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The pathways by which oncogenes, such as MLL-AF9, initiate transformation and leukemia in humans and mice are incompletely defined. In a study of target cells and oncogene dosage, we found that Mll-AF9, when under endogenous regulatory control, efficiently transformed LSK (Lin(-)Sca1(+)c-kit(+)) stem cells, while committed granulocyte-monocyte progenitors(More)
The 2 most frequent human MLL hematopoietic malignancies involve either AF4 or AF9 as fusion partners; each has distinct biology but the role of the fusion partner is not clear. We produced Mll-AF4 knock-in (KI) mice by homologous recombination in embryonic stem cells and compared them with Mll-AF9 KI mice. Young Mll-AF4 mice had lymphoid and myeloid(More)
Leukemias with MLL rearrangements are characterized by high expression of the homeobox gene MEIS1. In these studies, we knocked down Meis1 expression by shRNA lentivirus transduction in murine Mll-AF9 leukemia cells. Meis1 knockdown resulted in decreased proliferation and survival of murine Mll-AF9 leukemia cells. We also observed reduced clonogenic(More)
PURPOSE We conducted studies to evaluate the hypothesis that FLT3 is a client of heat shock protein (Hsp) 90 and inhibitors of Hsp90 may be useful for therapy of leukemia. EXPERIMENTAL DESIGN The effects of the Hsp90-inhibitor 17-allylamino-17-demethoxygeldanamycin (17-AAG) on cell growth, expression of signal transduction kinases, apoptosis, FLT3(More)
However, many text mining applications do not have adequate natural language processing ability beyond simple keyword indexing, and as a result, there are too many textual elements (words) included in the analysis. We argue that noun phrases as textual elements are better suited for text mining and could provide more discriminating power, than single words.(More)
Effectiveness and efficiency of searching and returned results presentation is the key to a search engine. Before downloading and examining the document text, users usually first judge the relevance of a return hit to the query by looking at document metadata presented in the return result. However, the metadata coming with the return hit is usually not(More)
In this paper, we propose the first real time rumor debunking algorithm for Twitter. We use cues from 'wisdom of the crowds', that is, the aggregate 'common sense' and investigative journalism of Twitter users. We concentrate on identification of a rumor as an event that may comprise of one or more conflicting microblogs. We continue monitoring the rumor(More)
We report a keyphrase identification program (KIP), which uses sample human keyphrases and then learns to identify additional new keyphrases. KIP first populates its database using manually identified keyphrases; each keyphrase is preprocessed and assigned an initial weight. It then extracts noun phrases from documents. All noun phrases will be assigned a(More)
Automated medical concept recognition is important for medical informatics such as medical document retrieval and text mining research. In this paper, we present a software tool called keyphrase identification program (KIP) for identifying topical concepts from medical documents. KIP combines two functions: noun phrase extraction and keyphrase(More)
Most search engines display some document metadata, such as title, snippet and URL, in conjunction with the returned hits to aid users in determining documents. However, metadata is usually fragmented pieces of information that, even when combined, does not provide an overview of a returned document. In this paper, we propose a mechanism of enriching(More)