ORCID Profile
0000-0002-8660-3608
Current Organisations
Beijing Institute of Technology
,
Open University
,
Tianjin University
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Information Storage, Retrieval And Management | Information Systems | Global Information Systems | Computation Theory And Mathematics Not Elsewhere Classified | Information Systems Development Methodologies | Computational Linguistics | Learning, Memory, Cognition And Language | Psychology | Sensory Processes, Perception And Performance | Library and Information Studies | Decision Support And Group Support Systems | Speech Recognition | Database Management
ANZSRC Socio-Economic Objective (SEO)
Information processing services | Library and related information services | Application tools and system utilities | Application packages | Computer software and services not elsewhere classified | Behavioural and cognitive sciences | Hearing, vision, speech and their disorders |
Related Links
Publications
Comparing Dissimilarity Measures for Content-Based Image Retrieval
Publisher: Springer Berlin Heidelberg
Date: 2008
Towards Contrastive Context-Aware Conversational Emotion Recognition
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2022
A study of document weight smoothness in pseudo relevance feedback
Publisher: Springer Berlin Heidelberg
Date: 2010
Query expansion using term relationships in language models for information retrieval
Publisher: ACM
Date: 31-10-2005
User-Adaptive Sketch-Based 3-D CAD Model Retrieval
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2013
Gut health, the microbiome and dietary choices: An exploration of consumer perspectives.
Publisher: Wiley
Date: 11-10-2022
Abstract: To explore consumer perceptions regarding dietary behaviours related to the gut microbiome, to assist in effective translation of research to practice. Online focus groups were conducted (adults with no formal medical or nutrition training). Semi-structured open-ended questioning explored perspectives related to gut health and dietary behaviours. A qualitative descriptive analysis approach was undertaken in duplicate. Fourteen focus groups were conducted (n = 38, 15 males, 23 females). Four overarching themes regarding consumer perceptions were identified. These were (a) gut health equates with wellbeing, (b) there are ergent perceptions of how diet influences gut health, (c) interest in scientific evidence does not necessarily influence dietary behaviour and (d) gastrointestinal symptoms influence dietary behaviour. Consumers are interested in gut health and understand that diet may be important. Given that current literature regarding diet and gut health does not differ from dietary guidelines, consumer interest may provide a timely slant to promote longstanding guidelines. Consumer education to limit scepticism around government messaging, including utilisation of social media by nutrition professionals, may be key to improving adherence to guidelines.
Learning to diversify web search results with a Document Repulsion Model
Publisher: Elsevier BV
Date: 10-2017
An Information Foraging Theory Based User Study of an Adaptive User Interaction Framework for Content-Based Image Retrieval
Publisher: Springer Berlin Heidelberg
Date: 2011
Pure high-order word dependence mining via information geometry
Publisher: Springer Berlin Heidelberg
Date: 2011
Toward a semantic granularity model for domain-specific information retrieval
Publisher: Association for Computing Machinery (ACM)
Date: 07-2011
Abstract: Both similarity-based and popularity-based document ranking functions have been successfully applied to information retrieval (IR) in general. However, the dimension of semantic granularity also should be considered for effective retrieval. In this article, we propose a semantic granularity-based IR model that takes into account the three dimensions, namely similarity, popularity, and semantic granularity, to improve domain-specific search. In particular, a concept-based computational model is developed to estimate the semantic granularity of documents with reference to a domain ontology. Semantic granularity refers to the levels of semantic detail carried by an information item. The results of our benchmark experiments confirm that the proposed semantic granularity based IR model performs significantly better than the similarity-based baseline in both a bio-medical and an agricultural domain. In addition, a series of user-oriented studies reveal that the proposed document ranking functions resemble the implicit ranking functions exercised by humans. The perceived relevance of the documents delivered by the granularity-based IR system is significantly higher than that produced by a popular search engine for a number of domain-specific search tasks. To the best of our knowledge, this is the first study regarding the application of semantic granularity to enhance domain-specific IR.
