Saman HalgamugeResearch School of Engineering, The Australian National University, Australia Keynote speech: Drugs and the Brain: What can Analytics reveal in the Age of Data Engineering and Deep Learning? Biography & Abstract |
Jianzhu ChenKoch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, USA Keynote speech: Application of Informatics in Immunological Research Biography & Abstract |
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Alfonso ValenciaDepartment of Life Sciences, Barcelona Supercomputing Centre (BSC), Spain Keynote speech: Networks based approaches in epigenomics, evolution and biomedicine Biography & Abstract |
Mindy ShiDepartment of Bioinformatics and Genomics, University of North Carolina at Charlotte, USA Invited talk: An Integrative Approach Toward Predictive Modelling for Big Data Genomics Biography & Abstract |
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Xiujie WangInstitute of Genetics and Developmental Biology, Chinese Academy of Sciences, China Keynote speech: Using Bioinformatics to Identify New Regulatory Mechanisms Biography & Abstract |
Yong HouBGI-research, BGI-Shenzhen, China Invited talk: Single cell sequencing and its application in cancer research Biography & Abstract |
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Yuelong ShuSchool of Public Health (Shenzhen), Sun Yat-sen University, WHO Collaborating Center for Reference and Research on Influenza, China Keynote speech: Bioinformatics in the prevention and control of infectious diseases Biography & Abstract |
Limsoon WongSchool of Computing, National University of Singapore Keynote speech: Advancing clinical proteomics via analysis based on biological complexes Biography & Abstract |
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Biography: Saman Halgamuge, Fellow of the IEEE, is a Professor and the Director/Head of Research School of Engineering, The Australian National University. He previously held appointments as Professor and Associate Dean International at the University of Melbourne. He graduated with Dipl.-Ing and PhD degrees in Data Engineering (“Datentechnik”) from Technical University of Darmstadt, Germany and B.Sc. Engineering from University of Moratuwa, Sri Lanka. He is an Associate Editor of BMC Bioinformatics, IEEE Transactions on Circuits and Systems II and Applied Mathematics (Hindawi). His research that lead to 25o publications has been funded over the last 20 years by Australian Research Council (16 grants), National Health and Medical Research Council (2 grants), industry and other external organisations (13 grants or contracts) and funding to support stipends for 45 PhD students. His research record is in Data engineering, which includes Data Analytics and Optimization focusing on applications in Mechatronics, Energy, Biology and Medicine. He is a member of the Australian Research Council College of Experts panel for Engineering, Information and Computing Sciences. His publication profile is at Google Scholar. Drugs and the Brain: What can Analytics reveal in the Age of Data Engineering and Deep Learning?Abstract: The concept of Data Engineering or “Datentechnique” has been a popular specialisation of Electrical Engineering in Germany for several decades. It covers broadly algorithms, computing, electronic hardware, AI, Control, and Networking etc. In today’s context, Data Engineering can be considered as the integration of multiple types of sensing, networked control, AI, data analytics and electronic hardware. Deep Learning (DL), in particular the Unsupervised DL has been useful in applications of Bioinformatics in particular in Metagenomics but also increasingly in other areas of knowledge discovery [1,2,3]. The inadequately studied direct interaction between drugs and the brain and also the computational work on drug repositioning are such applications. This lack of knowledge left room for some patients to experiment on their own in some cases with dangerous substances as well as with cocktails of existing drugs, plant extracts etc. Several projects in speaker’s research group located in Australian National University and University of Melbourne focus on drug usage and the impact on the brain.Repositioning of existing drugs as appropriate medication for previously not associated medical conditions help reduce the time, costs and risks of drug development. Identification of drug groups either as clusters or subnetworks has already been used to simplify the visualization and interpretation of data for the purpose of drug repositioning. In the first part of the presentation a new Physarum-inspired Prize-Collecting Steiner Tree algorithm is used to solve this problem on Drug Similarity Networks (DSN) that are generated using the chemical, therapeutic, protein, and phenotype