Prediction of Drug Target Interaction Using Association Rule Mining (ARM)

Dr Nurul Hashimah  Ahamed Hassain Malim1, Mr Muhammad Jaziem  Mohamed Javed1

1Universiti Sains Malaysia, USMPenang, Malaysia, nurulhashimah@usm.my

 

INTRODUCTION

Drug repositioning helps to identify new drug indications (i.e. new known disease) for known drugs [1]. It is an innovation stream of pharmaceutical development that offers an edge for both drug developers as well as for patients since the medicines is safe to use. This method is believed as a successful alternative method in the drug discovery process due to several drugs in the past have been successfully repositioned to a new indication, with the most prominent of them being Viagra and Thalidomide, which in turn has brought a higher revenue [2]. The main reason that made drug repositioning possible is the accepted concept of ‘polypharmacology’ [3]. In general, polypharmacology transformed the idea of drug development from “one drug one target” to “one drug multiple target” [4]. Involvement of polypharmacological in the drug discovery area can be seen when (a) single drug acting on multiple targets of a unique disease pathway, or (b) single drug acting on multiple targets in regards to multiple disease pathways and the polypharmacological property within a drug helps us to identify more than one target that it can act on and hence new uses of the respective drug can be discovered [4]. The use of in silico methods in order to predict the interactions between drugs and target proteins provides a crucial leap  for drug repositioning, as it can  remarkably reduce  wet-laboratory  work and lower the cost of the experimental discovery of new drug-target interactions (DTIs) [5].

IN SILICO APPROACHES USED FOR DRUG REPOSITIONING

Similarity Searching technique which falls under ligand-based category can be classify as one of the well- established method since it was used by many researchers in predicting DTIs [6]. Driving the introduction of these new application is the desire to find patentable, more suitable, lead compounds as well as reducing the high failure rates of compounds in the drug discovery and development pipeline [7]. Based on Figure 1.0 below, new prediction of DTIs happens when this method allows another reference ligand (nearest neighbour) to be found whenever a single ligand (active query) with known biological activity is used for searching process [8]. This reference ligand which are discovered after it is being screened against large number of database compounds will then bind to the same target as the query compound did and it is assumed as a potential drug [8]. The rational of this screening method is that true binders/drugs would share similar functional groups and/or geometric shapes given provided interacting hot spots within the binding site of the respective protein [9]. Despite possessing the edge when it comes in identifying a new drug, however similarity searching does have several disadvantages as well. First, this method depends on the availability of known ligands, which may be not heuristics in the earlier stages of the drug discovery process. In other words, it need at least one ligand compound in order to initiate its process [8]. Second, the similarity searching method which is based on the ligand similarity will have difficulties in identifying drugs with novel scaffolds that are contradict with those query compounds [10]. Last limitation that we identified on this technique is that it does not determine the binding position of the ligand compound within the binding site and the correlation binding score between the ligand and the protein [11]. The binding mode within the binding site is crucial in exploring the responsive mechanism between the protein and the ligand and the accuracy of the identified drug lead. The binding energy score, which relies on the forecast of correct binding modes, do play an important role as well when optimizing drug leads.

Knowledge Discovery in Databases (KDD) can be defined as the use of methods from domains such as machine learning, pattern recognition, statistics, and other related fields as to deduce knowledge from huge collections of data, where the respective knowledge is absence from the database structure [12]. Very large amounts of data are also characteristic of the databases of pharmaceutical companies, which has led to the growing use of KDD methods within the drug discovery process. However, lately researchers have diverted their interest to some other methodologies/ideas which can clarify in depth about molecular activity [12]. It is believed that those methods will not improve the prediction accuracy, but it still can assist the medicinal chemists in terms of developing the next marketable drugs [12]. This situation prompted different related techniques from KDD field being introduced to chemoinformatics, with one of them known as Association Rule Mining (ARM) [12]. ARM is a type of classification method that share the same properties with machine learning methods but slightly different in their primary aim as it focused on explanation rather than classification [12]. They focused on the features or group of features which may decide a particular classification for a set of objects [12]. Promising performance of ARM in several instances of target prediction has made it favourable in the case of predicting DTIs.

METHODOLOGIES

The information contains within activities classes ranging from heterogenous and homogenous category from ChEMBL database is important as it can be used to build the classification model. In our experiment, using that information we generate appropriate rules that will determine protein targets for a particular ligand. Each rule generated  were  based on  the  support and  confidence level  associate  with  them.  Support indicates how frequently  the items  appear in  the database.  While, confidence specify  the number of times the if/then statements have been found to be true. From the support and confidence scores obtained earlier, we select the best rules for the target prediction and these rules will be used to predict protein target for future ligands. However, the biggest challenge of ARM is that it’s a compute intensive procedures at the frequent itemsets generation. Hence, it is crucial that the execution is done on a high performance machine. At the moment we are lacking in high computing resources and this limit us to fully explore the capability of in relation to our objectives. Nevertheless, we have obtained results based on certain parameter ranges that would be present on the poster later.

FIGURES

Figure 1.0: Conventional similarity searching method used to predict new ligand that will interact with a particular target [8].

 

REFERENCES

[1] L. Yu, X. Ma, L. Zhang, J. Zhang and L. Gao, “Prediction of new drug indications based on clinical data and network modularity”, Scientific Reports, vol. 6, no. 1, 2016.

