Software

 

The software packages are made available solely for research and educational purposes.
If you wish to use them for commercial purposes, please contact the authors.

  • eeg2bold: deep neural networks for EEG to fMRI synthesis (see MLHC’23 publication)
  • fraudQT: fraud detection using response concordance and answer time (see TCSS’23 publication)
  • micf: multi-input prospect-aware NN forecasters (see DAMI’23 publication)
  • MSpa: descriptors of spatiotemporal dynamics from mobile data (see EPB’23 publication)
  • omics2flux: metabolic flux prediction from transcriptomics and proteomics data (see CSBJ’23 publication)
  • uniano: anomaly consensus in time series sensitive to cross-correlated profiles (see SDM’21 publication)
  • DISA: discriminative pattern assessment in the presence of numerical outcomes (see Plos ONE’23 publication)
  • TriSig: tests to assess the statistical significance of triclustering patterns in tensor (see PR’23 publication)
  • tex2net: storytelling (graphical text summaries) using networks (see SIGDOC’23 publication)
  • whereAMI: route choice estimation in rail transit systems using smart card data (see ETRR’22 publication)
  • DAPI: tool for spatial-aware inefficiency analysis in emergency response data (see ISPRS’22 publication)
  • tlc: time-lagged correlation analysis of shellfish toxicity (see Toxins’23 publication)
  • bpc: context-situated visualization of biclusters against global regularities (see AVI’23 publication)
  • pct: pattern-centric omics transforms for downstream prediction (see bioRxiv 2023.05.28.542574)
  • usbcf: user-specific bicluster-based collaborative filtering (see arxiv 2211.08366)
  • busOpt: optimization of bus networks-schedules from smart cards (see arXiv 2201.11616)
  • bicpams: exploratory and unsupervised analysis of tabular and network data, integrating state-of-the-art contributions on pattern-based biclustering (see BMC’18 publication)
  • iluapp: recommendation system for the context-aware analysis of urban data
  • data2help: platform the descriptive and predictive analysis of emergency occurrences in Portugal
  • autoMTS: autonomous time series processing in sensor networks (see QShine’20 publication)
  • e2pat: emerging pattern discovery from heterogeneous spatiotemporal data (see MobiQuituous’20 publication)
  • schizoeeg: brain signal classification using pairwise learning for schizophrenia diagnosis (see AI in Medicine’20 publication)
  • di2: prior-free and multi-item discretization of biological data (see BMC’21 publication)
  • wisdomapp: platform for multivariate time series analysis and anomaly detection in water distribution systems (see Wate’21)
  • eeg2bold: deep neural networks for EEG to fMRI synthesis
  • rDenoiser: imputation in spatiotemporal data using recurrent denoiser
  • avatar: algorithms integrated within GINsim to find probability estimates of attractor reachability in asynchronous logical models (see FPhys’18 publication)
  • bsig: method to assess the statistical significance of flexible biclustering solutions (see DAMI’17 publication)
  • uOPPM : algorithm to find order-preserving pattern matches over indeterminate strings (see CPM’18 publication)
  • flebic: associative classifiers using discriminative biclusters with flexible properties (under publication)
  • bigen: generator of synthetic data to plant biclusters with parameterizable coherence (type and strength), structure (number, size and positioning) and quality (under publication)
  • bip: flexible plaid models for an effective discovery of complex structures of biclusters with meaningful overlaps (see TCBB’15 publication)
  • bicspam: pattern-based approaches for flexible, efficient and robust order-preserving biclustering based on sequential patterns (see BMC’14 publication)
  • bicpam: pattern-based approach to discover biclusters with multiple patterns of expression, varying quality and alternative underlying structures (see AMB’14 publication)
  • evoc: framework for multi-period classifiers sensitive to temporal patterns (see DAMI’14a and ICIS’12 publications)
  • hi-spm: adapted multivariate hidden Markov models to perform sequential pattern discovery over transactional data (see DAMI’14b and ICIS’14 publications)
  • indexspan efficient discovery of sequential patterns in item-indexable sequences (see nfMCP’13 publication)
  • fine-ally: software, collected data, MBTI tests and statistical results for the emotion-driven interactions (see PhyCS’14 and ACII’13 publications)
  • pmsat: codifications of the frequent itemset problem using propositional satisfiability with performance-enhancing criteria (see this report)
  • edapm: miner of physiological signals using symbolic approximation and HMM-based pattern discovery (see AAMAS’12 publication)

 

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