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Articles 1 - 15 of 15
Full-Text Articles in Life Sciences
Foodmask: Real-Time Food Instance Counting, Segmentation And Recognition, Huu-Thanh Nguyen, Yu Cao, Chong-Wah Ngo, Wing-Kwong Chan
Foodmask: Real-Time Food Instance Counting, Segmentation And Recognition, Huu-Thanh Nguyen, Yu Cao, Chong-Wah Ngo, Wing-Kwong Chan
Research Collection School Of Computing and Information Systems
Food computing has long been studied and deployed to several applications. Understanding a food image at the instance level, including recognition, counting and segmentation, is essential to quantifying nutrition and calorie consumption. Nevertheless, existing techniques are limited to either category-specific instance detection, which does not reflect precisely the instance size at the pixel level, or category-agnostic instance segmentation, which is insufficient for dish recognition. This paper presents a compact and fast multi-task network, namely FoodMask, for clustering-based food instance counting, segmentation and recognition. The network learns a semantic space simultaneously encoding food category distribution and instance height at pixel basis. …
Non-Binary Evaluation Of Next-Basket Food Recommendation, Yue Liu, Palakorn Achananuparp, Ee-Peng Lim
Non-Binary Evaluation Of Next-Basket Food Recommendation, Yue Liu, Palakorn Achananuparp, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Next-basket recommendation (NBR) is a recommendation task that predicts a basket or a set of items a user is likely to adopt next based on his/her history of basket adoption sequences. It enables a wide range of novel applications and services from predicting next basket of items for grocery shopping to recommending food items a user is likely to consume together in the next meal. Even though much progress has been made in the algorithmic NBR research over the years, little research has been done to broaden knowledge about the evaluation of NBR methods, which is largely based on the …
Sibnet: Food Instance Counting And Segmentation, Huu-Thanh. Nguyen, Chong-Wah Ngo, Wing-Kwong Chan
Sibnet: Food Instance Counting And Segmentation, Huu-Thanh. Nguyen, Chong-Wah Ngo, Wing-Kwong Chan
Research Collection School Of Computing and Information Systems
Food computing has recently attracted considerable research attention due to its significance for health risk analysis. In the literature, the majority of research efforts are dedicated to food recognition. Relatively few works are conducted for food counting and segmentation, which are essential for portion size estimation. This paper presents a deep neural network, named SibNet, for simultaneous counting and extraction of food instances from an image. The problem is challenging due to varying size and shape of food as well as arbitrary viewing angle of camera, not to mention that food instances often occlude each other. SibNet is novel for …
Mixed Dish Recognition With Contextual Relation And Domain Alignment, Lixi Deng, Jingjing Chen, Chong-Wah Ngo, Qianru Sun, Sheng Tang, Yongdong Zhang, Tat-Seng Chua
Mixed Dish Recognition With Contextual Relation And Domain Alignment, Lixi Deng, Jingjing Chen, Chong-Wah Ngo, Qianru Sun, Sheng Tang, Yongdong Zhang, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Mixed dish is a food category that contains different dishes mixed in one plate, and is popular in Eastern and Southeast Asia. Recognizing the individual dishes in a mixed dish image is important for health related applications, e.g. to calculate the nutrition values of the dish. However, most existing methods that focus on single dish classification are not applicable to the recognition of mixed dish images. The main challenge of mixed dish recognition comes from three aspects: a wide range of dish types, the complex dish combination with severe overlap between different dishes and the large visual variances of same …
Integrating Node Embeddings And Biological Annotations For Genes To Predict Disease-Gene Associations, Sezin Kircali Ata, Le Ou-Yang, Yuan Fang, Chee-Keong Kwoh, Min Wu, Xiao-Li Li
Integrating Node Embeddings And Biological Annotations For Genes To Predict Disease-Gene Associations, Sezin Kircali Ata, Le Ou-Yang, Yuan Fang, Chee-Keong Kwoh, Min Wu, Xiao-Li Li
Research Collection School Of Computing and Information Systems
Background: Predicting disease causative genes (or simply, disease genes) has played critical roles in understandingthe genetic basis of human diseases and further providing disease treatment guidelines. While various computationalmethods have been proposed for disease gene prediction, with the recent increasing availability of biologicalinformation for genes, it is highly motivated to leverage these valuable data sources and extract useful information foraccurately predicting disease genes. Results: We present an integrative framework called N2VKO to predict disease genes. Firstly, we learn the nodeembeddings from protein-protein interaction (PPI) network for genes by adapting the well-known representationlearning method node2vec. Secondly, we combine the learned node …
Genomic Security (Lest We Forget), Tatiana Bradley, Xuhua Ding, Gene Tsudik
Genomic Security (Lest We Forget), Tatiana Bradley, Xuhua Ding, Gene Tsudik
Research Collection School Of Computing and Information Systems
Genomic privacy has attracted much attention from the research community, because its risks are unique and breaches can lead to terrifying leakage of sensitive information. The less-explored topic of genomic security must address threats of digitized genomes being altered, which can have dire consequences in medical or legal settings.
