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---
datasets:
- allenai/scifact
widget:
- text: X-linked genes, particularly those related to chromatin structure/remodeling, segregation, and ribosomal biogenesis and translational control, may also play key regulatory roles in breast carcinogenesis<SEP>While contribution of X chromosome in the susceptibility of prostate and ovarian cancer has been demonstrated, the role of X-linked genes in breast carcinogenesis is not clearly known. This study investigated and compared the X-linked gene expression profiles of MMTV-c-myc transgenic mammary tumor (MT) or MMTV-c-myc/MT-tgf-alpha double transgenic mouse mammary tumor (DT) to lactating mammary gland. cDNA microarray analysis using the Affymetrix system identified 1081 genes localized on the X chromosome with 174 and 194 genes at +/-2-fold change levels in MT and DT samples, respectively. Differentially expressed X-linked genes were predominantly related to chromatin structure/remodeling (e.g., Hdac8, Suv39h1, RbAp46 and Adr1), segregation (e.g., CENP-I and smc111) and, ribosomal biogenesis and translational control (e.g., Dkc1, Rpl44, Rpl39, Eif2s3x, Gspt2 and Rsk4). Confirmation of microarray data by semi-quantitative and quantitative RT-PCR in selected X-linked genes also showed similar pattern. In addition, the expression pattern of two chromosomal regions, XE3 and XF5, suggests that XE3 may have escaped from inactivation and XF5 subjected to inactivation. In conclusion, our data suggest that X-linked genes may play the key regulatory roles in the maintenance of chromatin structure, accurate chromosomal segregation and translational control; hence deregulation of X-linked genes may promote mammary gland tumorigenesis by promoting genetic instability and cell proliferation. Increased understanding of the role of X-linked genes and genetic pathways will provide the strategies to develop the molecular therapeutics to treat and prevent reproductive related cancers.
language:
- en
metrics:
- accuracy
- f1
- precision
- recall
tags:
- medical
---

# Textual Entailment for biomedical texts

## Model Description

This model is fine-tuned on SciFact dataset for textual antailment task in the domain of biomedicine.