【新闻】电信学院国奖博士「大·揭·秘」

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由博士生工作委员会主办

每周一期

不看后悔

看了自卑

de

《国奖博士大揭秘》

咔咔咔~

本学期余额将要耗尽

请及时充电

上周的黄金节目

《直播接力》

各位大佬是不是还意犹未尽呢~~

什么？没看到？

是不是觉得自己

错过了一个亿呢？

 啊~别方~

善良的小编

本期为大家带来

电信学院的三位国奖博士

就问你们怕不怕？

····

好了

赤鸡的内容马上开始。

壹

鄂世嘉

邮箱       丨[e-mail]

导师       丨向阳

研究方向丨深度学习与自然语言处理

              丨知识图谱

获得奖项

• 全国知识图谱与语义计算大会（CCKS 2016）链接预测与元组分类评测竞赛：第2名（2/30）

• 全国知识图谱与语义计算大会（CCKS 2016）产品预测竞赛：第1名（1/21）

• 全国知识图谱与语义计算大会（CCKS 2017）问题命名实体识别评测竞赛：第6名（6/17）

• Conference on Natural Language Processing and Chinese Computing （NLPCC 2017）中文词语语义关系分类评测竞赛：第2名（2/17）

发表论文

[1] Yuan S, Xiang Y, Shijia E. Text Big Data Content Understanding and Development Trend Based on Feature Learning[J]. Big Data Research, 2015.

[2] 鄂世嘉, 林培裕, 向阳. 自动化构建的中文知识图谱系统[J]. 计算机应用, 2016, 36(4): 992-996.

[3] Huang Z, Shijia E, Zhang J, et al. Pairwise learning to recommend with both users’ and items’ contextual information[J]. IET Communications, 2016, 10(16): 2084-2090. (SCI)

[4] Shijia E, Jia S, Xiang Y, et al. Knowledge Graph Embedding for Link Prediction and Triplet Classification[M]//Knowledge Graph and Semantic Computing: Semantic, Knowledge, and Linked Big Data. Springer Singapore, 2016: 228-232. (EI)

[5] Shijia E, Xiang Y. Product Prediction with Deep Neural Networks[M]//Knowledge Graph and Semantic Computing: Semantic, Knowledge, and Linked Big Data. Springer Singapore, 2016: 243-247. (EI)

[6] Huang Z, Zhao Z, Shijia E, et al. PRACE: A Taxi Recommender for Finding Passengers with Deep Learning Approaches[C]//International Conference on Intelligent Computing. Springer, Cham, 2017: 759-770. (EI)

[7] Shijia E, Xiang Y. Entity Search Based on the Representation Learning Model With Different Embedding Strategies[J]. IEEE Access, 2017, 5: 15174-15183. (SCI)

[8] Shijia E, Xiang Y. Chinese Named Entity Recognition with Character-Word Mixed Embedding. CIKM 2017. （accepted, CCF B类）

[9] Shijia E, Jia S. Study on the Chinese Word Semantic Relation Classification with Word Embedding. NLPCC 2017. （accepted, CCF C类）

最新发表论文及摘要

题目：Entity Search Based on the Representation Learning Model With Different Embedding Strategies

摘要：We focus on the problem of learning distributed representations for entity search queries, named entities, and their short descriptions. With our representation learning models, the entity search query, named entity, and description can be represented as low-dimensional vectors with minimal human preprocessing. Our goal is to develop a simple but effective model that can make the distributed representations of query-related entities similar to the query representation in the vector space. Hence, we propose three kinds of learning strategies, and the difference between them mainly lies in how to deal with the relationship between an entity and its description. We analyze the strengths and weaknesses of each learning strategy and validate our methods on public data sets, which contain various query types and different languages (i.e., English and Chinese). The experimental results indicate that our proposed methods can adapt to different types of entity search queries, and outperform the current state-of-the-art methods no matter the entity collection is homogeneous or heterogeneous. Besides, the proposed methods can be trained fast and can be easily extended to other similar tasks.