[2] T. Ashburn, B. K. Thor, Drug Repositioning: Identifying and Developing New Uses for Existing Drugs. Nat. Rev. Drug Discovery, vol. 3, pp. 673−683,2004.

[3]  J.C.  Nacher,   J.M.  Schwartz,  Modularity  in  Protein  Complex  and  Drug  Interactions  Reveals  New Polypharmacological Properties. PLoS One, vol. 7, e30028, 2012.

[4] J. Peters, “Polypharmacology – Foe or Friend?”, Journal of Medicinal Chemistry, vol. 56, no. 22, pp. 8955- 8971, 2013.

[5]   “computational   drug   discovery:   Topics   by   Science.gov”, Science.gov,   2017.   [Online].   Available: https://www.science.gov/topicpages/c/computational+drug+discovery.html. [Accessed: 06- Sep- 2017].

[6] T. Katsila, G. Spyroulias, G. Patrinos and M. Matsoukas, “Computational approaches in target identification and drug discovery”, Computational and Structural Biotechnology Journal, vol. 14, pp. 177-184, 2016.

[7] J. Auer, J. Bajorath. In: Keith J, editor. Bioinformatics. Humana Press, pp. 327–47,2008.

[8] P. Willett, J.M. Barnard, G.M. Downs. Chemical Similarity Searching. Journal of Chemical Information and Computer Sciences, vol. 38, pp. 983 – 996. 1998.

[9] S. Huang, M. Li, J. Wang and Y. Pan, “HybridDock: A Hybrid Protein–Ligand Docking Protocol Integrating Protein- and Ligand-Based Approaches”, Journal of Chemical Information and Modeling, vol. 56, no. 6, pp. 1078- 1087, 2016.

[10] N. Wale, I. Watson and G. Karypis, “Indirect Similarity Based Methods for Effective Scaffold-Hopping in Chemical Compounds”, Journal of Chemical Information and Modeling, vol. 48, no. 4, pp. 730-741, 2008.

[11] D. Mobley and K. Dill, “Binding of Small-Molecule Ligands to Proteins: “What You See” Is Not Always “What You Get””, Structure, vol. 17, no. 4, pp. 489-498, 2009.

[12] E. Gardiner and V. Gillet, “Perspectives on Knowledge Discovery Algorithms Recently Introduced in Chemoinformatics: Rough Set Theory, Association Rule Mining, Emerging Patterns, and Formal Concept Analysis”, Journal of Chemical Information and Modeling, vol. 55, no. 9, pp. 1781-1803, 2015.

 


Biography:

Nurul Hashimah Ahamed Hassain Malim (Nurul Malim) received her B.Sc (Hons) in computer science and M.Sc in computer science from Universiti Sains Malaysia, Malaysia. She completed her PhD in 2011 from The University of Sheffield, United Kingdom. Her current research interests include chemoinformatics, bioinformatics, data analytics, sentiment analysis and high-performance computing. She is currently a Senior Lecturer in the School of Computer Sciences, Universiti Sains Malaysia, Malaysia.

Cybercriminal Personality Detection through Machine Learning

Dr Nurul Hashimah  Ahamed Hassain Malim1Saravanan Sagadevan1, Muhd Baqir Hakim1, Nurul Izzati Ridzuwan1

1 Universiti Sains Malaysia, Penang, Malaysia, nurulhashimah@usm.my

 

ABSTRACT

The development of sophisticated forms of communication technologies such as social networks has exponentially raised the number of users that participate in online activities. Although the development encourages and brings many  positive  social  benefits,  the  dark  sides  of  online  communication are  still  a  major  concern  in  virtual interactions. The dark side of online activities occasionally referred as cyber threats or cyber crimes. Over the past two decades, cybercrime cases have increased exponentially and threatened the privacy and life of online users. Occasionally, severe kinds of cyber criminal activities such as cyber bullying and cyber harassment executed through exploiting text messages and the anonymity offered by social network platforms such as Facebook and Twitter. However, the linguistic clues such as patterns of writing and expression in the text messages often act as fingerprints in revealing the personality traits of the culprits who hide behind the anonymity provided by social networks [1]. Personality traits are hidden abstraction that combined emotion, behavior, motivation and thinking patterns of human that often mirror the true characteristics of them through their activities that conducted intentionally or unintentionally [2]. In nature, each individual are differed in terms of their talking and writing styles or patterns and it is hard to observe those differences. The distinct styles of talking and writing are unique from person to person and there are tendencies to decipher the identity of writers by simply observed at the pattern of the writing especially the formation of words, phrases and clauses. Sir Francis Galton was identified as the first person that hypothesized natural language terms might present the personality differences in humankind [3]. Furthermore, Hofstee suggested that nouns, sentences, and actions might have some kind of connotations towards personality [4].  In  the other hand, since several decades ago, the people from forensic psychology, behavioral sciences and the law enforcement agencies have been working together to study and integrate the science  of  psychology into  criminal  profiling  [5].  Through  the  review  of  literature related  with  psychology, linguistics  and  behavior,  it  can  be  affirmed  that  strong  relationship  presented  between  personality  traits especially related with criminals and writing/language skills. Therefore, curiosity raised on whether the writing pattern in social networks by cyber criminals could be identify or detected by using automatic classifiers. If yes, how better will be the performances of the classifiers and what are the words or combination of words that may frequently used by cyber predators. Therefore, in order to find answers to those questions, we conducted an empirical investigation [9] (main study) with two other small scales studies [10,11] (extend the main study) by using the textual sources from Facebook and Twitter and exploiting the descriptions stated in Three Factor Personality Model, and sentiment valences. For the main study, the open source data Facebook [6] and Twitter [7] were used as text input while the data for other two small scale studies were harvested from Twitter using Tweepy, a Python library for accessing the Twitter API. The main study and the second study used data that only written in English language while third study used tweets in Malay Language (Bahasa Malaysia).  In these studies, we employed four main classifiers namely Sequential Minimal Optimization (SMO), Naive Bayes (NB), K- Nearest Neighbor (KNN) and J48 with ZeroR as baseline from Waikato Environment for Knowledge Analysis (WEKA) Machine Learning Tool. The reason to  used the traits  from Three Factor Model in  this  study is  due  to  the widespread use of the model in criminology, less number of traits ease the characteristics categorization process and large number of empirical proved that associated Psychoticism trait with criminal characteristics whereas sentiment valences was used to measure the polarity of sentiment terms.  The major traits of Three Factor Model and its associated characteristics listed in Table 1.

