Papers by Md Mostafizur Rahman
Diabetes
Using bioinformatics network analysis, we identified complement-1q like-3 (C1ql3) as a secreted p... more Using bioinformatics network analysis, we identified complement-1q like-3 (C1ql3) as a secreted protein that affects pancreatic islet function in obese mice. Previously, we showed that the recombinant C1ql3 protein inhibits insulin secretion stimulated by exendin-4 (an agonist for GLP-1 receptor) from human and mouse islets. Currently, we are elucidating the role of C1ql3 in pancreatic β-cell function by using mice with β-cell-specific deletion of the C1ql3 (βKO) gene on a standard chow diet. C1ql3βKO mice showed significantly improved glucose clearance after oral glucose challenge than the control mice, whereas no difference in insulin sensitivity was observed. Moreover, the plasma insulin levels were significantly increased at 15 min after the glucose challenge in the C1ql3 βKO vs. control mice. However, islets isolated from C1ql3βKO significantly increased insulin secretion stimulated by cAMP and Exendin-4 than control islets. These results suggest that the loss of C1ql3 in β-cel...
Proceedings of the 35th Annual ACM Symposium on Applied Computing
The type of an entity is a key piece of information to understand what an entity is and how it re... more The type of an entity is a key piece of information to understand what an entity is and how it relates to other entities mentioned in a document. Search engine result pages (SERPs) often surface facts and entity type information from a background Knowledge Graph (KG) in response to queries that carry a semantic information need. In a KG, an entity usually holds multiple type properties. It is then important to, given an entity in a KG, rank entity types attached to the entity by relevance to a certain user and information need as not always the most popular type is the most informative within a textual context. In this paper we address the entity type ranking problem by means of KG embedding models. In our work, we show that entity type ranking can be seen as a special case of the KG completion problem. Embeddings can be learned from both the structural and probabilistic information of the entities. We propose a Representation Learning model for Type Ranking (RL-TRank) and the results of the structure embedding and the probabilistic embedding are combined to get the entity type ranking. Experimental results show that the accuracy of RL-TRank approaches outperform the state-ofthe-art type ranking models while, at the same time, being more efficient and scalable.
Diabetes
Insulin secretion from pancreatic β-cells occurs in a biphasic manner. The loss of insulin secret... more Insulin secretion from pancreatic β-cells occurs in a biphasic manner. The loss of insulin secretion, entailing the inability of insulin granules to fuse with the plasma membrane (PM) via the formation of the SNARE complex, is a key determinant in impaired glucose tolerance and development of type 2 diabetes (T2D) . Synaptotagmins (Syt) are calcium sensors that regulate insulin-containing granule fusion to the PM for insulin secretion. Syt9 is highly expressed in β-cells; however, its role in insulin secretion is not well-characterized. Our goal is to determine how Syt9 affects insulin secretion from pancreatic β-cells. Mice lacking Syt9 (Syt9-/-) have improved glucose clearance with no change in insulin action than wild-type control mice. Interestingly, Syt9-/- mice showed increased plasma insulin levels at 5 min and 15 min post-glucose challenge. Similarly, islets isolated from Syt9-/- mice demonstrate increased stimulation of insulin secretion in response to the early phase insul...
Knowledge graph (KG) is the most popular method for presenting knowledge in search engines and ot... more Knowledge graph (KG) is the most popular method for presenting knowledge in search engines and other natural-language processing (NLP) applications. However, KG remains incomplete, inconsistent, and not completely accurate. To deal with the challenges of KGs, many state-of-the-art models, such as TransE, TransH, and TransR, have been proposed. TransE and TransH use one semantic space for entities and relations, whereas TransR uses two different semantic spaces in its embedding model. An issue is that these proposed models ignore the category-specific projection of entities. For example, the entity “Washington” could belong to the person or location category depending on its context or relationships. An entity may therefore involve multiple types or aspects. Considering all entities in just one semantic space is therefore not a logical approach to building an effective model. In this paper, we propose TransET, which maps each entity based on its type. We can then apply any other exis...
IEICE Transactions on Information and Systems, 2020
Knowledge graph embedding aims to embed entities and relations of multi-relational data in low di... more Knowledge graph embedding aims to embed entities and relations of multi-relational data in low dimensional vector spaces. Knowledge graphs are useful for numerous artificial intelligence (AI) applications. However, they (KGs) are far from completeness and hence KG embedding models have quickly gained massive attention. Nevertheless, the state-of-the-art KG embedding models ignore the category specific projection of entities and the impact of entity types in relational aspect. For example, the entity "Washington" could belong to the person or location category depending on its appearance in a specific relation. In a KG, an entity usually holds many type properties. It leads us to a very interesting question: are all the type properties of an entity are meaningful for a specific relation? In this paper, we propose a KG embedding model TPRC that leverages entity-type properties in the relational context. To show the effectiveness of our model, we apply our idea to the TransE, TransR and TransD. Our approach outperforms other state-of-the-art approaches as TransE, TransD, DistMult and ComplEx. Another, important observation is: introducing entity type properties in the relational context can improve the performances of the original translation distance based models.
It should begin with the full title of the article. The abstract should not be more than 200 word... more It should begin with the full title of the article. The abstract should not be more than 200 words. The abstract should state the purpose of the study or investigation, basic procedures, main findings and principal conclusion.
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Papers by Md Mostafizur Rahman