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2017-6-26 Young Scholar Forum on Bioinformatics Computing

Type of Event: Young Scholar Forum on Bioinformatics Computing

Time: 08:30

Date: 2017-6-26

Venue: Academic Hall, School of Computer Science

Hosted by: School of Computer Science

Schedule of Event:

Schedule of Event:

Title of Lecture 1Discovery of mutateddriver pathways in cancer

Time2017-06-26 8:30-9:10

LecturerProf.Shihua Zhang

About the Lecture

    Large-scalecancer genomics projects are providing a large volume of data about genomic,epigenomic and gene expression aberrations in multiple cancer types. One of theremaining challenges is to identify driver mutations, driver genes and driverpathways promoting cancer proliferation and filter out the unfunctional andpassenger ones. In this talk, I will present a series of progresses about de novo mutated driver pathways or genesets identification from mutation data in cancer. Specifically, as to the so-calledmaximum weight submatrix problem, we first designed binary linear programmingmethod and genetic algorithm to solve it (MDPFinder). We further proposed anexact mathematical programming method denovo identify co-occurring mutated driver pathways (CoMDP) incarcinogenesis without any prior information beyond mutation profiles. Lastly,we proposed to computational methods to identify cancer common or specificmutated driver gene sets from mutation data of multiple cancers (ComMDP andSpeMDP). The efficiency of these methods were validated by testing on simulateddata. Tests on to several real biological datasets have been demonstrated toshow their effectiveness.

Profile of the Lecturer

    ShihuaZhang received the PhD degree in applied mathematics and bioinformatics fromthe Academy of Mathematics and Systems Science, Chinese Academy of Sciences in2008 with the highest honor. He joined the same institute as an AssistantProfessor in 2008. His research interests are mainly in bioinformatics and computationalbiology, network science, data mining, and machine learning. He has won variousawards and honors including NSFC for Excellent Young Scholars (2014),Outstanding Young Scientist Program of CAS (2014) and Youth Science andTechnology Award of China (2013). Now he serves as an Editorial Board Member orAssociate Editor of BMC Genomics, Scientific Reports, Current Bioinformaticsand Frontiers in Bioinformatics and Computational Biology, respectively. He isa member of the IEEE, ISCB and SIAM.




Title of Lecture 2Computational predictionof miRNA and miRNA-disease relationship

Time2017-06-26 9:10-9:50

LecturerProf.Quan Zou

About the Lecture

    microRNAis a kind of “star” molecular, and serves as a “director” since it can regulatethe expression of protein. In 2006, related works on gene silence won Nobelprice, which made miRNA be the hot topic in molecular genetics andbioinformatics. Mining miRNA and targets prediction are two classic topics incomputational miRNAnomics. In this talk, we focus on the miRNA mining problemsfrom machine learning views. We point out that the negative data is the keyproblem for decreasing the False Positive rather than exploring betterfeatures. miRNA-disease relationship prediction is another hot topic in recentyears. We introduce some novel network methods on calculating miRNA-miRNAsimilarity, which is the key issue for miRNA-disease relationship prediction. Someresults revealed that several novel miRNA could serve as the targets or markersfor some tumors.

Profile of the Lecturer

    Dr. QuanZou is a Professor of Computer Science at Tianjin University. He received hisPH.D. from Harbin Institute of Technology, P.R.China in 2009. From 2009 to2015, he is an assistant and associate professor in Xiamen University,P.R.China. His research is in the areas of bioinformatics, machine learning andparallel computing. Several related works have been published by Briefings inScience, Bioinformatics, Bioinformatics, IEEE/ACM Transactions on ComputationalBiology and Bioinformatics, etc. Google scholar showed that his more than 100papers have been cited more than 2000 times. He is the editor member ofScientific Reports and PLOS One. He is also a reviewer for many impactedjournals, including Bioinformatics, Briefings in Bioinformatics, IEEE/ACMTransactions on Computational Biology and Bioinformatics, etc. In Feb.2005, heis awarded as the Outstanding Reviewers for Computers in Biology and Medicine.He also serves as the Program Committee member for several internationalconferences.


Title of Lecture 3Predicting proteinsubmitochondrial locations: past, present and future

Time2017-06-26 9:50-10:30

LecturerProf.Pufeng Du

About the Lecture

    Predictingprotein subcellular location is a classical topic in bioinformatics. Predictingprotein submitochondrial locations goes one step further than the subcellularlocations. Over the past decades, we have carried out a serial of works inpredicting protein submitochondrial locations. In this talk, we will review thebiological fundamentals in predicting protein subcellular locations, introduceour works in predicting protein submitochondrial locations, and discuss the futuresin this research topics.