features of drugs [4]. In the second part, characterisation of drugs using Multi-Electrode Arrays (MEA) is discussed. MEA is an extracellular recording technology that enables the analysis of networks of neurons in vitro by producing “big data”. Neurons in culture exhibit a range of behavioural dynamics, which can be measured in terms of individual action potentials, network-wide synchronized firing and a host of other features that characterize network activity [5-6]. MEA data analysis is used to differentiate between two types of antiepileptic drugs with different mechanisms of action. It initially extracts features that characterise different aspects of neuronal activity that can be used to characterise network states. This utilises existing feature extraction methods as well as novel methods that are adaptive to activity patterns in unperturbed and perturbed network states. These features are then used to build network signatures that allow novel compounds to be compared with compounds with known mechanisms of action. This research demonstrates that MEA-based workflows can assist in rapid and efficient screening of pharmacological compounds, making them a useful addition to drug development pipelines. In the third part, developing new methods for modelling neurons to help identifying disease mechanisms leading to drug discovery is discussed [7]. The development of a “cell-computer hybrid system” to enable real-time modelling of neural conductance models is an on-going project. This real-time system enables accurate models to be built on as little as 1 second of recording data. The project conducted by PhD student Yadeesha and co-supervised by Prof Steve Petrou and his colleagues in the Florey Institute of Neuroscience and Mental Health incorporates the dynamic clamp; an electrophysiological method that enables “wetware in the loop” analysis for real-time interaction of our biological system with an in silico computer model. Acknowledgement: Prof Karin Verspoor of University of Melbourne and Prof Steve Petrou of Howard Florey Institute, former PhD students Isaam Saeed, Duleepa Jayasundara, Jayantha Siriwardena and current PhD students Yadeesha Deerasooriya, Dulini Mendis, Nusrath Hameed and Yahui Sun. Australian Research Council grants DP150103512 and LP140100670 partially supported this research. The following research papers cover the content of the presentation: [1] D Jayasundara, I Saeed, S Maheswararajah, BC Chang, SL Tang and S. K. Halgamuge, “ViQuaS: an improved reconstruction pipeline for viral quasispecies spectra generated by next-generation sequencing”, Bioinformatics 31 (6), 886-896, 2014. [2] I Saeed, SL Tang, SK Halgamuge, “Unsupervised discovery of microbial population structure within metagenomes using nucleotide base composition”, Nucleic acids research 40 (5), e34, 2011. [3] PN Hameed, K Verspoor, S Kusljic, S Halgamuge, “Positive-Unlabeled Learning for inferring drug interactions based on heterogeneous attributes” BMC bioinformatics 18 (1), 2017. [4] Y. Sun, N. Hameed, K. Verspoor and S. K. Halgamuge, “A Physarum-inspired Prize-Collecting Steiner Tree approach to identify subnetworks for drug repositioning”, BMC Systems Biology, 10 (5), 128, 2016. [5] G. D. C. Mendis, E. Morrisroe, S. Petrou, S.K. Halgamuge, “Use of adaptive network burst detection methods for multielectrode array data and the generation of artificial spike patterns for method evaluation", Journal of Neural Engineering, 2016, 13(2):026009. [6] G. D. C. Mendis, E. Morrisroe, C. A. Reid, S. K. Halgamuge and S. Petrou, "Use of local field potentials of dissociated cultures grown on multi-electrode arrays for pharmacological assays," 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, 2016, pp. 952-956. [7] Y. Deerasooriya, G. Berecki, S. Halgamuge and S. Petrou, “Real Cell-Computer Hybrid System”, http://www.cfne.unimelb.edu.au/news/real-cell/, Centre for Neural Engineering, University of Melbourne, 2017. |
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Biography: Jianzhu Chen is Professor of Biology at Koch Institute for Integrative Cancer Research and Department of Biology at Massachusetts Institute of Technology (MIT). He is also the lead Principle Investigator of the Infectious Disease Interdisciplinary Research Group of Singapore-MIT Alliance for Research and Technology (SMART). Dr. Chen’s research seeks fundamental understanding of the immune system as well as its application in disease intervention. Recently, his research activity has focused on developing humanized mouse technology for modeling human diseases with autologous immune system and therapeutic development. Dr. Chen received a B.S. degree from Wuhan University in China and a Ph.D. degree from Stanford University. He was a postdoctoral fellow and then