AIMC Topic: Carcinogenesis
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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
Jun 3, 2022
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...
Current cancer driver variant predictors learn to recognize driver genes instead of functional variants.
BMC biology
Jan 13, 2021
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...
A Deep Learning Framework Identifies Pathogenic Noncoding Somatic Mutations from Personal Prostate Cancer Genomes.
Cancer research
Sep 9, 2020
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...
HCV nonstructural protein 4 is associated with aggressiveness features of breast cancer.
Breast cancer (Tokyo, Japan)
Dec 28, 2017
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...
Hepatitis C
Viral Nonstructural Proteins
Mucin-1
Neoplasm Grading
Fibronectins
Hepacivirus
Disease Progression
Hepatitis C Antibodies
Incidence
Adult
Gene Expression Regulation, Neoplastic
Breast Neoplasms
Enzyme-Linked Immunosorbent Assay
Middle Aged
Egypt
Carcinogenesis
Humans
Neoplasm Staging
Female
Aged
Mutagenic Potential ofBos taurus Papillomavirus Type 1 E6 Recombinant Protein: First Description.
BioMed research international
Dec 9, 2015
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...
Endometrial tumorigenesis involves epigenetic plasticity demarcating non-coding somatic mutations and 3D-genome alterations.
Genome biology
May 9, 2025
BACKGROUND: The incidence and mortality of endometrial cancer (EC) is on the rise. Eighty-five percent of ECs depend on estrogen receptor alpha (ERα) for proliferation, but little is known about its transcriptional regulation in these tumors.
DeepAlloDriver: a deep learning-based strategy to predict cancer driver mutations.
Nucleic acids research
Jul 5, 2023
Driver mutations can contribute to the initial processes of cancer, and their identification is crucial for understanding tumorigenesis as well as for molecular drug discovery and development. Allostery regulates protein function away from the functi...
Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.
Cell
Apr 5, 2018
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...
Machine Learning-Based Modeling of Drug Toxicity.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2018
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...
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