Jožef Stefan Institute

Knowledge Technologies

Nada Lavrač

PhD, Research Councillor at Department of Knowledge Technologies, Jožef Stefan Institute and Professor at University of Nova Gorica, Slovenia
                   Nada Lavrač

Research interests

  • machine learning, data mining, inductive logic programming, computational creativity
  • applications in medicine, public health, bioinformatics and management of virtual enterprises

Selected activities

Teaching

Books

Impact

Citation indices by Google Scholar (November 2021)

  • Number of citations: 15577
  • h-index: 53
  • i10-index: 196

Selected publications (Google Scholar | COBISS)

  • Blaž Škrlj, Nika Eržen, Nada Lavrač, Tanja Kunej, Janez Konc
  • CaNDis : a web server for investigation of causal relationships between diseases, drugs, and drug targets
  • Bioinformatics, Volume 36, no. 6, Pages 885-887, 2021
  • [doi] [pdf]
  • Marina Dermastia, Blaž Škrlj, Rebeka Strah, Barbara Anžič, Špela Tomaž, Maja Križnik, Christina Schönhuber, Monika Riedle-Bauer, Živa Ramšak, Marko Petek, Aleš Kladnik, Nada Lavrač, Kristina Gruden, Thomas Roitsch, Günter Brader, Maruša Pompe Novak
  • Differential response of grapevine to Infection with ʼCandidatus Phytoplasma solaniʼ in early and late growing season through complex regulation of mRNA and small RNA transcriptomes
  • International journal of molecular sciences, Volume 22, no. 7, 2021, Pages 3531-1-3531-28
  • [doi] [pdf]
  • Blaž Škrlj, Matej Martinc, Nada Lavrač, Senja Pollak
  • autoBOT : evolving neuro-symbolic representations for explainable low resource text classifcation
  • Machine learning, Volume 110, 2021, Pages 989–1028
  • [doi] [pdf]
  • Blaž Škrlj, Maruša Pompe Novak, Günter Brader, Barbara Anžič, Živa Ramšak, Kristina Gruden, Jan Kralj, Aleš Kladnik, Nada Lavrač, Thomas Roitsch, Marina Dermastia
  • New cross-talks between pathways involved in grapevine infection with ʼCandidatus Phytoplasma solaniʼ revealed by temporal network modelling
  • Plants, Volume 10, issue 4, 2021, Pages 646-1- 646-18
  • [doi] [pdf]
  • Blaž Škrlj, Matej Martinc, Jan Kralj, Nada Lavrač, Senja Pollak
  • tax2vec : constructing interpretable features from taxonomies for short text classification
  • Computer speech & language, Volume 65, 2020, Pages 101104
  • [doi] [pdf]
  • Sebastian Mežnar, Nada Lavrač, Blaž Škrlj
  • SNoRE : scalable unsupervised learning of symbolic node representations
  • IEEE access, Volume 8, 2020, Pages 212568-212588
  • [doi] [pdf]
  • Blaž Škrlj, Jan Kralj, Nada Lavrač
  • Embedding-based Silhouette community detection
  • Machine learning, Volume 109, 2020, Pages 2161–2193
  • [doi] [pdf]
  • Nada Lavrač, Blaž Škrlj, Marko Robnik Šikonja
  • Propositionalization and embeddings : two sides of the same coin
  • Machine learning, Volume 109, no. 7, 2020, Pages 1465-1507
  • [doi] [pdf]
  • Nada Lavrač, Matej Martinc, Senja Pollak, Maruša Pompe Novak, Bojan Cestnik
  • Bisociative literature-based discovery : lessons learned and new word embedding approach
  • New generation computing, Volume 38, 2020, Pages 773-800
  • [doi] [pdf]
  • Matthew Purver, Ping Xiao, Dragana Miljković, Vid Podpečan, Senja Pollak, Jan Kralj, Martin Žnidaršič, Marko Bohanec, Nada Lavrač, Tanja Urbančič, et al.
  • Conceptual representations for computational concept creation
  • ACM computing surveys, Volume 52, no. 1, 2019, Pages 9-1-9-33
  • [doi] [pdf]
  • Blaž Škrlj, Jan Kralj, Nada Lavrač
  • Py3plex toolkit for visualization and analysis of multilayer networks
  • Applied network science, Volume 4, no. 1, 2019, Pages 94-1-94-24
  • [doi] [pdf]
  • Vid Podpečan, Živa Ramšak, Kristina Gruden, Hannu Toivonen, Nada Lavrač
  • Interactive exploration of heterogeneous biological networks with Biomine Explorer
  • Bioinformatics, Volume 35, issue 24, 2019, Pages 5385-5388
  • [doi] [pdf]
  • Pedro Martins, Martin Žnidaršič, Nada Lavrač, et al.
