50 citations · 199 across the 14 of their papers we have counts for
15 papers
DDeMON: Ontology-based function prediction by Deep Learning from Dynamic Multiplex Networks
Jan Kralj, Blaž Škrlj, Živa Ramšak +2
Biological systems can be studied at multiple levels of information, including gene, protein, RNA and different interaction networks levels. The goal of this work is to explore how…
Link Analysis meets Ontologies: Are Embeddings the Answer?
Sebastian Mežnar, Matej Bevec, Nada Lavrač +1
The increasing amounts of semantic resources offer valuable storage of human knowledge; however, the probability of wrong entries increases with the increased size. The development…
Prioritization of COVID-19-related literature via unsupervised keyphrase extraction and document representation learning
Blaž Škrlj, Marko Jukič, Nika Eržen +2
The COVID-19 pandemic triggered a wave of novel scientific literature that is impossible to inspect and study in a reasonable time frame manually. Current machine learning methods…
Semantic Reasoning from Model-Agnostic Explanations
Timen Stepišnik Perdih, Nada Lavrač, Blaž Škrlj
With the wide adoption of black-box models, instance-based \emph{post hoc} explanation tools, such as LIME and SHAP became increasingly popular. These tools produce explanations, p…
ReliefE: Feature Ranking in High-dimensional Spaces via Manifold Embeddings
Blaž Škrlj, Sašo Džeroski, Nada Lavrač +1
Feature ranking has been widely adopted in machine learning applications such as high-throughput biology and social sciences. The approaches of the popular Relief family of algorit…
SNoRe: Scalable Unsupervised Learning of Symbolic Node Representations
Sebastian Mežnar, Nada Lavrač, Blaž Škrlj
Learning from complex real-life networks is a lively research area, with recent advances in learning information-rich, low-dimensional network node representations. However, state-…
COVID-19 therapy target discovery with context-aware literature mining
Matej Martinc, Blaž Škrlj, Sergej Pirkmajer +4
The abundance of literature related to the widespread COVID-19 pandemic is beyond manual inspection of a single expert. Development of systems, capable of automatically processing…
Propositionalization and Embeddings: Two Sides of the Same Coin
Nada Lavrač, Blaž Škrlj, Marko Robnik-Šikonja
Data preprocessing is an important component of machine learning pipelines, which requires ample time and resources. An integral part of preprocessing is data transformation into t…
Feature Importance Estimation with Self-Attention Networks
Blaž Škrlj, Sašo Džeroski, Nada Lavrač +1
Black-box neural network models are widely used in industry and science, yet are hard to understand and interpret. Recently, the attention mechanism was introduced, offering insigh…
Symbolic Graph Embedding using Frequent Pattern Mining
Blaz Škrlj, Jan Kralj, Nada Lavrač
Relational data mining is becoming ubiquitous in many fields of study. It offers insights into behaviour of complex, real-world systems which cannot be modeled directly using propo…
Embedding-based Silhouette Community Detection
Blaž Škrlj, Jan Kralj, Nada Lavrač
Mining complex data in the form of networks is of increasing interest in many scientific disciplines. Network communities correspond to densely connected subnetworks, and often rep…
Deep Node Ranking for Neuro-symbolic Structural Node Embedding and Classification
Blaž Škrlj, Jan Kralj, Janez Konc +2
Network node embedding is an active research subfield of complex network analysis. This paper contributes a novel approach to learning network node embeddings and direct node class…
tax2vec: Constructing Interpretable Features from Taxonomies for Short Text Classification
Blaž Škrlj, Matej Martinc, Jan Kralj +2
The use of background knowledge is largely unexploited in text classification tasks. This paper explores word taxonomies as means for constructing new semantic features, which may…
Using Redescription Mining to Relate Clinical and Biological Characteristics of Cognitively Impaired and Alzheimer's Disease Patients
Matej Mihelčić, Goran Šimić, Mirjana Babić Leko +3
We used redescription mining to find interpretable rules revealing associations between those determinants that provide insights about the Alzheimer's disease (AD). We extended the…
A framework for redescription set construction
Matej Mihelčić, Sašo Džeroski, Nada Lavrač +1
Redescription mining is a field of knowledge discovery that aims at finding different descriptions of similar subsets of instances in the data. These descriptions are represented a…