Profile of the Lecturer

    Dr.Pufeng Du received his bachelor degree and his Ph.D. from Tsinghua Universityin 2005 and 2010, respectively. During 2013 and 2014, he worked as a visitingpostdoc fellow in the City University of Hong Kong, supported by the HK ScholarProgram. He worked as an assistant professor in the School of Computer Scienceand Technology of Tianjin University from Jan. 2010 to Jun. 2013. He hasalready published over 30 academic papers, which have been cited for over 700times.


Title of Lecture 4Multi-label learning forprotein function prediction

Time2017-06-26 10:40-11:20

LecturerProf.Guoxian Yu

About the Lecture

    Proteinfunction prediction is one of key problems in Bioinformatics. Proteins oftenengages in different life process and can be annotated with differentfunctional labels. Current functional information of proteins have many missingannotations and some noisy annotations. In this lecture, I will first introducehow to model the complex protein function prediction task by multi-labellearning. Then, I introduce our progress on using multi-label weak-labellearning, multi-label partial label learning and structural functional labelsof Gene Ontology to replenish missing annotations, irrelevant annotations andnoisy annotations. In addition, we will also introduce our work on fusingmultiple data sources for protein function prediction.

Profile of the Lecturer

    GuoxianYu, he received PhD in Computer Application Technology from South ChinaUniversity of Technology, in 2013. From 2011-2013 he was financially supportedby China Scholarship Council to visit the Data Mining Lab at George MasonUniversity, and from 2014-2015, he was a Postdoc Fellow at Department ofComputer Science, Hong Kong Baptist University. He is a member of CAAIBioinformatics and Artificial Lift technical committee, a member of CCFBioinformatics, and communication member of CCF BigData, ArtificialIntelligence and Pattern Recognition. His research interests include MachineLearning, Data Mining and their applications for biological data analysis. Hepublished more 30 papers in prestigious journals and conferences (KDD, IJCAI,Bioinformatics, Pattern Reconition), actively serves as reviewers for thesejournals and conferences. He received the best paper award of ICMLC2011, bestposter award of doctoral forum of SDM2012, and excellent student paper (withhis student) award of CCML2015.


Title of Lecture 5Network-based methods forpredicting disease associated genes, ncRNAs, and drugs

Time2017-06-26 11:20-12:00

LecturerProf.Min Li

About the Lecture

    Advancesin genome-scale molecular biology and molecular genetics have greatly elevatedour knowledge on the basic components of human diseases. Human diseases arealways caused by complex mechanisms involving aberrations in numerous proteinsand pathways. The increase in availability of genetics and molecular biologydata allowed describing human disease in terms of genes, non-coding RNAs anddrugs. Built upon these recent technological and conceptual advances,network-based approaches as powerful tools have been proposed to study humandiseases. For these network-based methods, each biological system is consideredas a network model, we try to elaborate how the molecules, the interactions andthe network structures determine the functions of biological systems, which canhelp us understand the cellular organizations, processes and functions. In thistalk, some of our works about predicting potential associations fordrug-target, drug-disease, miRNA-disease and lncRNA-disease are presented.These studies are commonly based on assumption that similar biological entitiestend to show similar interaction patterns, i.e a group of similar biologicalentities usually related with other class of similar biological entities.

Profile of the Lecturer

    Min Li iscurrently a Professor of Computer Science at the School of Information Scienceand Engineering, Central South University, P. R. China. Her research interestsinclude Algorithms for Computational Biology and Bioinformatics, mainly focuson algorithms and tools in De novo genome assembly, protein interaction networkanalysis, and essential protein discovery, etc. She has published more than 60technical papers in refereed journals such as Bioinformatics, IEEE/ACMTransactions on Computational Biology and Bioinformatics, Proteomics, andMethods. According to Google scholar, her paper citations is more than 1900 andH-index is 23. She is a member of ACM, a member of IEEE, and a member of CCF.She is serving as the Editorial Board Member of Interdisciplinary Sciences:Computational Life Sciences and IJBRA. She is also serving as the Guest Editorof a number of refereed journals such as IEEE/ACM Transactions on ComputationalBiology and Bioinformatics, Neurocomputing, Complexity, BMC Bioinformatics, BMCGenomics, and Current Bioinformatics and as the Program Committee Chair orMember of several international conferences, including BIBM2011, ISBRA 2012,ICIC2013, ISBRA 2013, ISBRA 2014, ISBRA 2015, ISBRA 2016, BIBM2016, BNHD2016,CBC 2016, ISBRA 2017, CBC 2017, BIBM2017, ICPCSEE2017.

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