an instructor at Harvard Medical School before he joined the faculty in the Department of Biology at MIT. Application of Informatics in Immunological ResearchAbstract: The availability of large amount of data in genomic, transcriptional, structural, pathway and network analyses have fundamentally changed how biomedical research is conducted. Informatics analysis has become an integral part of any biological research project by not only supporting analysis of large data set but also providing initial analysis of publically available data to generate hypothesis to be tested. In our study of transcriptional control of memory T cell development and maintenance, we first mined public data base of transcriptional profiles of naïve, effector and memory CD8 T cells. This analysis resulted in identification of transcription factors known to be involved in memory CD8 T cell development as well as new transcription factors whose role in memory T cell development is not known. We then tested the role of some transcription factors in memory T cell development in cell culture and in mouse models and obtained direct experimental support for these transcription factors in memory CD8 T cell development.Increasingly, biological data set is integrated with chemical data set in study of metabolism, physiology, and drug development. In this context, we have screened human macrophage responses to FDA-approved drugs, bioactive compounds and natural compounds (over 4,000). We identified around 300 compounds that can either polarize human macrophages to inflammatory (M1) or anti-inflammatory (M2) state. Again, informatics analysis helps to sort through the large data set. However, how to rationally narrow down 300 compounds to a dozen or so compounds that can be tested experimentally remains a challenge. Similarly, how to rationally identify specific compound that might be use for treating specific disease also requires more sophisticated informatics approaches. |
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Biography: Professor Alfonso Valencia has been recently appointed at the Barcelona Supercomputing Center - Centro Nacional de Supercomputación (BSC-CNS) as Director of the Life Sciences Department, with the support of the ICREA program. He is also the Director of the National Institute of Bioinformatics (Salud Carlos III Institute platform (INB-ISCIII) and node of ELIXIR the European Infrastructure of Bioinformatics), Founder and President of the International Society for Computational Biology and Co-Executive Director of the main journal in the field (Bioinformatics of Oxford University Press). Alfonso Valencia's research is centred in the area of Bioinformatics and Computational Biology. The computational methods for the genome analysis are particularly application to Precision Medicine. He has also worked in the development of computational methods for the prediction of protein structures and functions, the analysis biological networks and for modelling of molecular systems. These methods are based in the development of open and collaborative structures and are immersed in large international collaborative projects. Networks based approaches in epigenomics, evolution and biomedicineAbstract: In the first study, we processed heterogeneous ChIP‐Seq information to build a comprehensive genome co‐localization network of Chromatin Related Proteins (CRPs), histone marks and DNA modifications in mouse embryonic stems cells. In this network, co‐localization preferences can be translated into specific of “mESC Chromatin States”, such as active regions or enhancers. The study of the properties of the “co‐localization” network points to the 5hmC DNA modifications, as the key component in the organization of the mouseESC network. In a second network based study, the importance of 5hmC, as organizer of the epigenetic network, is reinforced by the evolutionary analysis of the protein components of the network. There, 5hmC acts as a mediator in the co‐evolution of the CRPs protein components of the mESC network. The third network‐based approach explores the functional significance of the mESC Epigenetic Properties and Chromatin States, by analysing them in the context of the structure of the chromatin in the cell nucleus. The results revealed interesting properties of the organization of the mESC epigenetic control system, in line with the emerging models of gene expression control and chromatin organization, and again support the role of 5hmC as a factor present in a significant number of interactions related with active transcription in mouse embryonic stems cells. One additional network approach shows how the same network properties can help to understand the complex relations between expression patterns related with human diseases.