Rebuilding Visual Vocabulary via Spatial-temporal Context Similarity for Video Retrieval
Publisher: Springer International Publishing
Date: 2014
Back to the future: A logical framework for temporal information representation and inferencing from financial news
Publisher: IEEE
Date: 2003
An adaptive contextual quantum language model
Publisher: Elsevier BV
Date: 08-2016
Mining pure high-order word associations via information geometry for information retrieval
Publisher: Association for Computing Machinery (ACM)
Date: 07-2013
Abstract: The classical bag-of-word models for information retrieval (IR) fail to capture contextual associations between words. In this article, we propose to investigate pure high-order dependence among a number of words forming an unseparable semantic entity, that is, the high-order dependence that cannot be reduced to the random coincidence of lower-order dependencies. We believe that identifying these pure high-order dependence patterns would lead to a better representation of documents and novel retrieval models. Specifically, two formal definitions of pure dependence—unconditional pure dependence (UPD) and conditional pure dependence (CPD)—are defined. The exact decision on UPD and CPD, however, is NP-hard in general. We hence derive and prove the sufficient criteria that entail UPD and CPD, within the well-principled information geometry (IG) framework, leading to a more feasible UPD/CPD identification procedure. We further develop novel methods for extracting word patterns with pure high-order dependence. Our methods are applied to and extensively evaluated on three typical IR tasks: text classification and text retrieval without and with query expansion.
Characterizing pure high-order entanglements in lexical semantic spaces via information geometry
Publisher: Springer Berlin Heidelberg
Date: 2009
Towards Operational Abduction from a Cognitive Perspective
Publisher: Oxford University Press (OUP)
Date: 03-2006
Dimensionality reduction for dimension-specific search
Publisher: ACM
Date: 23-07-2007
Combining Interaction and Content for Feedback-Based Ranking
Publisher: Springer Berlin Heidelberg
Date: 2011
Facilitating query decomposition in query language modeling by association rule mining using multiple sliding windows
Publisher: Springer Berlin Heidelberg
Date: 2008
Investigating query-drift problem from a novel perspective of photon polarization
Publisher: Springer Berlin Heidelberg
Date: 2011
Community Relation Discovery by Named Entities
Publisher: IEEE
Date: 2007
Introduction to special issue on the second international conference on the theory of information retrieval
Publisher: Springer Science and Business Media LLC
Date: 27-08-2010
Dissimilarity measures for content-based image retrieval
Publisher: IEEE
Date: 06-2008
Investigating Bell Inequalities for Multidimensional Relevance Judgments in Information Retrieval
Publisher: Springer International Publishing
Date: 2019
Improving content-based image retrieval by identifying least and most correlated visual words
Publisher: Springer Berlin Heidelberg
Date: 2012
Generalized analysis of a distribution separation method
Publisher: MDPI AG
Date: 13-04-2016
DOI: 10.3390/E18040105
Bipartite entanglement and hypergraph states
Publisher: Springer Science and Business Media LLC
Date: 30-11-2014
Enabling Effective User Interactions in Content-Based Image Retrieval
Publisher: Springer Berlin Heidelberg
Date: 2009
How different features contribute to the session search?