Table 1: Three Factor Model Traits and its characteristics [8].

Traits Specific Characteristics
Extraversion Sociable, lively, active, assertive, sensation seeking, carefree, and dominant.
Neuroticism Anxious, depressed, guilt feelings, low self-esteem, tense, irrational, and moody.
Psychoticism Aggressive, egocentric, impersonal, impulsive, antisocial, creative and tough-minded.

The three studies used similar research framework as following. Step 1 : Data Collection & Preprocessing (Data Cleansing, Stemming, Part-Of-Speech Tagging), Step 2 : Data Annotations, Step 3 : Automatic Classification by the four Classifiers, Step 4 : Performance analysis, criminal related terms identification (using Chi-Square method).  The following tables illustrated the performances of machine learning classifiers of the studies and the list of the terms that identified to be associated to criminal behavior. The class balancing method called Synthetic Minority Over-sampling Technique (SMOTE) was used to overcome the unbalance volume of class instances.

Table 2 : Accuracy of classifiers based on with/without SMOTE class balancing methods[10].

Performance measurement based on True Positive (TP)and False Positive (FP)
Type/Classifier ZeroR NB KNN SMO J48
TP FP TP FP TP FP TP FP TP FP
Without SMOTE 47.2

7

52.73 58.18 41.82 47.27 52.73 72.73 27.27 78.18 21.82
With SMOTE 40.6

3

59.38 68.75 31.25 53.13 46.88 73.44 26.56 75.00 25

 

Table 3 : Accuracy of classifiers based on measuring the effect of class measuring[11].

Performance measurement based on True Positive (TP)and False Positive (FP)
Cross

Validation/Classifier

ZeroR NB KNN SMO J48
TP FP TP FP TP FP TP FP TP FP
3 53.3 46.7 80.0 20.0 63.3 36.7 73.3 26.7 50.0 50.0
5 53.3 46.7 90.0 10.0 56.7 43.3 70.0 30.0 63.3 36.7
10 53.3 46.7 90.0 10.0 56.7 43.3 86.3 16.7 70.0 30.0

 

Table 4 : Terms that highly associated with criminal behavior [9].

Facebook Twitter
Unigram Bigram Trigram Unigram Bigram Trigram
Damn The hell I want to Suck Damn It A big ass
Shit Damn it Damn it I Adore The hell A bit more
Fuck Hell i Is a bitch Annoy A bitch A bitch and
Hell My Fuck What the Fuck Asshole A damn A damn good
Ass The shit What the hell Shit A fuck All fuck up
Suck Damn you I feel like Fuck A hell A great fuck
Bad The fuck The hell I Hell Damn you A great night
Feel A bitch Cute A shit A pain in
Hate Fuck yeah Damn My ass A fuck off

 

As conclusion, our investigation showed that J48 performed better than other classifiers with and without applied the SMOTE class balancing technique and the effect of cross validation vary for each classifiers. However, in overall view, Naïve Bayes performed better on each cross validation experiments. This investigation also produced a list of the words that may used by cyber criminals based on language models specification. Then, for future study, we planned to used deep learning methods to analyze the contents related with cyber terrorism and welcome any collaboration for social networks cyber terrorism textual data collaboration.