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Biography: Xinghua (Mindy) Shi is an assistant professor in the Department of Bioinformatics and Genomics, College of Computing and Informatics, University of North Carolina at Charlotte. Before joining UNC Charlotte, she was a postdoctoral research fellow at Brigham and Women’s Hospital and Harvard Medical School, an NIH T32 medical genetics training fellow at Harvard Medical School, a visiting research fellow in the Medical and Population Genetics program at Broad Institute, and an associate in the Quantitative Genetics Program at Harvard School of Public Health. She has received her Ph.D. and M.S. degrees in Computer Science from the University of Chicago, and M.Eng and B. Eng degrees in Computer Science from Beijing Institute of Technology, China. Her research interest is in bioinformatics and computational systems biology. Particularly, she works on the design and development of tools and algorithms to solve large-scale computational problems in biology and biomedical research. She is currently focused on integrating genetic and epigenetic datasets to study how genetic architecture affects biological processes and complex phenotypes at the systems level. She is also interested in genetic privacy, complex network analysis, and big data analytics in biomedical research. Her work is supported by multiple agencies and foundations including Wells Fargo Foundation Fund, NSF, NIH, and DARPA. An Integrative Approach Toward Predictive Modelling for Big Data GenomicsAbstract: The biological data deluge thanks to recent advances in biotechnology, has fundamentally transformed life sciences and biomedical research into a data science frontier. We witness a genomic era of data acquisition on a broader scale, with finer accuracy, higher dimensionality, and higher throughput than ever. The unprecedented accumulation of genomic data presents a unique challenging opportunity to dive deep into understanding the complex interplay of (epi-)genetics with phenotypic variation. To fully exploit big genomic data and enable translation of genomic analytics to clinical practice, we have developed a suite of machine learning methods to investigate these complex relationships toward predictive modeling in genomics. In this talk, I will first review the current status of cataloging human genetic variation and assessing their functional impact in the 1000 Genomes Project. Next, I will present our recent work of integrating genomics and interactome for quantitative trait locus network analysis, and constructing predictive models based on a deep learning framework. Finally, I will summarize the talk and point to future research directions in genetic privacy, and infrastructure support that transforms beyond current high performance computing for big data genomics. |
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Biography: Professor Xiujie Wang received her Ph.D. degree in bioinformatics from The Rockefeller University in 2004, and is currently the Director for Center of Molecular Systems Biology, a Principal Investigator at the Institute of Genetics and Developmental Biology (IGDB), Chinese Academy of Sciences. Since joining IGDB at the beginning of 2005, Dr. Wang has been leading a research group working on bioinformatics and systems biology, with an emphasis on non-coding RNA prediction and functional study as well as transcriptomic data analysis. They have identified miRNA-24 as a key regulator for heart failure, discovered a cluster of stem cell pluripotency related miRNAs, and revealed the new roles of miRNAs in regulating the formation of RNA m6A modification. Dr. Wang’s group has published over 80 research papers on journals including Cell Stem Cell, Genes & Development, PNAS, Circulation Research, Genome Biology, Nucleic Acids Research, etc, and developed a few bioinformatics software, including GOEAST, psRobot, ISRNA. Dr. Wang received NSFC Outstanding Young Scientist Award and DuPont Young Scientist Award in 2007, was elected to the Ten Thousand Talent Leading Scientist Program, and jointly won China National Natural Science Award (Second Class) in 2014 and 2016, respectively. Using Bioinformatics to Identify New Regulatory MechanismsAbstract: Increasing amount of large-scale biological data had been produced in recent years with the broad application of high-throughput sequencing technologies. In combination with bioinformatic analysis and experimental validation, we have identified a cluster of miRNAs whose expression abundance is positively correlated with the pluripotency level of ESCs, and confirmed that one of the functions of these miRNAs is to target the key component of the PRC2 complex therefore to regulate H3K27me3 modification. We also identified a long non-coding RNA which functions as a ceRNA to compete for miRNAs targeting a key pluripotency factor, Nanog. We have proven that ESC-specific transcription factors are capable to produce ESC-specific transcripts with alternative transcription start sites from ubiquitously expressed genes, thus confer ubiquitously expressed genes novel functions to involve in the maintenance of ESC pluripotency. In addition, we identified that the m6A modification on mRNAs is regulated by miRNAs via a sequence pairing mechanism, which revealed a new role for miRNAs in regulating mRNA epigenetic modifications. |