Publisher: Springer International Publishing
Date: 2015
Investigating Non-classical Correlations Between Decision Fused Multi-modal Documents
Publisher: Springer International Publishing
Date: 2019
Word Graph Network: Understanding Obscure Sentences on Social Media for Violation Comment Detection
Publisher: Springer International Publishing
Date: 2020
Improving search personalisation with dynamic group formation
Publisher: ACM
Date: 03-07-2014
Relevance in Technicolor
Publisher: Springer Berlin Heidelberg
Date: 2010
An aspect query language model based on query decomposition and high-order contextual term associations
Publisher: Wiley
Date: 02-2012
Toward a fuzzy domain ontology extraction method for adaptive e-learning
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2009
A Survey of Controllable Text Generation Using Transformer-based Pre-trained Language Models
Publisher: Association for Computing Machinery (ACM)
Date: 06-10-2023
DOI: 10.1145/3617680
Abstract: Controllable Text Generation (CTG) is emerging area in the field of natural language generation (NLG). It is regarded as crucial for the development of advanced text generation technologies that better meet the specific constraints in practical applications. In recent years, methods using large-scale pre-trained language models (PLMs), in particular the widely used transformer-based PLMs, have become a new paradigm of NLG, allowing generation of more erse and fluent text. However, due to the limited level of interpretability of deep neural networks, the controllability of these methods need to be guaranteed. To this end, controllable text generation using transformer-based PLMs has become a rapidly growing yet challenging new research hotspot. A erse range of approaches have emerged in the recent 3-4 years, targeting different CTG tasks that require different types of controlled constraints. In this paper, we present a systematic critical review on the common tasks, main approaches, and evaluation methods in this area. Finally, we discuss the challenges that the field is facing, and put forward various promising future directions. To the best of our knowledge, this is the first survey paper to summarize the state-of-the-art CTG techniques from the perspective of Transformer-based PLMs. We hope it can help researchers and practitioners in the related fields to quickly track the academic and technological frontier, providing them with a landscape of the area and a roadmap for future research.
Extending the Extreme Physical Information to Universal Cognitive Models via a Confident Information First Principle
Publisher: MDPI AG
Date: 07-2014
DOI: 10.3390/E16073670
Why did you pick that? Visualising relevance criteria in exploratory search
Publisher: Springer Science and Business Media LLC
Date: 06-2010
Integrating Document Features for Entity Ranking
Publisher: Springer Berlin Heidelberg
Date: 2008
Towards context sensitive information inference
Publisher: Wiley
Date: 22-01-2003
DOI: 10.1002/ASI.10213
CFN: A Complex-Valued Fuzzy Network for Sarcasm Detection in Conversations
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2021
SECC: A novel search engine interface with live chat channel
Publisher: ACM
Date: 07-07-2016
Words-of-interest selection based on temporal motion coherence for video retrieval
Publisher: ACM
Date: 24-07-2011
Modeling multi-query retrieval tasks using density matrix transformation
Publisher: ACM
Date: 09-08-2015
Multi-facets quality assessment of online opinionated expressions
Publisher: Springer Berlin Heidelberg
Date: 2011
Dimension-Specific Search for Multimedia Retrieval
Publisher: Springer Berlin Heidelberg
Date: 2009
Integrating multiple document features in language models for expert finding
Publisher: Springer Science and Business Media LLC
Date: 26-03-2009
A sequential latent topic-based readability model for domain-specific information retrieval
Publisher: Springer International Publishing
Date: 2015
Knowledge discovery for adaptive negotiation agents in e-marketplaces
Publisher: Elsevier BV
Date: 05-2008
An effective approach to verbose queries using a limited dependencies language model
Publisher: Springer Berlin Heidelberg
Date: 2009
Improving bag-of-visual-words model with spatial-temporal correlation for video retrieval
Publisher: ACM
Date: 29-10-2012
Learning from users' querying experience on intranets
Publisher: ACM