Integrating Water Exclusion Theory Into Β Contacts To Predict Binding Free Energy Changes And Binding Hot Spots, Qian Liu, Steven C. H. Hoi, Chee Keong Kwoh, Limsoon Wong, Jinyan Li
Integrating Water Exclusion Theory Into Β Contacts To Predict Binding Free Energy Changes And Binding Hot Spots, Qian Liu, Steven C. H. Hoi, Chee Keong Kwoh, Limsoon Wong, Jinyan Li
Research Collection School Of Computing and Information Systems
Binding free energy and binding hot spots at protein-protein interfaces are two important research areas for understanding protein interactions. Computational methods have been developed previously for accurate prediction of binding free energy change upon mutation for interfacial residues. However, a large number of interrupted and unimportant atomic contacts are used in the training phase which caused accuracy loss. Results: This work proposes a new method, β ACV ASA , to predict the change of binding free energy after alanine mutations. β ACV ASA integrates accessible surface area (ASA) and our newly defined β contacts together into an atomic contact vector …
Integrating Water Exclusion Theory Into Β Contacts To Predict Binding Free Energy Changes And Binding Hot Spots, Qian Liu, Steven C. H. Hoi, Chee Keong Kwoh, Limsoon Wong, Jinyan Li
Integrating Water Exclusion Theory Into Β Contacts To Predict Binding Free Energy Changes And Binding Hot Spots, Qian Liu, Steven C. H. Hoi, Chee Keong Kwoh, Limsoon Wong, Jinyan Li
Research Collection School Of Computing and Information Systems
Binding free energy and binding hot spots at protein-protein interfaces are two important research areas for understanding protein interactions. Computational methods have been developed previously for accurate prediction of binding free energy change upon mutation for interfacial residues. However, a large number of interrupted and unimportant atomic contacts are used in the training phase which caused accuracy loss. Results: This work proposes a new method, β ACV ASA , to predict the change of binding free energy after alanine mutations. β ACV ASA integrates accessible surface area (ASA) and our newly defined β contacts together into an atomic contact vector …
Beta Atomic Contacts: Identifying Critical Specific Contacts In Protein Binding Interfaces, Qian Lu, Chee Keong Kwoh, Steven C. H. Hoi
Beta Atomic Contacts: Identifying Critical Specific Contacts In Protein Binding Interfaces, Qian Lu, Chee Keong Kwoh, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Specific binding between proteins plays a crucial role in molecular functions and biological processes. Protein binding interfaces and their atomic contacts are typically defined by simple criteria, such as distance-based definitions that only use some threshold of spatial distance in previous studies. These definitions neglect the nearby atomic organization of contact atoms, and thus detect predominant contacts which are interrupted by other atoms. It is questionable whether such kinds of interrupted contacts are as important as other contacts in protein binding. To tackle this challenge, we propose a new definition called beta (β) atomic contacts. Our definition, founded on the …
B-Cell Epitope Prediction Through A Graph Model, Liang Zhao, Limsoon Wong, Lanyuan Lu, Steven C. H. Hoi, Jinyan Li
B-Cell Epitope Prediction Through A Graph Model, Liang Zhao, Limsoon Wong, Lanyuan Lu, Steven C. H. Hoi, Jinyan Li
Research Collection School Of Computing and Information Systems
Prediction of B-cell epitopes from antigens is useful to understand the immune basis of antibody-antigen recognition, and is helpful in vaccine design and drug development. Tremendous efforts have been devoted to this long-studied problem, however, existing methods have at least two common limitations. One is that they only favor prediction of those epitopes with protrusive conformations, but show poor performance in dealing with planar epitopes. The other limit is that they predict all of the antigenic residues of an antigen as belonging to one single epitope even when multiple non-overlapping epitopes of an antigen exist.
