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Semantic-enabled hybrid genetic disease diagnostics in Next-Generation Sequenced data

Zawadzka-Gosk E., Wołk K.

Computer Science

2018

Vol. 19 (2)

179--199

Next Generation Sequencing is a technology for genome sequencing used in genetics for the diagnosis of disease. NGS provides a list of all mutations in a genome, so identifying the one that causes a disease is not trivial. A number of applications for variant prioritization were developed, but the data they provide is a suggestion rather than a diagnosis; moreover, they sufer from issues such as identifying a nonpathogenic variant as a causal one or the inability to identify a causal gene. These issues inspired us to create a strategy for variant prioritization, which includes the use of the Exomiser and OMIM Explorer result sets improved by semantic analysis of abstracts and articles freely available from the PubMed and PubMed Central databases. For the wider scope of scientific articles, the Google Scholar repository will be used. The described approach enables us to present the latest and most accurate information about potential pathogenic variants.

Comparison and adaptation of automatic evaluation metrics for quality assessment of re-speaking

Wołk K., Korzinek D.

Computer Science

2017

Vol. 18 (2)

129--144

Re-speaking is a mechanism for obtaining high-quality subtitles for use in live broadcasts and other public events. Because it relies on humans to perform the actual re-speaking, the task of estimating the quality of the results is non- trivial. Most organizations rely on human effort to perform the actual quality assessment, but purely automatic methods have been developed for other similar problems (like Machine Translation). This paper will try to compare several of these methods: BLEU, EBLEU, NIST, METEOR, METEOR-PL, TER, and RIBES. These will then be matched to the human-derived NER metric, commonly used in re-speaking. The purpose of this paper is to assess whether the above automatic metrics normally used for MT system evaluation can be used in lieu of the manual NER metric to evaluate re-speaking transcripts.

Noisy-parallel and comparable corpora filtering methodology for the extraction of bi-lingual equivalent data at sentence level

Wołk K.

Computer Science

2015

Vol. 16 (2)

169--184

Text alignment and text quality are critical to the accuracy of Machine Translation (MT) systems, some NLP tools, and any other text processing tasks requiring bilingual data. This research proposes a language-independent bisentence filtering approach based on Polish (not a position-sensitive language) to English experiments. This cleaning approach was developed on the TED Talks corpus and also initially tested on the Wikipedia comparable corpus, but it can be used for any text domain or language pair. The proposed approach implements various heuristics for sentence comparison. Some of the heuristics leverage synonyms as well as semantic and structural analysis of text as additional information. Minimization of data loss has been? ensured. An improvement in MT system scores with text processed using this tool is discussed.