Date: 16-04-2012
Generalized bias-variance evaluation of TREC participated systems
Publisher: ACM
Date: 03-11-2014
Approximating true relevance distribution from a mixture model based on irrelevance data
Publisher: ACM
Date: 19-07-2009
Multipartite entanglement in Grover’s search algorithm
Publisher: Springer Science and Business Media LLC
Date: 10-01-2015
Novel local features with hybrid sampling technique for image retrieval
Publisher: ACM
Date: 26-10-2010
A latent variable model for query expansion using the Hidden Markov Model
Publisher: ACM
Date: 26-10-2008
Stance-level Sarcasm Detection with BERT and Stance-centered Graph Attention Networks
Publisher: Association for Computing Machinery (ACM)
Date: 18-05-2023
DOI: 10.1145/3533430
Abstract: Computational Linguistics (CL) associated with the Internet of Multimedia Things (IoMT)-enabled multimedia computing applications brings several research challenges, such as real-time speech understanding, deep fake video detection, emotion recognition, home automation, and so on. Due to the emergence of machine translation, CL solutions have increased tremendously for different natural language processing (NLP) applications. Nowadays, NLP-enabled IoMT is essential for its success. Sarcasm detection, a recently emerging artificial intelligence (AI) and NLP task, aims at discovering sarcastic, ironic, and metaphoric information implied in texts that are generated in the IoMT. It has drawn much attention from the AI and IoMT research community. The advance of sarcasm detection and NLP techniques will provide a cost-effective, intelligent way to work together with machine devices and high-level human-to-device interactions. However, existing sarcasm detection approaches neglect the hidden stance behind texts, thus insufficient to exploit the full potential of the task. Indeed, the stance, i.e., whether the author of a text is in favor of, against, or neutral toward the proposition or target talked in the text, largely determines the text’s actual sarcasm orientation. To fill the gap, in this research, we propose a new task: stance-level sarcasm detection (SLSD), where the goal is to uncover the author’s latent stance and based on it to identify the sarcasm polarity expressed in the text. We then propose an integral framework, which consists of Bidirectional Encoder Representations from Transformers (BERT) and a novel stance-centered graph attention networks (SCGAT). Specifically, BERT is used to capture the sentence representation, and SCGAT is designed to capture the stance information on specific target. Extensive experiments are conducted on a Chinese sarcasm sentiment dataset we created and the SemEval-2018 Task 3 English sarcasm dataset. The experimental results prove the effectiveness of the SCGAT framework over state-of-the-art baselines by a large margin.
Beyond Redundancies: A Metric-Invariant Method for Unsupervised Feature Selection
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2010
DOI: 10.1109/TKDE.2009.84
A Survey of Quantum-cognitively Inspired Sentiment Analysis Models
Publisher: Association for Computing Machinery (ACM)
Date: 26-08-2023
DOI: 10.1145/3604550
Abstract: Quantum theory, originally proposed as a physical theory to describe the motions of microscopic particles, has been applied to various non-physics domains involving human cognition and decision-making that are inherently uncertain and exhibit certain non-classical, quantum-like characteristics. Sentiment analysis is a typical ex le of such domains. In the last few years, by leveraging the modeling power of quantum probability (a non-classical probability stemming from quantum mechanics methodology) and deep neural networks, a range of novel quantum-cognitively inspired models for sentiment analysis have emerged and performed well. This survey presents a timely overview of the latest developments in this fascinating cross-disciplinary area. We first provide a background of quantum probability and quantum cognition at a theoretical level, analyzing their advantages over classical theories in modeling the cognitive aspects of sentiment analysis. Then, recent quantum-cognitively inspired models are introduced and discussed in detail, focusing on how they approach the key challenges of the sentiment analysis task. Finally, we discuss the limitations of the current research and highlight future research directions.
