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Carcinogenesis

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HCV nonstructural protein 4 is associated with aggressiveness features of breast cancer.

Breast cancer (Tokyo, Japan)
BACKGROUND: Hepatitis C virus (HCV) has the lymphotropic feature that is supposed to be the reason of related extrahepatic manifestation. HCV viral oncoproteins may participate in the regulation of some gene expression that has been implicated in tum...

Mutagenic Potential ofBos taurus Papillomavirus Type 1 E6 Recombinant Protein: First Description.

BioMed research international
Bovine papillomavirus (BPV) is considered a useful model to study HPV oncogenic process. BPV interacts with the host chromatin, resulting in DNA damage, which is attributed to E5, E6, and E7 viral oncoproteins activity. However, the oncogenic mechani...

Machine Learning-Based Modeling of Drug Toxicity.

Methods in molecular biology (Clifton, N.J.)
Toxicity is an important reason for the failure of drug research and development (R&D). The traditional experimental testings for chemical toxicity profile are costly and time-consuming. Therefore, it is attractive to develop the effective and accura...

Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.

Cell
Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovati...

A Deep Learning Framework Identifies Pathogenic Noncoding Somatic Mutations from Personal Prostate Cancer Genomes.

Cancer research
Our understanding of noncoding mutations in cancer genomes has been derived primarily from mutational recurrence analysis by aggregating clinical samples on a large scale. These cohort-based approaches cannot directly identify individual pathogenic n...

Current cancer driver variant predictors learn to recognize driver genes instead of functional variants.

BMC biology
BACKGROUND: Identifying variants that drive tumor progression (driver variants) and distinguishing these from variants that are a byproduct of the uncontrolled cell growth in cancer (passenger variants) is a crucial step for understanding tumorigenes...

DiaDeL: An Accurate Deep Learning-Based Model With Mutational Signatures for Predicting Metastasis Stage and Cancer Types.

IEEE/ACM transactions on computational biology and bioinformatics
Mutational signatures help identify cancer-associated genes that are being involved in tumorigenesis pathways. Hence, these pathways guide precision medicine approaches to find appropriate drugs and treatments. The pattern of mutations varies in diff...