网址：http://ieeexplore.ieee.org/document/7990482/

鄂学长，有什么寄语想送给迷弟迷妹们？

你想拥有什么就追求什么！

坚持自己的想法，并为之用心付出！

哇！我想先赚它一个亿……

贰

REC

李睿祎

邮箱       丨 [e-mail]

导师       丨关佶红

研究方向丨生物信息学、数据挖掘

获得奖项

      同济大学2017年新生国家奖学金

发表论文

[1] Qin G M, Li R Y, Zhao X M. Inferring the miRNA-disease associations based on domain-disease associations[J]. IFAC-PapersOnLine, 2015, 48(28):7-11.

[2] Qin G M, Li R Y, Zhao X M. Identifying disease associated miRNAs based on protein domains[J]. IEEE/ACM Trans Comput Biol Bioinform, 2016, PP (99):1-1.

[3] Qin, Gui-Min, Rui-Yi Li, and Xing-Ming Zhao. "PhosD: inferring kinase–substrate interactions based on protein domains." Bioinformatics 33.8 (2016): 1197-1204.

最新发表论文及摘要

题目：PhosD: inferring kinase–substrate interactions based on protein domains

摘要：

Motivation: Identifying the kinase-substrate relationships is vital to understanding the phosphorylation events and various biological processes, especially signal transductions. Although large amount of phosphorylation sites have been detected, unfortunately, it is rarely known which kinases activate those sites. Despite distinct computational approaches have been proposed to predict the kinase-substrate interactions, the prediction accuracy still needs to be improved.

Results: In this paper, we propose a novel probabilistic model named as PhosD to predict kinase-substrate relationships based on protein domains with the assumption that kinase-substrate interactions are accomplished with kinase-domain interactions. By further taking into account protein-protein interactions, our PhosD outperforms other popular approaches on several benchmark datasets with higher precision. In addition, some of our predicted kinase-substrate relationships are validated by signaling pathways, indicating the predictive power of our approach. Furthermore, we notice that given a kinase, the more substrates are known for the kinase the more accurate its predicted substrates will be, and the domains involved in kinase-substrate interactions are found to be more conserved across proteins phosphorylated by multiple kinases. These findings can help develop more efficient computational approaches in the future.

网址：

https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btw792

 漂酿小姐姐，有什么寄语想送给屏幕前的朋友们？

心静而后定，心定而后慧，慧而后可明心。

哇！我先捋一捋……

叁

朱广辉

邮箱       丨[e-mail]

导师       丨吴俊

研究方向丨计算机在生物数据挖掘的

              丨应用

获得奖项

    2015-2016光华奖学金

发表论文

CSTEA: a webserver for the Cell State Transition Expression Atlas

2017.07 SCI收录 IF10.162 一作

A survey on biomarker identification based on molecular networks

2016.12 期刊论文 已发表 一作

PPIM: A protein-protein interaction database for Maize

2016.01 SCI收录 一作 IF6.28

,第一作者,Identification of gene and microRNA signatures for oral cancer developed from oral leukoplakia

2015.05 SCI收录 一作 IF2.45

2015.04 SCI收录IF第三作者,Identifying cancer-related microRNAs based on gene expression data

2015.05 SCI收录 三作 IF7.3

最新发表论文及摘要

题目：CSTEA: a webserver for the Cell State Transition Expression Atlas

摘要：Cell state transition is one of the fundamental events in the development of multicellular organisms, and the transition trajectory path has recently attracted much attention. With the accumulation of large amounts of "-omics" data, it is becoming possible to get insights into the molecule mechanisms underlying the transitions between cell states. Here, we present CSTEA (Cell State Transition Expression Atlas), a webserver that organizes, analyzes and visualizes the time-course gene expression data during cell differentiation, cellular reprogramming and trans-differentiation in human and mouse. In particular, CSTEA defines gene signatures for uncharacterized stages during cell state transitions, thereby enabling both experimental and computational biologists to better understand the mechanisms of cell fate determination in mammals. To our best knowledge, CSTEA is the first webserver dedicated to the analysis of time-series gene expression data during cell state transitions. CSTEA is freely available at http://comp-sysbio.org/cstea/.

网址：https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkx402

大神大神，有什么寄语想送给科研小白们？

生命不息，科研不止~

大吉大利，晚上吃鸡？！

“

平凡的人都是一样的平凡，

优秀的人却各有各的优秀。

”

三位国奖得主的实力

有没有惊艳到你呢

~

那在羡慕之余

我们也应该想到

优秀，向来与努力并存。

~

话不多说，

最后祝电视机前的

各位仙女/男们

期末愉快！！！

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