Modeling document features for expert finding
Publisher: ACM
Date: 26-10-2008
Using Dempster-Shafer's evidence theory for query expansion based on freebase knowledge
Publisher: Springer Berlin Heidelberg
Date: 2013
Gut microbiome responses to dietary intake of grain-based fibers with the potential to modulate markers of metabolic disease: a systematic literature review
Publisher: Oxford University Press (OUP)
Date: 28-12-2021
Abstract: Cereal fiber modulates the gut microbiome and benefits metabolic health. The potential link between these effects is of interest.0 The aim for this systematic review was to assess evidence surrounding the influence of cereal fiber intake on microbiome composition, microbiome ersity, short-chain fatty acid production, and risk factors for metabolic syndrome. The MEDLINE, PubMed, CINAHL, and Cochrane Library databases were searched systematically, and quality of studies was assessed using the Cochrane Risk of Bias 2.0 tool. Evidence relating to study design, dietary data collection, and outcomes was qualitatively synthesized on the basis of fiber type. Forty-six primary publications and 2 secondary analyses were included. Cereal fiber modulated the microbiome in most studies however, taxonomic changes indicated high heterogeneity. Short-chain fatty acid production, microbiome ersity, and metabolic-related outcomes varied and did not always occur in parallel with microbiome changes. Poor dietary data were a further limitation. Cereal fiber may modulate the gut microbiome however, evidence of the link between this and metabolic outcomes is limited. Additional research is required with a focus on robust and consistent methodology. PROSPERO registration no. CRD42018107117
Optimization of an integrated model for automatic reduction and expansion of long queries
Publisher: Springer Berlin Heidelberg
Date: 2013
Modelling time-aware search tasks for search personalisation
Publisher: ACM
Date: 18-05-2015
Exploring Ant Colony Optimisation for Adaptive Interactive Search
Publisher: Springer Berlin Heidelberg
Date: 2011
Enhancing music information retrieval by incorporating image-based local features
Publisher: Springer Berlin Heidelberg
Date: 2012
Developing Position Structure-Based Framework for Chinese Entity Relation Extraction
Publisher: Association for Computing Machinery (ACM)
Date: 09-2011
Abstract: Relation extraction is the task of finding semantic relations between two entities in text, and is often cast as a classification problem. In contrast to the significant achievements on English language, research progress in Chinese relation extraction is relatively limited. In this article, we present a novel Chinese relation extraction framework, which is mainly based on a 9-position structure. The design of this proposed structure is motivated by the fact that there are some obvious connections between relation types/subtypes and position structures of two entities. The 9-position structure can be captured with less effort than applying deep natural language processing, and is effective to relieve the class imbalance problem which often hurts the classification performance. In our framework, all involved features do not require Chinese word segmentation, which has long been limiting the performance of Chinese language processing. We also utilize some correction and inference mechanisms to further improve the classified results. Experiments on the ACE 2005 Chinese data set show that the 9-position structure feature can provide strong support for Chinese relation extraction. As well as this, other strategies are also effective to further improve the performance.
Incorporating Intra‐Query Term Dependencies in an Aspect Query Language Model
Publisher: Wiley
Date: 26-11-2014
DOI: 10.1111/COIN.12058
Quantum Theory-inspired search
Publisher: Elsevier BV
Date: 2011
Bias-variance decomposition of IR evaluation
Publisher: ACM
Date: 28-07-2013
M3GAT: A Multi-modal, Multi-task Interactive Graph Attention Network for Conversational Sentiment Analysis and Emotion Recognition
Publisher: Association for Computing Machinery (ACM)
Date: 21-08-2023
DOI: 10.1145/3593583
Abstract: Sentiment and emotion, which correspond to long-term and short-lived human feelings, are closely linked to each other, leading to the fact that sentiment analysis and emotion recognition are also two interdependent tasks in natural language processing (NLP). One task often leverages the shared knowledge from another task and performs better when solved in a joint learning paradigm. Conversational context dependency, multi-modal interaction, and multi-task correlation are three key factors that contribute to this joint paradigm. However, none of the recent approaches have considered them in a unified framework. To fill this gap, we propose a multi-modal, multi-task interactive graph attention network, termed M3GAT, to simultaneously solve the three problems. At the heart of the model is a proposed interactive conversation graph layer containing three core sub-modules, which are: (1) local-global context connection for modeling both local and global conversational context, (2) cross-modal connection for learning multi-modal complementary and (3) cross-task connection for capturing the correlation across two tasks. Comprehensive experiments on three benchmarking datasets, MELD, MEISD, and MSED, show the effectiveness of M3GAT over state-of-the-art baselines with the margin of 1.88%, 5.37%, and 0.19% for sentiment analysis, and 1.99%, 3.65%, and 0.13% for emotion recognition, respectively. In addition, we also show the superiority of multi-task learning over the single-task framework.