REFERENCES

  1. Olivia Goldhill.. Digital detectives: solving crimes through Twitter, 2013. The Telegraph.
  2. Navonil  Majumder,  Soujanya  Poria,  Alexander  Gelbukh,  and  Erik  Cambria.  2017.  Deep  learning  based document modeling for personality detection from text. IEEE Intelligent Systems 32(2):74–79.
  3. Sapir, Edward. Language: An Introduction to the Study of Speech. New York: Harcourt, Brace, 1921.
  4. Matthews, G, Ian, J. D., & Martha, C. W. Personality Traits (2nd edition). Cambridge University Press, 2003.
  5. Gierowski, J. K. Podstawowa problematyka psychologiczna w procesie karnym. Psychologia w postępowaniu karnym, Lexis Nexis, Warszawa 2010.
  6. Celli, F., Pianesi, F., Stillwell, D., & Kosinski, M. Workshop on Computational Personality Recognition (Shared Task). In Proceedings of WCPR13, in conjunction with ICWSM-2013.
  7. Alec, G., Richa, B, & Lei, H.. Twitter Sentiment Classification using Distant Supervision, 2009.
  8. Coleta, V. D, Jan, M. A., Janssens, M., & Eric E. J. PEN, Big Five, juvenile delinquency and criminal recidivism. Personality and Individual Differences, 39, (2005) 7–19. DOI:10.1016/j.paid.2004.06.016.
  9. Saravanan Sagadevan. Thesis : Comparison Of Machine Learning Algorithms For Personality Detection In Online Social Networking, 2017.
  10. Muhd, Baqir Hakim. Profiling Online Social Network (OSN) User Using PEN Model and Dark Triad Based on English Text Using Machine Learning Algorithm, 2017 (In Review).
  11. Nurul Izzati Binti Ridzuwan. Online Social Network User-Level Personality Profiling Using Pen Model Based On Malay Text (In Review), 2017.

 


Biography:

Nurul Hashimah Ahamed Hassain Malim (Nurul Malim) received her B.Sc (Hons) in computer science and M.Sc in computer science from Universiti Sains Malaysia, Malaysia. She completed her PhD in 2011 from The University of Sheffield, United Kingdom. Her current research interests include chemoinformatics, bioinformatics, data analytics, sentiment analysis and high-performance computing. She is currently a Senior Lecturer in the School of Computer Sciences, Universiti Sains Malaysia, Malaysia.

Digital Earth Australia (DEA): From Satellites to Services

Mr Neal Evans1, Dr Trevor Dhu2, Mr David Gavin3, Dr David Hudson4, Mr Trent Kershaw5, Dr Leo Lymburner6, Ms Alla Metlenko7, Mr Norman Mueller8, Mr Simon Oliver9, Chris Penning10, Dr Medhavy Thankappan11, Ms Alicia Thomson12

1Geoscience Australia, Canberra, AUS, Neal.Evans@ga.gov.au

2Geoscience Australia, Canberra, AUS, Trevor.Dhu@ga.gov.au

3Geoscience Australia, Canberra, AUS, Daivd.Gavin@ga.gov.au

4Geoscience Australia, Canberra, AUS, David.Hudson@ga.gov.au

5Geoscience Australia, Canberra, AUS, Trent.Kershaw@ga.gov.au

6Geoscience Australia, Canberra, AUS, Leo.Lymburner@ga.gov.au

7Geoscience Australia, Canberra, AUS, Alla.Metlenko@ga.gov.au

8Geoscience Australia, Canberra, AUS, Norman.Mueller@ga.gov.au

9Geoscience Australia, Canberra, AUS, Simon.Oliver@ga.gov.au

10Geoscience Australia, Canberra, AUS, Chris.Penning@ga.gov.au

11Geoscience Australia, Canberra, AUS, Medhavy.Thankappan@ga.gov.au

12Geoscience Australia, Canberra, AUS, Alicia.Thomson@ga.gov.au

 

INTRODUCTION

The 2017/18 Budget identified over $2 billion of investments in monitoring, protecting or enhancing Australia’s land, coasts and oceans over the next four years including: the National Landcare Program; the Commonwealth Marine Reserves implementation; implementation of the Murray-Darling Basin Plan and water reform agenda; support for State and Territory governments to develop secure and affordable water infrastructure; improving water quality and scientific knowledge of the Great Barrier Reef.

Geoscience Australia’s efforts within this investment will be a program known as Digital Earth Australia (DEA) and will directly support these investments through the provision of an evidence base for the design, implementation and evaluation of policies, programs and regulation. It will also support Industry with access to stable, standardised data and imagery products from which it can innovate to produce new value added products and services.

WHAT IS DEA?

DEA is an analysis platform for satellite imagery and other Earth observations. Today, it translates 30 years of Earth observation data (taken every two weeks at 25 metre squared resolution) and tracks changes across Australia in unprecedented detail, identifying soil and coastal erosion, crop growth, water quality, and changes to cities and regions. When fully operational, DEA will provide new information for every 10 square metres of Australia, every five days.

DEA uses open source standards, building off the international Open Data Cube technology which is supported by the Committee on Earth Observation Satellites (CEOS)1.

 

       

           Figure 1: WOfS Gulf of Carpentaria, QLD                                        Figure 2: Intertidal model over Exmouth Gulf, WA

Initial examples of how DEA will support government, industry and the research community through improved data include Water Observations from Space (WOfS), a continent-scale map of the presence of surface water; and the Intertidal Extents Model (ITEM) that consistently maps Australia’s vast intertidal zone to support coastal planning.

WOfS is already helping to improve the Australian Government’s understanding of water availability, historical flood inundation and environmental flows, while ITEM has yielded the first continent-wide tidal extent map for Australia and is being used by the Queensland government to assist in their intertidal and subtidal habitat mapping program.