  • Computational creativity infrastructure for online software composition : a conceptual blending use case
  • IBM journal of research and development, Volume 63, no. 1, 2019, Pages 9-1-9-17
  • [doi] [pdf]
  • Dragan Gamberger, Tjaša Stare, Dragana Miljković, Kristina Gruden, Nada Lavrač
  • Discovery of relevant response in infected potato plants from time series of gene expression data
  • Machine learning and knowledge extraction, Volume 1, no. 1, 2019, Pages 401-413
  • [doi] [pdf]
  • Andraž Repar, Vid Podpečan, Anže Vavpetič, Nada Lavrač, Senja Pollak
  • An ensemble learning approach to bilingual term extraction and alignment
  • Terminology, Volume 25, no. 1, 2019, Pages 93-120
  • [doi] [pdf]
  • Anita Valmarska, Dragana Miljković, Spyros Konitsiotis, Dimitros Gatsios, Nada Lavrač, Marko Robnik Šikonja
  • Symptoms and medications change patterns for Parkinson's disease patients stratification
  • Artificial intelligence in medicine, Volume 91, 2018, Pages 82-95
  • [doi] [pdf]
  • Anita Valmarska, Nada Lavrač, Johannes Fürnkranz, Marko Robnik Šikonja
  • Refinement and selection heuristics in subgroup discovery and classification rule learning
  • Expert systems with applications, Volume 81, 2017, Pages 147–162
  • [doi] [pdf]
  • Donatella Gubiani, Elsa Fabbretti, Bojan Cestnik, Nada Lavrač, Tanja Urbančič
  • Outlier based literature exploration for cross-domain linking of Alzheimer's disease and gut microbiota
  • Expert systems with applications, Volume 85, 2017, Pages 386–396
  • [doi] [pdf]
  • Janez Kranjc, Roman Orač, Vid Podpečana, Nada Lavrač, Marko Robnik-Šikonja
  • ClowdFlows: Online workflows for distributed big data mining
  • Future Generation Computer Systems, Volume 68, March 2017, Pages 38–58
  • [doi] [pdf]
  • Bojan Cestnik, Elsa Fabbretti, Donatella Gubiani, Tanja Urbančič, Nada Lavrač
  • Reducing the search space in literature-based discovery by exploring outlier documents : a case study in finding links between gut microbiome and Alzheimer's disease
  • Genomics and computational biology, Volume 3, no. 3, 2017, Pages e58-1-e58-10
  • [doi] [pdf]
  • Matej Mihelčič, Goran Šimić, Mirjana Babić Leko, Nada Lavrač, Sašo Džeroski, Tomislav Šmuc
  • Using redescription mining to relate clinical and biological characteristics of cognitively impaired and Alzheimer's disease patients
  • PloS one, Volume 12, no. 10, 2017, Pages 0187364 -1- 0187364 -35
  • [doi] [pdf]
  • Dragan Gamberger, Nada Lavrač, et al.
  • Identification of clusters of rapid and slow decliners among subjects at risk for Alzheimer's disease
  • Scientific reports, Volume 7, 2017, Pages 1-12
  • [doi] [pdf]
  • Matic Perovšek, Janez Kranjca, Tomaž Erjavec, Bojan Cestnik, Nada Lavrač
  • TextFlows: A visual programming platform for text mining and natural language processing
  • Science of Computer Programming, 2016, 121:128-152
  • [link] [pdf]
  • Prem Raj Adhikari, Anže Vavpetič, Jan Kralj, Nada Lavrač, Jaakko Hollmén
  • Explaining mixture models through semantic pattern mining and banded matrix visualization
  • Machine Learning, 2016, 105:3-39
  • [link] [pdf]
  • Dragan Gamberger, Bernard Ženko, Alexis Mitelpunkt, Netta Shachar, Nada Lavrač
  • Clusters of male and female Alzheimer’s disease patients in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database
  • Brain Informatics, 2016, 105:3-39
  • [link] [pdf]
  • Janez Kranjc, Jasmina Smailović, Vid Podpečan, Miha Grčar, Martin Žnidaršič, Nada Lavrač
  • Active learning for sentiment analysis on data streams: Methodology and workflow implementation in the ClowdFlows platform
  • Information Processing and Management, 2015, 17 pgs.
  • [link] [pdf]
  • Matic Perovšek, Anže Vavpetič, Janez Kranjc, Bojan Cestnik, Nada Lavrač
  • Wordification: Propositionalization by unfolding relational data into bags of words
  • Expert Systems with Applications, 2015, 15 pgs.
  • [link] [pdf]
  • Borut Sluban, Nada Lavrač
  • Relating ensemble diversity and performance: A study in class noise detection