Adaptation of the concept hierarchy model with search logs for query recommendation on intranets
Publisher: ACM
Date: 12-08-2012
Introduction to special issue on reasoning in natural language information processing
Publisher: Association for Computing Machinery (ACM)
Date: 12-2006
Abstract: For any applications related to Natural Language Processing (NLP), reasoning has been recognized as a necessary underlying aspect. Many of the existing work in NLP deals with specific NLP problems in a highly heuristic manner, yet not from an explicit reasoning perspective. Recently, there have been developments on models that allow reasoning in NLP such as language models, logical models, and so on. The goal of this special issue is to present high-quality contributions that integrate reasoning involved in different areas of natural language processing both at theoretical and/or practical levels. In this article, we give a brief overview on some major aspects of explicating reasoning in NLP and summarize the articles included in this special issue.
An intelligent information agent for document title classification and filtering in document-intensive domains
Publisher: Elsevier BV
Date: 11-2007
You have e-mail, what happens next? Tracking the eyes for genre
Publisher: Elsevier BV
Date: 2014
Bias-variance analysis in estimating true query model for information retrieval
Publisher: Elsevier BV
Date: 2014
Towards a belief-revision-based adaptive and context-sensitive information retrieval system
Publisher: Association for Computing Machinery (ACM)
Date: 03-2008
Abstract: In an adaptive information retrieval (IR) setting, the information seekers' beliefs about which terms are relevant or nonrelevant will naturally fluctuate. This article investigates how the theory of belief revision can be used to model adaptive IR. More specifically, belief revision logic provides a rich representation scheme to formalize retrieval contexts so as to disambiguate vague user queries. In addition, belief revision theory underpins the development of an effective mechanism to revise user profiles in accordance with information seekers' changing information needs. It is argued that information retrieval contexts can be extracted by means of the information-flow text mining method so as to realize a highly autonomous adaptive IR system. The extra bonus of a belief-based IR model is that its retrieval behavior is more predictable and explanatory. Our initial experiments show that the belief-based adaptive IR system is as effective as a classical adaptive IR system. To our best knowledge, this is the first successful implementation and evaluation of a logic-based adaptive IR model which can efficiently process large IR collections.
Deriving query suggestions for site search
Publisher: Wiley
Date: 30-07-2013
DOI: 10.1002/ASI.22901
A Real-Time Eye Tracking Based Query Expansion Approach via Latent Topic Modeling
Publisher: ACM
Date: 17-10-2015
Pre-heat treatment of carbonate precursor firstly in nitrogen and then oxygen atmospheres: A new procedure to improve tap density of high-performance cathode material Li1.167(Ni0.139Co0.139Mn0.556)O2
Publisher: Elsevier BV
Date: 10-2015
Analysis of Query Reformulations in a Search Engine of a Local Web Site
Publisher: Springer Berlin Heidelberg
Date: 2012
A Quantum Query Expansion Approach for Session Search
Publisher: MDPI AG
Date: 18-04-2016
DOI: 10.3390/E18040146
Automatically structuring domain knowledge from text: An overview of current research
Publisher: Elsevier BV
Date: 05-2012
Learning Adaptive Domain Models from Click Data to Bootstrap Interactive Web Search
Publisher: Springer Berlin Heidelberg
Date: 2012
Exploration of Quantum Interference in Document Relevance Judgement Discrepancy
Publisher: MDPI AG
Date: 18-04-2016
DOI: 10.3390/E18040144
A query expansion approach using entity distribution based on Markov random fields
Publisher: Springer International Publishing
Date: 2015
Contextual image annotation via projection and quantum theory inspired measurement for integration of text and visual features
Publisher: Springer Berlin Heidelberg
Date: 2011
Exploiting Term Co-occurrence for Enhancing Automated Image Annotation
Publisher: Springer Berlin Heidelberg
Date: 2009
Incorporating Seasonality into Search Suggestions Derived from Intranet Query Logs
Publisher: IEEE
Date: 08-2010