BENEFITS TO GOVERNMENT

DEA will benefit government departments and agencies that need accurate and timely spatial information on the health and productivity of Australia’s landscape. This near real-time information can be readily used as an evidence base for the design, implementation, and evaluation of policies, programs and regulation, and for developing policy advice.

DEA will also support agencies to better monitor change, protect and enhance Australia’s natural resources, and enable more effective responses to problems of national significance. Information extracted from Earth observation data will reduce risk from natural hazards such as bushfires and floods, assist in securing food resources, and enable informed decision making across government. Economic benefits are expected to be realised from better targeted government investment, reduced burden on the recipients of government funding, and increased productivity.

The DEA Program is developing joint projects to deliver products that address policy challenges across a range of Australian Government departments.

JOIN US

We invite you to be part of the future of DEA, as we build new products and tools to support Australian Government agencies to better monitor, protect, and enhance Australia’s natural resources.

Contact us to discuss how DEA can inform and support the work of your agency.

W: www.ga.gov.au/dea

E: Earth.Observation@ga.gov.au

REFERENCES

  1. Lewis, A., Oliver, S., Lymburner, L., Evans, B., Wyborn, L., Mueller, N., Raevksi, G., Hooke, J., Woodcock, R., Sixsmith, J., Wu, W., Tan, P., Li, F., Killough, B., Minchin, S., Roberts, D., Ayers, D., Bala, B., Dwyer, J., Dekker, A., Dhu, T., Hicks, A., Ip, A., Purss, M., Richards, C., Sagar, S., Trenham, C., Wang, P., L-W Wang, L-W., The Australian Geoscience Data Cube – Foundations and lessons learned, Remote Sensing of Environment (In Press). https://doi.org/10.1016/j.rse.2017.03.015
  2. CEOS. Available from http://www.ceosdatacube.org, accessed 28 Aug 2017
  3. Mueller, N., Lewis, A., Roberts, D., Ring, S., Melrose, R., Sixsmith, J., Lymburner, L., McIntyre, A., Tan, P., Curnow, S., Ip, A. Water observations from space: Mapping surface water from 25 years of Landsat imagery across Australia, Remote Sensing of Environment 174, 341-352, ISSN 0034-4257.
  4. Sagar, S., Roberts, D., Bala, B., Lymburner, L., 2017. Extracting the intertidal extent and topography of the Australian coastline from a 28 year time series of Landsat observations.Remote Sensing of Environment 195, 153–169. https://doi.org/10.1016/j.rse.2017.04.009
  5. GA eCat Record http://pid.geoscience.gov.au/dataset/113041, created 28 Aug 2017

SMART’S Digital Living Lab embracing the IoT revolution

Mr Tim Davies1, Ms Tania  Brown1

1Smart Infrastructure Facility, University Of Wollongong,  Wollongong, Australia, tdavies@uow.edu.autaniabr@uow.edu.au

 

The SMART Infrastructure Facility, with the backing of the University of Wollongong, has embarked on a mission to become a technologically powered hub with the potential to improve the lives of every person in the community.

The establishment of SMART’s Digital Living Lab will see the region become home to a free-to-air Internet of Things (IoT) network, that enables us to fully embrace the IoT revolution. Designed to address key social and environmental challenges within the region, it will ultimately allow individuals, community groups and businesses the ability to connect like never before.

Over the next months and years, we believe this will lead to us truly becoming a smart city which will use broad-ranging, research-oriented projects to improve the ‘liveability’ of the city and the lifestyle of the people within it.

In collaboration with Wollongong City Council and Sydney Water, UOW is deploying a free-to-air Internet of Things (IoT) network across the region to support high impact community-orientated projects.

The Internet of Things (IoT) enables a cost effective mechanism to collect data from thousands of small digital devices (nodes) that inform real time applications. Current examples include smart home thermostats, smart street lighting or smart street parking finders.

IoT comes in many ways and forms. The LoRaWAN technology offers an open protocol and free-to-air connection to application developers and end-users. UOW is partnering with Meshed and The Things Network to deploy this network in the Illawarra region, allowing for cost effective community-orientated solutions.

The Digital Living Lab will connect individuals, community groups and businesses, helping them develop technology-based, research-oriented projects to improve the liveability of the city and the lifestyle of the people within it.

One of the first projects will use sensors to monitor the Wollongong city’s storm water network. The city’s topography bounded by an escarpment on one side and the ocean on the other, makes it especially vulnerable to catastrophic floods. Wollongong is a network of small streams that come off the escarpment very quickly, there is a real need to better understand how floods work and how streams rise and fall in various rainfall events. This is a great opportunity to collect a lot of data, and therefore further refine flood models, and allow people to be more confident about the flood impacts on their property.

Other projects will be developed to map the availability of wheelchair access to pivotal venues; monitor water flow in primary lagoons, benefit aged care homes; test air quality or develop improved transport options.

The Internet of Things network is designed for community use and this initiative is all about people working together to create a region that reflect the needs and desires of the people at its heart. The possibilities are endless.

The development of the Wollongong region as a Digital Living Lab would place it on the cutting-edge of a global revolution, allowing it to become Australia’s most dynamic and forward-thinking hub.


Biography:

Tim Davies works for the SMART Infrastructure Facility at the University of Wollongong. He specialises in data visualisation and design.