  • Neurocomputing, 2015, 12 pgs.
  • [link] [pdf]
  • Dragana Miljković, Vid Podpečan, Tjaša Stare, Igor Mozetič, Kristina Gruden, Nada Lavrač
  • Incremental construction of biological networks by relation extraction from literature
  • Current Bioinformatics, 2014, 23 pgs.
  • [link] [pdf]
  • Dany Morisset, Petra Kralj Novak, Darko Zupanič, Kristina Gruden, Nada Lavrač, Jana Žel
  • GMOseek: a user friendly tool for optimized GMO testing
  • BMC Bioinformatics, 2014, 13 pgs.
  • [link] [pdf]
  • Jasmina Smailović, Miha Grčar, Nada Lavrač, Martin Žnidaršič
  • Stream-based active learning for sentiment analysis in the financial domain
  • Information Sciences, 2014, 23 pgs.
  • [link] [pdf]
  • Nada Lavrač, Petra Kralj Novak
  • Relational and semantic data mining for biomedical research
  • Informatica, 2013, vol. 37, no. 1, pg. 35-39
  • [link] [pdf]
  • Laura Langohr, Vid Podpečan, Marko Petek, Igor Mozetič, Kristina Gruden, Nada Lavrač, Hannu Toivonen
  • Contrasting subgroup discovery
  • The Computer Journal, 2012, 15 pgs.
  • [link] [pdf]
  • Miha Grčar, Nejc Trdin, Nada Lavrač
  • A Methodology for Mining Document-Enriched Heterogeneous Information Networks
  • The Computer Journal, 2012, 15 pgs.
  • [link] [pdf]
  • Ingrid Petrič, Bojan Cestnik, Nada Lavrač, Tanja Urbančič
  • Outlier detection in cross-context link discovery for creative literature mining
  • The Computer Journal,(2012) 55 (1): 47-61
  • [link] [pdf]
  • Vid Podpečan, Monika Zemenova, Nada Lavrač
  • Orange4WS environment for service-oriented data mining
  • The Computer Journal, 2012, 55 (1): 82-98
  • [link] [pdf]
  • Dragana Miljković, Tjaša Stare, Igor Mozetič, Vid Podpečan, Marko Petek, Kamil Witek, Marina Dermastia, Nada Lavrač, Kristina Gruden
  • Signalling network construction for modelling plant defence response
  • PloS one, 2012, vol. 7, no. 12, pg. e51822-1e51822-18
  • [link] [pdf]
  • Senja Pollak, Roel Coesemans, Walter Daelemans, Nada Lavrač
  • Detecting contrast patterns in newspaper articles by combining discourse analysis and text mining
  • Pragmatics (Wilrijk), 2011, vol. 21, no. 4, pg. 647-683.
  • [pdf]
  • Matjaž Juršič, Igor Mozetič, Tomaž Erjavec, Nada Lavrač
  • LemmaGen : multilingual lemmatisation with induced Ripple-Down rules
  • Journal of Universal Computer Science, 2010, vol. 16, no. 9, pg. 1190-1214
  • [pdf]
  • Ana Rotter, Petra Kralj Novak, Špela Baebler, Nataša Toplak, Andrej Blejec, Nada Lavrač, Kristina Gruden
  • Gene expression data analysis using closed itemset mining for labeled data
  • Omics (Larchmt. N.Y.), 2010, vol. 14, no. 2, pg. 177-186.
  • [pdf]
  • Vid Podpečan, Nada Lavrač, Igor Mozetič, Petra Kralj Novak, Igor Trajkovski, Laura Langohr, Kimmo Kulovesi, Hannu Toivonen, Marko Petek, Helena Motaln, Kristina Gruden
  • SegMine workflows for semantic microarray data analysis in Orange4WS
  • BMC bioinformatics, 2011, vol. 12, no. 416, pg. 416-1-416-16
  • [link] [pdf]
  • Petra Kralj Novak, Kristina Gruden, Dany Morisset, Nada Lavrač, Dejan Štebih, Ana Rotter, Jana Žel
  • GMOtrack : generator of cost-effective GMO testing strategies
  • The Journal of AOAC INTERNATIONAL, 2009, vol. 92, no. 6, pg. 1739-1746
  • [pdf]
  • Petra Kralj Novak, Nada Lavrač, Geoffrey I. Webb
  • Supervised descriptive rule discovery : a unifying survey of contrast set, emerging pattern and subgroup mining
  • Journal of Machine Learning Research, 2009, vol. 10, pg. 377-403
  • [link] [pdf]
  • Petra Kralj Novak, Nada Lavrač, Dragan Gamberger, Antonija Krstačić
  • CSM-SD: methodology for contrast set mining through subgroup discovery
  • Journal of Biomedical Informatics, 2009, vol. 42, no. 1, pg. 113-122
  • [link] [pdf]
  • Tom Ruttnik, Dany Morisset, Bart Van Droogenbroeck, Nada Lavrač, Guy Van Den Ende, Jana Žel, Marc De Loose
  • Knowledge-technology-based discovery of unauthorized genetically modified organisms
