Assessment of Dendritic Cell Therapy Effectiveness Based on the Feature Extraction from Scientific Publications
Dendritic cells (DCs) vaccination is a promising way to contend cancer metastases especially in the case of immunogenic tumors. Unfortunately, it is only rarely possible to achieve a satisfactory clinical outcome in the majority of patients treated with a particular DC vaccine. Apparently, DC vaccination can be successful with certain combinations of features of the tumor and patients immune system that are not yet fully revealed. Difficulty in predicting the results of the therapy and high price of preparation of individual vaccines prevent wider use of DC vaccines in medical practice. Here we propose an approach aimed to uncover correlation between the effectiveness of specific DC vaccine types and personal characteristics of patients to increase efficiency of cancer treatment and reduce prices. To accomplish this, we suggest two-step analysis of published clinical trials results for DCs vaccines: first, the information extraction subsystem is trained, and, second, the extracted data is analyzed using JSM and AQ methodology.
This study is dedicated to the introduction of a novel method that automatically extracts potential structural alerts from a data set of molecules. These triggering structures can be further used for knowledge discovery and classification purposes. Computation of the structural alerts results from an implementation of a sophisticated workflow that integrates a graph mining tool guided by growth rate and stability. The growth rate is a well-established measurement of contrast between classes. Moreover, the extracted patterns correspond to formal concepts; the most robust patterns, named the stable emerging patterns (SEPs), can then be identified thanks to their stability, a new notion originating from the domain of formal concept analysis. All of these elements are explained in the paper from the point of view of computation. The method was applied to a molecular data set on mutagenicity. The experimental results demonstrate its efficiency: it automatically outputs a manageable number of structural patterns that are strongly related to mutagenicity. Moreover, a part of the resulting structures corresponds to already known structural alerts. Finally, an in-depth chemical analysis relying on these structures demonstrates how the method can initiate promising processes of chemical knowledge discovery. © 2015 American Chemical Society.
This paper concerns discourse-new mention detection in Russian. This might be helpful for different NLP applications such as coreference resolution, protagonist identification, summarization and different tasks of information extraction to detect the mention of an entity newly introduced into discourse. In our work, we are dealing with the Russian where there is no grammatical devices, like articles in English, for the overt marking a newly introduced referent. Our aim is to check the impact of various features on this task. The focus is on specific devices for introducing a new discourse prominent referent in Russian specified in theoretical studies. We conduct a pilot study of features impact and provide a series of experiments on detecting the first mention of a referent in a non-singleton coreference chain, drawing on linguistic insights about how a prominent entity introduced into discourse is affected by structural, morphological and lexical features.
The 13rd IEEE International Conference on Data Mining (IEEE ICDM 2013) has solicited workshops on topics related to new research directions and novel applications of data mining. The goal of the ICDM workshops program (IEEE ICDMW) is to identify grand challenges in data mining, to explore the possible paths to address these urgent problems, and to solicit broad participation from the data mining community and other relevant research communities. IEEE ICDMW 2013 was held on December 7 in Dallas, Texas, USA, and was immediately followed by IEEE ICDM 2013. This year, we have received 41 workshop proposals, a 141% increase from the number of proposals in the previous year. Of those submissions, 26 workshop proposals were accepted through a thorough review by the ICDMW workshop organization committee. 18 workshops eventually made their way to prepare their workshop programs after a rigorous paper review process. The final program consisted of 13 full-day workshops and 5 halfday workshops. Overall, the ICDMW Program received 364 submissions, which is a 19% increase from the number of submissions in the previous year. Of those submissions, 183 papers were accepted. The workshop proposal acceptance rate is about 44%, and the workshop papers acceptance rate is about 50%. The highly competitive acceptance rates have resulted in the highquality and exciting ICDMW proceedings. IEEE ICDMW 2013 covered many new research and application areas as well as fundamental data mining topics. The traditional and fundamental disciplines included spatial and spatiotemporal data mining, optimization, concept drift, domain driven data mining, opinion mining, and sentiment analysis. Emerging disciplines included high-dimensional data mining, causal discovery, cloud and distributed computing, data mining in service applications, and of course, big data. IEEE ICDMW 2013 provided discussion forums for exciting applications including biological data mining in healthcare, data mining in networks, data privacy, and data mining case studies. The ICDMW Program also explored new areas of data markets in sciences and businesses, data mining in experimental economics, and data mining in astronomical problems. Many people worked together in organizing IEEE ICDMW 2013. We would like to thank all workshop organizers for the high-quality workshop proposals received. The workshop organizers are the key to the success of the ICDMW program. We should thank them all for their tremendous effort putting together 18 exciting workshops in the final program.
Nowadays, a field of dialogue systems and conversational agents is one of the rapidly growing research areas in artificial intelligence applications. Business and industry are showing increasing interest in implementing intelligent conversational agents into their products. Many recent studies has tended to focus on possibility of developing task-oriented systems which are able to have long and free social chats that occur naturally in social human interactions. In order to better understand the user’s expression, and then feedback the correct information, natural language understanding plays an extremely important role. Despite progress made in solving NLP problems, it remains very challenging today in the field of dialogue systems. In this paper, we review the recent progress in developing dialogue systems, its current architecture features and further prospects. We focus on the natural language understanding tasks which are key for building a good conversational agent, and than we are summarizing NLP methods and frameworks, in order to allow researchers to study the potential improvements of the state-of-the-art dialogue systems. Additionally, we consider the dialogue concept in context of human-machine interaction, and briefly describe dialogue evaluation metrics.