
Distant Recurrence ◽  
Breast Cancer Recurrence ◽  
Metastatic Site ◽  
Radiology Reports ◽  
Metastatic Sites

2043 Background: Electronic health records (EHR) are used for retrospective cancer outcomes analysis. Sites and timing of recurrence are not captured in structured EHR data. Novel computerized methods are necessary to use unstructured longitudinal EHR data for large scale studies. Methods: We previously developed a neural network-based NLP algorithm to identify no recurrence vs. metastatic recurrence cases by analyzing physician notes, pathology and radiology reports in Stanford’s breast cancer database, Oncoshare (Cohort A). To validate this algorithm for local vs. distant recurrence, we identified a distinct Oncoshare cohort (Cohort B). Cases were manually curated for longitudinal development of local or distant recurrence and metastatic sites. A two-sided t-test was used to compare mean probabilities between local and distant recurrence cases. Next, we combined cases in Cohorts A and B to train and validate a novel NLP classifier that identifies metastatic site. The combined cohort was randomly divided into training and validation sets. Sensitivity and specificity were calculated for the NLP algorithm’s ability to detect metastatic sites compared to manual curation. Results: In Cohort B: 350 metastatic cases were identified. Mean probability for local and distant recurrence was 0.43 and 0.79, respectively and differed significantly for patients with local vs. distant recurrence (p<0.01). In Cohorts A and B: 632 metastatic cases were used for determination of sites. Sensitivity and specificity were highest for detection of peritoneal metastasis followed by liver, lung, skin, bone and central nervous system (table). Conclusions: This NLP algorithm is a scalable tool that uses unstructured EHR data to capture breast cancer recurrence, distinguishing local from distant recurrence and identifying metastatic site. This method may facilitate analysis of large datasets and correlation of outcomes with metastatic site. [Table: see text]

Review of Improvement of Web Search Based on Web Log File

2012 ◽  
Vol 3 (2) ◽  
pp. 298-300 ◽  
Author(s):  
Soniya P. Chaudhari ◽  
Prof. Hitesh Gupta ◽  
S. J. Patil
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Web Search ◽  
Pattern Mining ◽  
Efficiency Improvement ◽  
Web Searching ◽  
Important Method ◽  
Web Log ◽  
Journal Paper ◽  
Log File

In this paper we review various research of journal paper as Web Searching efficiency improvement. Some important method based on sequential pattern Mining. Some are based on supervised learning or unsupervised learning. And also used for other method such as Fuzzy logic and neural network

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