ABSTRACT Greek to Greeklish transcription does not appear to be a difficult task since it can be ... more ABSTRACT Greek to Greeklish transcription does not appear to be a difficult task since it can be achieved by directly mapping each Greek character to a corresponding symbol of the Latin alphabet. Nevertheless, such transliteration systems do not simulate efficiently the human way of Greeklish writing, since Greeklish users do not follow a standardized way of transliteration. In this paper a stochastic Greek to Greeklish transcriber modeled by real user data is presented. The proposed transcriber employs knowledge derived from the analytical processing of 9,288 Greek-Greeklish word pairs annotated by real users and achieves the automatic transcription of any Greek word into a valid Greeklish form in a stochastic way (i.e. each Greek symbolset corresponds to a variety of Latin symbols according to the processed data), simulating thus human-like behavior. This transcriber could be used as a real-time Greek-to-Greeklish transcriber and/or as a data generator engine used for the performance evaluation of Greeklish-to-Greek transliteration systems.
... w(t). In order to create the FSTs, we use an algorithm that was first developed for construct... more ... w(t). In order to create the FSTs, we use an algorithm that was first developed for constructing Directed Acyclic Word Graphs (DAWGs) and then ... that can be predicted by the language model are: θέλω να μιλήσω με το {γιάννη, διονύση} I want to speak with {john, dennis} θα ήθελα ...
Towards unraveling the influenza A (H1N1) immunome, this work aims at constructing the murine hos... more Towards unraveling the influenza A (H1N1) immunome, this work aims at constructing the murine host response pathway interactome. To accomplish that, an ensemble of dynamic and time-varying Gene Regulatory Network Inference methodologies was recruited to set a confident interactome based on mouse time series transcriptome data (day 1-day 60). The proposed H1N1 interactome demonstrated significant transformations among activated and suppressed pathways in time. Enhanced interplay was observed at day 1, while the maximal network complexity was reached at day 8 (correlated with viral clearance and iBALT tissue formation) and one interaction was present at day 40. Next, we searched for common interactivity features between the murine-adapted PR8 strain and other influenza A subtypes/strains. For this, two other interactomes, describing the murine host response against H5N1 and H1N1pdm, were constructed, which in turn validated many of the observed interactions (in the period day 1-day 7). The H1N1 interactome revealed the role of cell cycle both in innate and adaptive immunity (day 1-day 14). Also, pathogen sensory pathways (e.g., RIG-I) displayed long-lasting association with cytokine/chemokine signaling (until day 8). Interestingly, the above observations were also supported by the H5N1 and H1N1pdm models. It also elucidated the enhanced coupling of the activated innate pathways with the suppressed PPAR signaling to keep low inflammation until viral clearance (until day 14). Further, it showed that interactions reflecting phagocytosis processes continued long after the viral clearance and the establishment of adaptive immunity (day 8-day 40). Additionally, interactions involving B cell receptor pathway were evident since day 1. These results collectively inform the emerging field of public health omics and future clinical studies aimed at deciphering dynamic host responses to infectious agents.
ABSTRACT Greek to Greeklish transcription does not appear to be a difficult task since it can be ... more ABSTRACT Greek to Greeklish transcription does not appear to be a difficult task since it can be achieved by directly mapping each Greek character to a corresponding symbol of the Latin alphabet. Nevertheless, such transliteration systems do not simulate efficiently the human way of Greeklish writing, since Greeklish users do not follow a standardized way of transliteration. In this paper a stochastic Greek to Greeklish transcriber modeled by real user data is presented. The proposed transcriber employs knowledge derived from the analytical processing of 9,288 Greek-Greeklish word pairs annotated by real users and achieves the automatic transcription of any Greek word into a valid Greeklish form in a stochastic way (i.e. each Greek symbolset corresponds to a variety of Latin symbols according to the processed data), simulating thus human-like behavior. This transcriber could be used as a real-time Greek-to-Greeklish transcriber and/or as a data generator engine used for the performance evaluation of Greeklish-to-Greek transliteration systems.
... w(t). In order to create the FSTs, we use an algorithm that was first developed for construct... more ... w(t). In order to create the FSTs, we use an algorithm that was first developed for constructing Directed Acyclic Word Graphs (DAWGs) and then ... that can be predicted by the language model are: θέλω να μιλήσω με το {γιάννη, διονύση} I want to speak with {john, dennis} θα ήθελα ...
Towards unraveling the influenza A (H1N1) immunome, this work aims at constructing the murine hos... more Towards unraveling the influenza A (H1N1) immunome, this work aims at constructing the murine host response pathway interactome. To accomplish that, an ensemble of dynamic and time-varying Gene Regulatory Network Inference methodologies was recruited to set a confident interactome based on mouse time series transcriptome data (day 1-day 60). The proposed H1N1 interactome demonstrated significant transformations among activated and suppressed pathways in time. Enhanced interplay was observed at day 1, while the maximal network complexity was reached at day 8 (correlated with viral clearance and iBALT tissue formation) and one interaction was present at day 40. Next, we searched for common interactivity features between the murine-adapted PR8 strain and other influenza A subtypes/strains. For this, two other interactomes, describing the murine host response against H5N1 and H1N1pdm, were constructed, which in turn validated many of the observed interactions (in the period day 1-day 7). The H1N1 interactome revealed the role of cell cycle both in innate and adaptive immunity (day 1-day 14). Also, pathogen sensory pathways (e.g., RIG-I) displayed long-lasting association with cytokine/chemokine signaling (until day 8). Interestingly, the above observations were also supported by the H5N1 and H1N1pdm models. It also elucidated the enhanced coupling of the activated innate pathways with the suppressed PPAR signaling to keep low inflammation until viral clearance (until day 14). Further, it showed that interactions reflecting phagocytosis processes continued long after the viral clearance and the establishment of adaptive immunity (day 8-day 40). Additionally, interactions involving B cell receptor pathway were evident since day 1. These results collectively inform the emerging field of public health omics and future clinical studies aimed at deciphering dynamic host responses to infectious agents.
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Papers by K. Sgarbas