The Experts below are selected from a list of 26487 Experts worldwide ranked by ideXlab platform
Ramiz M. Aliguliyev - One of the best experts on this subject based on the ideXlab platform.
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Web Intelligence - Effective Summarization Method of Text Documents
The 2005 IEEE WIC ACM International Conference on Web Intelligence (WI'05), 1Co-Authors: Rasim M. Alguliev, Ramiz M. AliguliyevAbstract:In this paper, we propose text summarization method that creates text summary by definition of the Relevance Score of each sentence and extracting sentences from the original documents. While summarization this method takes into account weight of each sentence in the document. The essence of the method suggested is in preliminary identification of every sentence in the document with characteristic vector of words, which appear in the document, and calculation of Relevance Score for each sentence. The Relevance Score of sentence is determined through its comparison with all the other sentences in the document and with the document title by cosine measure. Prior to application of this method the scope of features is defined and then the weight of each word in the sentence is calculated with account of those features. The weights of features, influencing Relevance of words, are determined using genetic algorithms.
Jonathan K Pritchard - One of the best experts on this subject based on the ideXlab platform.
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inferring relevant cell types for complex traits by using single cell gene expression
American Journal of Human Genetics, 2017Co-Authors: Diego Calderon, Anand Bhaskar, David A Knowles, David E Golan, Towfique Raj, Jonathan K PritchardAbstract:Previous studies have prioritized trait-relevant cell types by looking for an enrichment of genome-wide association study (GWAS) signal within functional regions. However, these studies are limited in cell resolution by the lack of functional annotations from difficult-to-characterize or rare cell populations. Measurement of single-cell gene expression has become a popular method for characterizing novel cell types, and yet limited work has linked single-cell RNA sequencing (RNA-seq) to phenotypes of interest. To address this deficiency, we present RolyPoly, a regression-based polygenic model that can prioritize trait-relevant cell types and genes from GWAS summary statistics and gene expression data. RolyPoly is designed to use expression data from either bulk tissue or single-cell RNA-seq. In this study, we demonstrated RolyPoly's accuracy through simulation and validated previously known tissue-trait associations. We discovered a significant association between microglia and late-onset Alzheimer disease and an association between schizophrenia and oligodendrocytes and replicating fetal cortical cells. Additionally, RolyPoly computes a trait-Relevance Score for each gene to reflect the importance of expression specific to a cell type. We found that differentially expressed genes in the prefrontal cortex of individuals with Alzheimer disease were significantly enriched with genes ranked highly by RolyPoly gene Scores. Overall, our method represents a powerful framework for understanding the effect of common variants on cell types contributing to complex traits.
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inferring relevant cell types for complex traits using single cell gene expression
bioRxiv, 2017Co-Authors: Diego Calderon, Anand Bhaskar, David A Knowles, David E Golan, Towfique Raj, Jonathan K PritchardAbstract:Previous studies have prioritized trait-relevant cell types by looking for an enrichment of GWAS signal within functional regions. However, these studies are limited in cell resolution by the lack of functional annotations from difficult-to-characterize or rare cell populations. Measurement of single-cell gene expression has become a popular method for characterizing novel cell types, and yet, hardly any work exists linking single-cell RNA-seq to phenotypes of interest. To address this deficiency, we present RolyPoly, a regression-based polygenic model that can prioritize trait-relevant cell types and genes from GWAS summary statistics and single-cell RNA-seq. We demonstrate RolyPoly9s accuracy through simulation and validate previously known tissue-trait associations. We discover a significant association between microglia and late-onset Alzheimer9s disease, and an association between oligodendrocytes and replicating fetal cortical cells with schizophrenia. Additionally, RolyPoly computes a trait-Relevance Score for each gene which reflects the importance of expression specific to a cell type. We found that differentially expressed genes in the prefrontal cortex of Alzheimer9s patients were significantly enriched for highly ranked genes by RolyPoly gene Scores. Overall, our method represents a powerful framework for understanding the effect of common variants on cell types contributing to complex traits.
Yuhong Xiong - One of the best experts on this subject based on the ideXlab platform.
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ECIR - Using weighted tagging to facilitate enterprise search
Lecture Notes in Computer Science, 2010Co-Authors: Shengwen Yang, Jianming Jin, Yuhong XiongAbstract:Motivated by the success of social tagging in web communities, this paper proposes a novel document tagging method more suitable for the enterprise environment, named weighted tagging. The method allows users to tag a document with weighted tags which are then used as an additional source for the query matching and Relevance scoring to improve the search results. The method enables a user-driven search result ranking by adapting the Relevance Score of a search result through weighted tags based on user feedbacks. A prototype intranet search system has been built to demonstrate the viability of the method.
Rasim M. Alguliev - One of the best experts on this subject based on the ideXlab platform.
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Web Intelligence - Effective Summarization Method of Text Documents
The 2005 IEEE WIC ACM International Conference on Web Intelligence (WI'05), 1Co-Authors: Rasim M. Alguliev, Ramiz M. AliguliyevAbstract:In this paper, we propose text summarization method that creates text summary by definition of the Relevance Score of each sentence and extracting sentences from the original documents. While summarization this method takes into account weight of each sentence in the document. The essence of the method suggested is in preliminary identification of every sentence in the document with characteristic vector of words, which appear in the document, and calculation of Relevance Score for each sentence. The Relevance Score of sentence is determined through its comparison with all the other sentences in the document and with the document title by cosine measure. Prior to application of this method the scope of features is defined and then the weight of each word in the sentence is calculated with account of those features. The weights of features, influencing Relevance of words, are determined using genetic algorithms.
Hans Wildiers - One of the best experts on this subject based on the ideXlab platform.
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Real-time symptom management in the context of a remote symptom-monitoring system: prospective process evaluation and cross-sectional survey to explore clinical Relevance
Supportive Care in Cancer, 2021Co-Authors: Annemarie Coolbrandt, Kristof Muylaert, Evi Vandeneede, Christophe Dooms, Hans WildiersAbstract:Purpose Electronic systems for remotely monitoring symptoms during systemic anticancer treatment are increasingly being used. Some of these systems have features triggering alerts to healthcare professionals for worsening and/or severe symptoms, enabling real-time symptom management. This study aimed at exploring the characteristics and process of real-time alert management as well as its clinical Relevance as perceived by healthcare professionals. Methods From January until September 2019, a prospective process evaluation was set up to collect data on all alerts and their management. Also, an online survey presenting a selected number of cases was set up to explore oncologists’ and oncology nurses’ perceived clinical Relevance of the real-time management of the alerts. Results The overall incidence rate of alerts was 1.4%. Of 253 alerts, pain, fever, dyspnea, and nausea were the most prevalent symptoms triggering an alert. The majority of alerts was managed by a nursing telephone consult alone. In 25.3% of cases, clinical examination was deemed necessary to manage the alert. In 148 of the ratings, oncologists and oncology nurses (totally) agreed with the clinical Relevance of the real-time management (95.1%). The mean Relevance Score attached to the cases was 4.51 (±0.80). Conclusions The majority of alerts triggered by a mobile tool for remote symptom monitoring during cancer treatment can be managed by a telephone nursing consult and real-time management is evaluated as (very) relevant by the majority of clinicians.