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Biography: Dr. Yong Hou was trained as PhD in bioinformatics in Copenhagen University, and now works as Research Scientist and Associate Director of BGI-Research. He has strong background of next generation sequencing data analysis and interpretation, especially on single cell analysis and cancer research. Published more than 30 peer reviewed scientific papers on journals including Cell, Nature Biotechnology, Nature Communications and listed as the co-inventor of more than 30 of patents in related area. He has granted more than 12 million RMB from national or local funding agencies to investigate the clinical application of next generation sequencing on cancer diagnosis. He is invited as guest editor for Journal of Clinical and Translational Medicine on Clinical Bioinformatics Session, and reviewer of BMC Bioinformatics, Oncotarget and Cell Biology and Toxicology. Now he is focusing on the translational research of applying next generation sequencing and single cell analysis to cancer precision medicine.. Single cell sequencing and its application in cancer researchAbstract: Single cell sequencing emerges as the Method of Year 2012 and one of technology changing the trajectory for cancer on the recent published New England Journal of Medicine. From 2010, we seek to answer why sequencing single cell is important. We and others demonstrated the cell-cell genetic variation using single cell sequencing on a variety of cancers, gamete cells, neurons et al. Using single cell sequencing, we also observed individual cells within the same population may differ dramatically, and these differences can have important consequences for the cancer precision diagnosis We found single cell sequencing could systematically describe the given “state” of a cancer cell, define cancer-normal cell-to-cell variation, measure the impact of environmental perturbations, and help understand cellular responses in the larger context of cancer tissues. However, in the future, better WGA methods and lower cost single cell sequencing pipeline could facilitate the clinical usage of single cell sequencing towards cancer precision diagnosis. |
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Biography: Dr. Shu obtained his PhD degree in 1998 at the Institute of Virology, Chinese Academy of Preventive Medicine. He got post-doctoral training at the Mount Sinai Medical School (1998–1999), and University of California, Los Angeles (1999–2002). He is currently the Dean of Public Health School (Shenzhen), Sun Yat-Sen University. He served as the director of WHO Collaborating Center for Reference and Research on Influenza (2010-2017), the director of Chinese National Influenza Centre (CNIC, 2004-2017), the deputy director of National Institute for Viral Disease Control and Prevention of China CDC (2008-2017). Dr. Shu played a leadership to establish the national influenza surveillance network in China including 408 influenza laboratories and 554 influenza sentinel hospitals, which has played important roles influenza vaccine composition recommendation and pandemic preparedness and response. He has been committed to the research mainly on molecular evolution, the mechanisms of interspecies transmission, infectivity, and pathogenicity of influenza viruses, and the new detection techniques development and vaccine and antiviral drug related research. In 2013, he firstly discovered a novel H7N9 avian influenza virus caused severe human infection in China. He leads the studies on the biological features of the H7N9 avian influenza virus; the findings provided scientific insights for the infectivity, transmissibility and pathogenesis of the novel H7N9 virus. He successfully developed diagnosis kits for H7N9 and pandemic H1N1 viruses to improve the clinical treatment. He also firstly identified the avian influenza H10N8 and H5N6 viruses caused human infection. He made great contributions to the prevention and control of H5N1 and pandemic H1N1 2009 in China. Dr.Shu has lead more than 20 scientific projects supported by China central government agencies, National Institutes of Health (NIH) and Centers for Disease Control and Prevention (CDC), USA, et al. He is also the Distinguished Young Scholar funded by National Natural Science Foundation of China. He has published more than 100 peer-reviewed scientific journal papers including in Science, Nature, NEJM, Lancet et al. Dr. Shu was the winner for National Science and Technology progress award and the China Youth Science and