  • Analytical and Bioanalytical Chemistry, 2009, 9 pg.
  • [link] [pdf]
  • Joël Plisson, Nada Lavrač, Dunja Mladenić, Tomaž Erjavec
  • Ripple down rule learning for automated word lemmatisation
  • AI Communications, 2008, vol. 21, no. 1, pg. 15-26
  • [link] [pdf]
  • Nada Lavrač, Marko Bohanec, Aleksander Pur, Bojan Cestnik, Marko Debeljak, Andrej Kobler
  • Data mining and visualization for decision support and modeling of public health-care resources
  • Journal of Biomedical Informatics, 2007, vol.40, no. 4, pg. 438-447
  • [link] [pdf]
  • Dragan Gamberger, Nada Lavrač, Antonija Krstačić
  • Clinical data analysis based on iterative subgroup discovery : experiments in brain ischaemia data analysis
  • Applied Intelligence, 2007, vol. 27, no. 3, pg. 205-217
  • [link] [pdf]
  • Filip Železný, Nada Lavrač
  • Propositionalization-based relational subgroup discovery with RSD
  • Machine Learning, 2006, vol. 62, no. 1-2, pg. 33-63
  • [link] [pdf]
  • Petra Kralj Novak, Ana Rotter, Nataša Toplak, Kristina Gruden, Nada Lavrač, Gemma C. Garriga
  • Application of closed itemset mining for class labeled data in functional genomics
  • Informatica medica slovenica, 2006, year 11, no. 1, pg. 40-45
  • [link] [pdf]
  • Peter Ljubič, Nada Lavrač, Dunja Mladenić, Joël Plisson, Igor Mozetič
  • Automated structuring of company profiles
  • Metodološki zvezki, 2006, Volume 3, no. 2, pg. 369-380
  • [link] [pdf]
  • Nada Lavrač, Bojan Cestnik, Dragan Gamberger, Peter A. Flach
  • Decision support through subgroup discovery : three case studies and the lessons learned
  • Machine Learning, 2004, vol. 57, pg. 115-143
  • [link] [pdf]
  • Nada Lavrač, Hiroshi Motoda, Tom Fawcett, Robert C. Holte, Pat Langley, Pieter Adriaans
  • Introduction : lessons learned from data mining applications and collaborative problem solving
  • Machine Learning, 2004, vol. 57, pg. 13-34
  • [link] [pdf]
  • Dragan Gamberger, Nada Lavrač, Filip Železný, Jakub Tolar
  • Induction of comprehensible models for gene expression datasets by subgroup discovery methodology
  • Journal of biomedical informatics, 2004, vol. 37, pg. 269-284
  • [link] [pdf]
  • Mitja Jermol, Nada Lavrač, Tanja Urbančič
  • Managing business intelligence in a virtual enterprise : a case study and knowledge management lessons learned
  • Journal of intelligent & fuzzy systems, 2004, vol. 14, pg. 121-136
  • [link]
  • Dragan Gamberger, Nada Lavrač, Goran Krstačić
  • Confirmation rule induction and its applications to coronary heart disease diagnosis and risk group discovery
  • Journal of intelligent & fuzzy systems, 2002, vol. 12, pg. 35-48
  • [link]
  • Tadej Bajd, Alojz Kralj, Martin Štefančič, Nada Lavrač
  • Use of functional electrical stimulation in the lower extremities of incomplete spinal cord injured patients
  • Artificial organs, 1999, vol. 23, no. 5, pg. 403-409
  • [link] [pdf]
  • Igor Zelič, Igor Kononenko, Nada Lavrač, Vanja Vuga
  • Induction of decision trees and Bayesian classification applied to diagnosis of sport injuries
  • Journal of medical systems, 1997, vol. 21, pg. 429-444
  • [link] [pdf]
  • Nada Lavrač, Sašo Džeroski, Vladimir Pirnat, Viljem Križman
  • The utility of background knowledge in learning medical diagnostic rules
  • Applied artificial intelligence, 1993, vol. 7, no. 3, pg. 273-293
  • [link] [pdf]
  • Nada Lavrač, Igor Mozetič
  • Methods for knowledge acquisition and refinement in second generation expert Systems
  • SIGART newsletter, 1989, vol. 108, pg. 63-69
  • [link] [pdf]
  • M. Olave, Nada Lavrač, Bojan Cestnik
  • Application of expert systems to management control systems in public enterprises of developing countries
  • Omega, 1988, vol. 16, pg. 353-362
  • [link] [pdf]
  • Nada Lavrač, Ivan Bratko, Igor Mozetič, B. Čerček, A. Grad, M. Horvat
  • KARDIO-E - An expert system for electrocardiographic diagnosis of cardiac arrhythmias
  • Expert systems, 1985, vol. 2, pg. 46-50
  • [link] [pdf]

