High-throughput screening (HTS) has become an important part of drug discovery at most pharmaceutical and many biotechnology companies worldwide, and use of HTS technologies is expanding into new areas. Target validation, assay development, secondary screening, ADME/Tox, and lead optimization are among the areas in which there is an increasing use of HTS technologies. It is becoming fully integrated within drug discovery, both upstream and downstream, which includes increasing use of cell-based assays and high-content screening (HCS) technologies to achieve more physiologically relevant results and to find higher quality leads. In addition, HTS laboratories are continually evaluating new technologies as they struggle to increase their success rate for finding drug candidates. The material in this article is based on a 900-page HTS industry report involving 54 HTS directors representing 58 HTS laboratories and 34 suppliers. (Journal of Biomolecular Screening 2006:864-869)
The presence of toxic content has become a major problem for many online communities. Moderators try to limit this problem by implementing more and more refined comment filters, but toxic users are constantly finding new ways to circumvent them. Our hypothesis is that while modifying toxic content and keywords to fool filters can be easy, hiding sentiment is harder. In this paper, we explore various aspects of sentiment detection and their correlation to toxicity, and use our results to implement a toxicity detection tool. We then test how adding the sentiment information helps detect toxicity in three different real-world datasets, and incorporate subversion to these datasets to simulate a user trying to circumvent the system. Our results show sentiment information has a positive impact on toxicity detection.
Sentiment analysis in text mining is a challenging task. Sentiment is subtly reflected by the tone and affective content of a writer's words. Conventional text mining techniques, which are based on keyword frequencies, usually run short of accurately detecting such subjective information implied in the text. In this paper, we evaluate several popular classification algorithms, along with three filtering schemes. The filtering schemes progressively shrink the original dataset with respect to the contextual polarity and frequent terms of a document. We call this approach "hierarchical classification". The effects of the approach in different combination of classification algorithms and filtering schemes are discussed over three sets of controversial online news articles where binary and multi-class classifications are applied. Meanwhile we use two methods to test this hierarchical classification model, and also have a comparison of the two methods.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.