Parks Australia Collaborations across e-Research systems: Biomes of Australian Soil Environments (BASE) project

Dr Belinda  Brown1, Dr Andrew Bissett2, Professor Andrew Young3, Dr Anna Fitzgerald4, Dr Andrew Gilbert4

1Parks Australia, Canberra, Australia, Belinda.Brown@environment.gov.au

2CSIRO, Hobart, Australia, Andrew.Bissett@csiro.au

3National Research Collections Australia, CSIRO, Canberra, Australia, Andrew.Young@csiro.au

4 Bioplatforms Australia Ltd, Sydney, Australia, afitzgerald@bioplatforms.com agilbert@bioplatforms.com 

 

Environmental information is a strategic asset of Parks Australia and is at the heart of management decisions. The way in which information is collected, described, managed, stored and used is critical to business needs.

The Knowledge Management Strategy for Parks Australia Environmental Information supports information management objectives, now and into the future, to help build the knowledge needed to protect and conserve Australia’s biodiversity, as well as engage with stakeholders and national research infrastructure partners.

Parks Australia contributes environmental information to national and international networks, including, amongst others: the Atlas of Living Australia (ALA), Terrestrial Ecosystem Research Network (TERN) and the Global Biodiversity Information Facility (GBIF).

The agency also contributes to strategic partnerships and projects. The Biomes of Australian Soil Environments (BASE) Project is a recent example with partners from CSIRO and Bioplatforms Australia, with contributions to a national e-Research database. The project collaboration marshalled complementary partners around Australia to pool time and resources to collect under a national sampling framework, including Parks Australia reserves. The project was an opportunity to develop a national environmental and soil microbial diversity framework, to enable new continental baseline information on soil microbial communities, which are primary drivers of soil ecological processes such as nutrient and carbon cycling.

BASE was developed in an open data framework, and is the first Australian soil microbial diversity database. BASE links environmental and soil data across bio-geographic regions, including Commonwealth reserves. Its database provides a reference for comparative analysis across different datasets and regions. The database provides a platform to grow and evolve over time.  It provides a basis to link with other databases and tools, and national e-research infrastructure networks, such as those at the Atlas of Living Australia and Bioplatforms Australia. It provides a new baseline for investigations into the largely un-quantified role of soil microbial diversity in broad scale patterns of plant species abundance, and ecosystem resilience.

Partnerships, and collaboration models such as this provide a basis to value add multi-disciplinary data and enabling science. Information sharing and collaborative practice will continue to expand and change with more organisations working together for multi-disciplinary and integrated outcomes. Now, with emerging policy initiatives across the public sector for data integration, sharing and re-use; national infrastructure and e-Research collaborations continue to be an important component to help build high-value datasets for targeted science, services, policies and programs.


Biography:

Belinda works across multidisciplinary areas for science, environment, and information management;  drawing on over 15 years of experience in the research and public sectors.

Belinda has a PhD in earth systems sciences, and started her career as a research scientist working on a range of international  projects into the development of southern ocean seaways and palaeo-climate around Antarctica.  This laid the foundations for her work in the science-data-policy interface; including amongst other things, working with the National Biodiscovery Working Group, the COAG National Science Working Group for Climate Change Adaptation, COAG Solar Thermal Technology Roadmap Committee, the UN Convention on Biological Diversity and the Global Strategy for Plant Conservation.

Belinda is also lead author and manager for the Knowledge Management Strategy for Parks Australia Environmental Information, and its implementation. Belinda has an interest in enabling evidence based information for improved social, economic, and environmental outcomes; and works with colleagues to extend the value of public data, including Linked Data and eResearch. Recent projects include the Biomes of Australian Soil Environments (BASE) Project, a National Threatened Species Project, and a National Environmental Science Program Emerging Priorities project for the digital curation of long term monitoring datasets.

Digital Earth Australia (DEA): From Satellites to Services

Evans, N1, Dhu, T.2, Gavin, D.3, Hudson, D.4, Kershaw, T.5, Lymburner, L.6, Metlenko, A.7, Mueller, N.8, Oliver, S.9, Penning, C.10, Thankappan, M.11,Thomson, A.12

1Geoscience Australia, Canberra, AUS

Neal.Evans@ga.gov.au Trevor.Dhu@ga.gov.au Daivd.Gavin@ga.gov.au David.Hudson@ga.gov.au Trent.Kershaw@ga.gov.au Leo.Lymburner@ga.gov.au Alla.Metlenko@ga.gov.au , Norman.Mueller@ga.gov.au Simon.Oliver@ga.gov.au Chris.Penning@ga.gov.au Medhavy.Thankappan@ga.gov.au Alicia.Thomson@ga.gov.au

INTRODUCTION

The 2017/18 Budget identified over $2 billion of investments in monitoring, protecting or enhancing Australia’s land, coasts and oceans over the next four years including: the National Landcare Program; the Commonwealth Marine Reserves implementation; implementation of the Murray-Darling Basin Plan and water reform agenda; support for State and Territory governments to develop secure and affordable water infrastructure; improving water quality and scientific knowledge of the Great Barrier Reef.

Geoscience Australia’s efforts within this investment will be a program known as Digital Earth Australia (DEA) and will directly support these investments through the provision of an evidence base for the design, implementation and evaluation of policies, programs and regulation. It will also support Industry with access to stable, standardised data and imagery products from which it can innovate to produce new value added products and services.