High-order concept associations mining and inferential language modeling for online review spam detection
Publisher: IEEE
Date: 12-2010
Enriching Query Flow Graphs with Click Information
Publisher: Springer Berlin Heidelberg
Date: 2011
A Quantum Probability Driven Framework for Joint Multi-Modal Sarcasm, Sentiment and Emotion Analysis
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Combining visual and textual systems within the context of user feedback
Publisher: Springer Berlin Heidelberg
Date: 2013
Concept Induction via Fuzzy C-Means Clustering on a High Dimensional Semantic Space
Publisher: Wiley
Date: 20-04-2007
QUADRATIC TSALLIS ENTROPY BIAS AND GENERALIZED MAXIMUM ENTROPY MODELS
Publisher: Wiley
Date: 04-07-2012
A study of collection-based features for adapting the balance parameter in pseudo relevance feedback
Publisher: Springer International Publishing
Date: 2015
Moving towards Adaptive Search in Digital Libraries
Publisher: Springer Berlin Heidelberg
Date: 2011
Towards fuzzy domain ontology based concept map generation for E-learning
Publisher: Springer Berlin Heidelberg
Date: 2008
A Four-Factor User Interaction Model for Content-Based Image Retrieval
Publisher: Springer Berlin Heidelberg
Date: 2009
On modeling rank-independent risk in estimating probability of relevance
Publisher: Springer Berlin Heidelberg
Date: 2011
Applying information foraging theory to understand user interaction with content-based image retrieval
Publisher: ACM
Date: 18-08-2010
Dyniqx: A novel meta-search engine for metadata based cross search
Publisher: IEEE
Date: 08-2008
Text based knowledge discovery with information flow analysis
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11610113_60
The functional and phenotypic diversity of single T-cell infiltrates in human colorectal cancer as correlated with clinical outcome
Publisher: Cold Spring Harbor Laboratory
Date: 28-09-2020
DOI: 10.1101/2020.09.27.313445
Abstract: Although degree of T-cell infiltration in CRC was shown to correlate with a positive prognosis, the contribution of phenotypically and functionally distinct T cell subtypes within tumors remains unclear. We analyzed 37,931 single T cells with respect to transcriptome, TCR sequence and 23 cell surface proteins, from tumors and adjacent normal colon of 16 patients. Our comprehensive analysis revealed two phenotypically distinct cytotoxic T cell populations within tumors, including positively prognostic effector memory cells and non-prognostic resident memory cells. These cytotoxic T cell infiltrates transitioned from effector memory to resident memory in a stage-dependent manner. We further defined several Treg subpopulations within tumors. While Tregs overall were associated with positive clinical outcomes, CD38 + peripherally-derived Tregs, phenotypically related to Th17 cells, correlated with poor outcomes independent of cancer stage. Thus, our data highlight the ersity of T cells in CRC and demonstrate the prognostic significance of distinct T cell subtypes, which could inform therapeutic strategies.
Robust Query-Specific Pseudo Feedback Document Selection for Query Expansion
Publisher: Springer Berlin Heidelberg
Date: 2008
Concept-based document readability in domain specific information retrieval
Publisher: ACM Press
Date: 2006
Integrating multiple windows and document features for expert finding
Publisher: Wiley
Date: 12-01-2009
DOI: 10.1002/ASI.21012
Related Organisations
Columbia University Columbia College
Location: United States of America
Robert Gordon University
Location: United Kingdom of Great Britain and Northern Ireland
Open University
Location: United Kingdom of Great Britain and Northern Ireland
Related Funding Activities
Discovery Projects - Grant ID: DP0343042
Start Date: 2003
End Date: 12-2005
Amount: $211,035.00
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP0663272
Start Date: 2006
End Date: 03-2010
Amount: $210,000.00
Funder: Australian Research Council
View Funded ActivitySpecial Research Initiatives - Grant ID: SR0354696
Start Date: 2004
End Date: 12-2004
Amount: $30,000.00
Funder: Australian Research Council
View Funded ActivityResearch Networks - Grant ID: RN0459895
Start Date: 09-2004
End Date: 12-2011
Amount: $1,600,000.00
Funder: Australian Research Council
View Funded ActivityResearch Networks - Grant ID: RN0460284
Start Date: 12-2004
End Date: 12-2010
Amount: $2,000,000.00
Funder: Australian Research Council
View Funded Activity