Short CV

Prof. Nada Lavrac is Research Councillor and former Head of the Department of Knowledge Technologies (2004-2020), was Head of Intelligent Data Analysis and Computational Linguistics research group (in 1999-2003) at the Department of Intelligent Systems, and researcher of Jožef Stefan Institute, Ljubljana, Slovenia (since 1978). She is Full Professor at University of Nova Gorica and Head of Information and Communication Technologies Program at Jozef Stefan International Postgraduate School (since 2016). She was visiting professor at Bristol University, UK (1997-2002, teaching parts of courses Introduction to Machine Learning and Learning from structured Data) and at Klagenfurt University, Austria (1987-2002, teaching courses on Knowledge Acquisition, Data Mining and Decision Support).

She received a BSc in Technical Mathematics and MSc in Computer Science from Ljubljana University, and a PhD in Technical Sciences from Maribor University, Slovenia. In 1984 she was in a group of researchers who were awarded a national prize for research excellence, in 1997 she was awarded the Ambassador of Science of Slovenia prize, in 2007 she has been elected ECCAI Fellow, and in 2013 she received the Zois Recognition Award for important contributions to science, research and development in the area of intelligent data analysis.

Her main research interest is machine learning and data mining, in particular inductive logic programming and intelligent data analysis in medicine. She is coauthor of KARDIO: A Study in Deep and Qualitative Knowledge for Expert Systems, The MIT Press 1989, and Inductive Logic Programming: Techniques and Applications, Ellis Horwood 1994, and co-editor of Relational Data Mining, Springer 2001, Intelligent Data Analysis in Medicine and Pharmacology, Kluwer 1997. She was founder the Solomon European Network and acted as co-coordinator of the EU 5th Framework project Data Mining and Decision Support for Business Competitiveness: A European Virtual Enterprise (Sol-Eu-Net, IST-1999-11495, 2000-2003). She was coordinator of the European Scientific Network in Inductive Logic Programming ILPNET (1993-1996). She is member of editorial boards of Artificial Intelligence in Medicine AI Communications New Generation Computing Applied AI Machine Learning Journal and Data Mining and Knowledge Discovery. She was vice-president of ECCAI (1996-98), and is member of the International Machine Learning Society board (IMLS, 2001-2005), and Artificial Intelligence in Medicine board (AIME, 1999-2015).

Longer CV