WHAT IS DEA?

DEA is an analysis platform for satellite imagery and other Earth observations. Today, it translates 30 years of Earth observation data (taken every two weeks at 25 metre squared resolution) and tracks changes across Australia in unprecedented detail, identifying soil and coastal erosion, crop growth, water quality, and changes to cities and regions. When fully operational, DEA will provide new information for every 10 square metres of Australia, every five days.

DEA uses open source standards, building off the international Open Data Cube technology which is supported by the Committee on Earth Observation Satellites (CEOS)1.

 

 

 

 

 

 

 

Initial examples of how DEA will support government, industry and the research community through improved data include Water Observations from Space (WOfS), a continent-scale map of the presence of surface water; and the Intertidal Extents Model (ITEM) that consistently maps Australia’s vast intertidal zone to support coastal planning.

WOfS is already helping to improve the Australian Government’s understanding of water availability, historical flood inundation and environmental flows, while ITEM has yielded the first continent-wide tidal extent map for Australia and is being used by the Queensland government to assist in their intertidal and subtidal habitat mapping program.

BENEFITS TO GOVERNMENT

DEA will benefit government departments and agencies that need accurate and timely spatial information on the health and productivity of Australia’s landscape. This near real-time information can be readily used as an evidence base for the design, implementation, and evaluation of policies, programs and regulation, and for developing policy advice.
DEA will also support agencies to better monitor change, protect and enhance Australia’s natural resources, and enable more effective responses to problems of national significance. Information extracted from Earth observation data will reduce risk from natural hazards such as bushfires and floods, assist in securing food resources, and enable informed decision making across government. Economic benefits are expected to be realised from better targeted government investment, reduced burden on the recipients of government funding, and increased productivity.
The DEA Program is developing joint projects to deliver products that address policy challenges across a range of Australian Government departments.

JOIN US

We invite you to be part of the future of DEA, as we build new products and tools to support Australian Government agencies to better monitor, protect, and enhance Australia’s natural resources.
Contact us to discuss how DEA can inform and support the work of your agency.
W: www.ga.gov.au/dea
E: Earth.Observation@ga.gov.au

REFERENCES

  1. Lewis, A., Oliver, S., Lymburner, L., Evans, B., Wyborn, L., Mueller, N., Raevksi, G., Hooke, J., Woodcock, R., Sixsmith, J., Wu, W., Tan, P., Li, F., Killough, B., Minchin, S., Roberts, D., Ayers, D., Bala, B., Dwyer, J., Dekker, A., Dhu, T., Hicks, A., Ip, A., Purss, M., Richards, C., Sagar, S., Trenham, C., Wang, P., L-W Wang, L-W., The Australian Geoscience Data Cube – Foundations and lessons learned, Remote Sensing of Environment (In Press). https://doi.org/10.1016/j.rse.2017.03.015
  2. Available from http://www.ceosdatacube.org, accessed 28 Aug 2017
  3. Mueller, N., Lewis, A., Roberts, D., Ring, S., Melrose, R., Sixsmith, J., Lymburner, L., McIntyre, A., Tan, P., Curnow, S., Ip, A. Water observations from space: Mapping surface water from 25 years of Landsat imagery across Australia, Remote Sensing of Environment 174, 341-352, ISSN 0034-4257.
  4. Sagar, S., Roberts, D., Bala, B., Lymburner, L., 2017. Extracting the intertidal extent and topography of the Australian coastline from a 28 year time series of Landsat observations.Remote Sensing of Environment 195, 153–169. https://doi.org/10.1016/j.rse.2017.04.009
  5. GA eCat Record http://pid.geoscience.gov.au/dataset/113041, created 28 Aug 2017

 

Biography:

Neal Evans holds a Bachelor of Science Degree in Geology and following 10 years working for a multi-national exploration company CRA Exploration (now known as Rio Tinto), has worked for 20 years within the Australian Public Service, much of this with Geoscience Australia and its predecessors.

Neal’s roles have included Information Geologist, Database Developer, Mineral Commodity Specialist, Database & Application Team Leader, Project Manager, Director of Information Services and Divisional Information Officer, perhaps best summarised as a Data Scientist.

SMART’S Digital Living Lab embracing the IoT revolution

Mr Tim Davies1, Ms Tania  Brown

1Smart Infrastructure Facility, University Of Wollongong,  Wollongong, Australia tdavies@uow.edu.au taniabr@uow.edu.au

The SMART Infrastructure Facility, with the backing of the University of Wollongong, has embarked on a mission to become a technologically powered hub with the potential to improve the lives of every person in the community.

The establishment of SMART’s Digital Living Lab will see the region become home to a free-to-air Internet of Things (IoT) network, that enables us to fully embrace the IoT revolution. Designed to address key social and environmental challenges within the region, it will ultimately allow individuals, community groups and businesses the ability to connect like never before.
Over the next months and years, we believe this will lead to us truly becoming a smart city which will use broad-ranging, research-oriented projects to improve the ‘liveability’ of the city and the lifestyle of the people within it.

In collaboration with Wollongong City Council and Sydney Water, UOW is deploying a free-to-air Internet of Things (IoT) network across the region to support high impact community-orientated projects.