technology prize; He was selected as the National Science and Technology Innovation Leader in 2012 and nominated as the Science and Technology Innovator in 2014. Bioinformatics in the prevention and control of infectious diseasesAbstract: Continually outbreaks caused by Influenza, ZIKA, Ebola and MERS et al suggest that although huge progresses have been made in prevention and control of emerging infectious diseases, they remain the threats for the global public health and still cause large morbidity and mortality to humans. How to effectively control and prevent the infectious diseases is still the globe priority of public heatlh. With the advent and rapid development of DNA sequencing technology, sequencing-based methods have been extensively used in surveillance of infectious diseases, during which process the “big data” for infectious diseases, including but not limited to genomic, virological, epidemiological and environmental data were accumulated rapidly. Based on such big data, numerous bioinformatics methods have been developed and demonstrated to be helpful in rapid identification, tracing, prediction and early warnings of emerging infectious diseases. Here, we described our efforts in the prevention and control of influenza viruses by combining computational methods and big data for influenza viruses, which were derived from the world’s largest influenza surveillance network in China. Firstly, by integrating genomic, virological and epidemiological data, for the first time we isolated and identified a novel reassortant avian-origin influenza A (H7N9) virus which caused the outbreaks in China in the spring of 2013. Further, through an in-depth evolutionary analysis of whole-genome sequence data of H7N9 and H9N2 viruses, we identified the pathways for the generation of diverse H7N9 genotypes in China. Similar method was also successfully used to identify the origin of Zika virus outbreak in Brazil in 2006. Secondly, we had developed a novel method named co-occurrence network model to capture the coevolution of viral genome, and present each genome as s network. The co-occurrence network could help to build the good association between viral genotype and pheonytpe. Characteristics derived from viral co-occurrence network were successfully used to predict the antigenic variation of influenza viruses, and to access the severity of Ebola viruses. Thirdly, we had also developed a computational method, named PREDAC, for predicting antigenic clusters (i.e., a group of viruses with similar antigenicity) based on the hemaggulutinin protein sequence of influenza viruses, which allowed us to systematically model the antigenic evolution of influenza viruses, including human influenza H3N2, H1N1 and highly pathogenic avian influenza H5N1 viruses. Moreover, we demonstrated that coupling PREDAC and large-scale sequencing of human influenza H3N2 viruses could significantly improve vaccine strain recommendation for China. In summary, in the big data era, bioinformatics methods will make a great contribution for prevention and control of infectious diseases. |
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Biography: Wong Limsoon is Kwan-Im-Thong-Hood-Cho-Temple Chair Professor of Computer Science at the National University of Singapore. He currently works mostly on knowledge discovery technologies and their application to biomedicine. He is a Fellow of the ACM, inducted for his contributions to database theory and computational biology. His other awards include the 2003 FEER Asian Innovation Gold Award for his work on treatment optimization of childhood leukemias, and the ICDT 2014 Test of Time Award for his work on naturally embedded query languages. Advancing clinical proteomics via analysis based on biological complexesAbstract: Mass spectrometry (MS)-based proteomics is a widely used and powerful tool for profiling systems-wide protein expression changes. It can be applied for various purposes, e.g. biomarker discovery in diseases and study of drug responses. Nonetheless, MS-based proteomics tend to have consistency issues (poor reproducibility and inter-sample agreement) and coverage issues (inability to detect the entire proteome) that need to be urgently addressed. This talk discusses how these issues can be addressed by proteomic profile analysis techniques that use biological networks (especially protein complexes) as the biological context. In particular, several techniques that we have been developing for complex-based analysis of proteomics profile are described. These techniques are useful in identifying proteomics-profile analysis results that are more consistent, more reproducible, more robust in the presence of batch effects, and more biologically coherent, and these techniques allow expansion of the detected proteome to uncover and/or discover novel proteins. Incidentally, I think this work beautifully demonstrates the triumph of logic and computational thinking over noise. |