Structural Analysis Of The Hot Spots In The Binding Between H1n1 Ha And The 2di Antibody: Do Mutations Of H1n1 From 1918 To 2009 Affect Much On This Binding?, Qian Liu, Steven C. H. Hoi, Chinh T. T. Su, Zhenhua Li, Chee-Keong Kwoh, Limsoon Wong, Jinyan Li
Structural Analysis Of The Hot Spots In The Binding Between H1n1 Ha And The 2di Antibody: Do Mutations Of H1n1 From 1918 To 2009 Affect Much On This Binding?, Qian Liu, Steven C. H. Hoi, Chinh T. T. Su, Zhenhua Li, Chee-Keong Kwoh, Limsoon Wong, Jinyan Li
Research Collection School Of Computing and Information Systems
Worldwide and substantial mortality caused by the 2009 H1N1 influenza A has stimulated a new surge of research on H1N1 viruses. An epitope conservation has been learned in the HA1 protein that allows antibodies to cross-neutralize both 1918 and 2009 H1N1. However, few works have thoroughly studied the binding hot spots in those two antigen–antibody interfaces which are responsible for the antibody cross-neutralization. We apply predictive methods to identify binding hot spots at the epitope sites of the HA1 proteins and at the paratope sites of the 2D1 antibody. We find that the six mutations at the HA1's epitope from …
Beespace Navigator: Exploratory Analysis Of Gene Function Using Semantic Indexing Of Biological Literature, Moushumi Sen Sarma, David Arcoleo, Radhika S. Khetani, Brant Chee, Xu Ling, Xin He, Jing Jiang, Qiaozhu Mei, Chengxiang Zhai, Bruce Schatz
Beespace Navigator: Exploratory Analysis Of Gene Function Using Semantic Indexing Of Biological Literature, Moushumi Sen Sarma, David Arcoleo, Radhika S. Khetani, Brant Chee, Xu Ling, Xin He, Jing Jiang, Qiaozhu Mei, Chengxiang Zhai, Bruce Schatz
Research Collection School Of Computing and Information Systems
With the rapid decrease in cost of genome sequencing, the classification of gene function is becoming a primary problem. Such classification has been performed by human curators who read biological literature to extract evidence. BeeSpace Navigator is a prototype software for exploratory analysis of gene function using biological literature. The software supports an automatic analogue of the curator process to extract functions, with a simple interface intended for all biologists. Since extraction is done on selected collections that are semantically indexed into conceptual spaces, the curation can be task specific. Biological literature containing references to gene lists from expression experiments …
Experiences With Tracking The Effects Of Changing Requirements On Morphbank: A Web-Based Bioinformatics Application, Subhajit Datta, Robert Van Engelen, David Gaitros, Neelima Jammigumpula
Experiences With Tracking The Effects Of Changing Requirements On Morphbank: A Web-Based Bioinformatics Application, Subhajit Datta, Robert Van Engelen, David Gaitros, Neelima Jammigumpula
Research Collection School Of Computing and Information Systems
In this paper, we present a case study of applying the metrics Mutation Index, Component Set, Dependency Index on Morphbank- a web based bioinformatics application - to track the effects of changing requirements on a software system and suggest design modifications to mitigate such impact. Morphbank is "an open web repository of biological images documenting specimen-based research in comparative anatomy, morphological phylogenetics, taxonomy and related fields focused on increasing our knowledge about biodiversity". This paper discusses the context of the case study, analyzes the results, highlights observations and learning, and mentions directions of future work.
Prediction Of Rna-Binding Proteins From Primary Sequence By A Support Vector Machine Approach., Lian Yi Han, Cong Zhong Cai, Siaw Ling Lo, Maxey Chung, Yu Zong Chen
Prediction Of Rna-Binding Proteins From Primary Sequence By A Support Vector Machine Approach., Lian Yi Han, Cong Zhong Cai, Siaw Ling Lo, Maxey Chung, Yu Zong Chen
Research Collection School Of Computing and Information Systems
Elucidation of the interaction of proteins with different molecules is of significance in the understanding of cellular processes. Computational methods have been developed for the prediction of protein-protein interactions. But insufficient attention has been paid to the prediction of protein-RNA interactions, which play central roles in regulating gene expression and certain RNA-mediated enzymatic processes. This work explored the use of a machine learning method, support vector machines (SVM), for the prediction of RNA-binding proteins directly from their primary sequence. Based on the knowledge of known RNA-binding and non-RNA-binding proteins, an SVM system was trained to recognize RNA-binding proteins. A total …
Mining Of Correlated Rules In Genome Sequences, L. Lin, L. Wong, Tze-Yun Leong, P. S. Lai
Mining Of Correlated Rules In Genome Sequences, L. Lin, L. Wong, Tze-Yun Leong, P. S. Lai
Research Collection School Of Computing and Information Systems
With the huge amount of data collected by scientists in the molecular genetics community in recent years, there exists a need to develop some novel algorithms based on existing data mining techniques to discover useful information from genome databases. We propose an algorithm that integrates the statistical method, association rule mining, and classification rule mining in the discovery of allelic combinations of genes that are peculiar to certain phenotypes of diseased patients.