The Internet of Things (IoT) enables a cost effective mechanism to collect data from thousands of small digital devices (nodes) that inform real time applications. Current examples include smart home thermostats, smart street lighting or smart street parking finders.

IoT comes in many ways and forms. The LoRaWAN technology offers an open protocol and free-to-air connection to application developers and end-users. UOW is partnering with Meshed and The Things Network to deploy this network in the Illawarra region, allowing for cost effective community-orientated solutions.

The Digital Living Lab will connect individuals, community groups and businesses, helping them develop technology-based, research-oriented projects to improve the liveability of the city and the lifestyle of the people within it.

One of the first projects will use sensors to monitor the Wollongong city’s storm water network. The city’s topography bounded by an escarpment on one side and the ocean on the other, makes it especially vulnerable to catastrophic floods. Wollongong is a network of small streams that come off the escarpment very quickly, there is a real need to better understand how floods work and how streams rise and fall in various rainfall events. This is a great opportunity to collect a lot of data, and therefore further refine flood models, and allow people to be more confident about the flood impacts on their property.

Other projects will be developed to map the availability of wheelchair access to pivotal venues; monitor water flow in primary lagoons, benefit aged care homes; test air quality or develop improved transport options.

The Internet of Things network is designed for community use and this initiative is all about people working together to create a region that reflect the needs and desires of the people at its heart. The possibilities are endless.

The development of the Wollongong region as a Digital Living Lab would place it on the cutting-edge of a global revolution, allowing it to become Australia’s most dynamic and forward-thinking hub.


Biography:

Tim Davies works for the SMART Infrastructure Facility at the University of Wollongong. He specialises in data visualisation and design.

International Workshop on Science Gateways – Australia

Michelle Barker1, Steve Androulakis2, David Abramson3, Sandra Gesing4, Rebecca Pirzl5, Richard Sinnott6, Nancy Wilkins-Diehr7

1NeCTAR, Parkville, Australia, michelle.barker@nectar.org.au

2ANDS, NeCTAR, RDS, Parkville, Australia, steve.androulakis@nectar.org.au

3University of Queensland, St Lucia, Australia, david.abramson@uq.edu.au

4University of Notre Dame, South Bend, USA, sandra.gesing@nd.edu

5ALA, Canberra, rebecca.pirzl@csiro.au

6University of Melbourne, Parkville, rsinnott@unimelb.edu.au

7San Diego Supercomputing Centre, La Jolla, USA, wilkinsn@sdsc.edu

 

GENERAL INFORMATION

Workshop Length: Two Days

Primary Presenter: Various, including Dan Katz as keynote, submitted talk and discussions.

Workshop Format: Invited speakers, Open Call for Proposals (Lightning talks, demonstrations), World Cafe.

 

 

DESCRIPTION

This two day workshop offers participants the opportunity to engage with other members of the Science Gateways community, to explore common issues and share successes.

A Science Gateway is a community-developed set of tools, applications, and data collections that are integrated through a tailored web-based environment. Often Science Gateways leverage larger scale computing and data storage facilities that would otherwise be inaccessible to many domain scientists. Gateways can be used to tackle common scientific goals, engage with industry, and offer resources for educating students and informing non-experts.

To continue the development of this community, this workshop offers a venue for knowledge exchange and skills development. Australian science gateways evidence many valuable impacts for their research communities, including collaboration with international gateways in their field. The significance of science gateways programs is evidenced in the existence of a range of national/regional programs that facilitate development of science gateways.

The submission closing date for IWSG-A is 30 June 2017. All submissions will be double-blind peer reviewed and evaluated on quality and relevance.

WHO SHOULD ATTEND

Data Analysts

Data Managers

Government Representatives

HPC Managers & Specialists

IT Managers & Directors

Librarians

Programmers

Professionals in associated disciplines

Project Managers

Researchers

Research Computing Specialists

Research Managers

Scientists

Software & App engineers

University Representatives

WHAT TO BRING

Attendees don’t need to bring computers.

DELIVERY

Call for Papers finishes 30 June 2017. Instructions for making a submission can be found on the IWSG-A web site: http://iwsg-life.org/site/iwsglife/about-iwsg-a

COST:  This workshop isn’t subsidised. Full conference workshop fees apply.


Biography

Michelle Barker is Deputy Director (Research Software Infrastructure) at National eResearch Collaborative Tools and Resources (Nectar), a National Collaborative Research Infrastructure Strategy (NCRIS) funded program. She is one of the convenors of the annual International Workshop on Science Gateways ­ Australia, and the International Coalition on Science Gateways. As Deputy Director at Nectar, Michelle directs the virtual laboratory program, which has facilitated the development of twelve virtual laboratories in diverse disciplines, with over 10,000 users. In this role she also facilitates national conversations around common challenges such as research reproducibility, software sustainability and impact metrics. She was previously Program Director of a science gateway for the malaria community, based at James Cook University. Follow her on Twitter as @michelle1barker

About the conference

eResearch Australasia provides opportunities for delegates to engage, connect, and share their ideas and exemplars concerning new information centric research capabilities, and how information and communication technologies help researchers to collaborate, collect, manage, share, process, analyse, store, find, understand and re-use information.

Conference Managers

Please contact the team at Conference Design with any questions regarding the conference.

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