Results 81 to 90 of about 732,251 (333)

From lactation to malignancy: A comparison between healthy and cancerous breast gland at single‐cell resolution reveals new issues for tumorigenesis

FEBS Letters, EarlyView.
Single‐cell RNA sequencing reveals an opposite role of SLPI in basal tumors based on metastatic spread, along with shared activation of specific regulons in cancer cells and mature luminal lactocytes, as well as downregulation of MALAT1 and NEAT1 in the latter.
Pietro Ancona, Carlo M. Bergamini, Carlo Ferrari, Stefano Volinia, Nicoletta Bianchi   +4 more
wiley   +1 more source

Highly multiplexed digital PCR assay for simultaneous quantification of variant allele frequencies and copy number alterations of KRAS and GNAS in pancreatic cancer precursors

Molecular Oncology, EarlyView.
Combining melting curve analysis enhances the multiplexing capability of digital PCR. Here, we developed a 14‐plex assay to simultaneously measure single nucleotide mutations and amplifications of KRAS and GNAS, which are common driver genes in pancreatic cancer precursors. This assay accurately quantified variant allele frequencies in clinical samples
Junko Tanaka, Tatsuo Nakagawa, Yusuke Ono, Yoshio Kamura, Takeshi Ishida, Hidemasa Kawabata, Kenji Takahashi, Hiroki Sato, Andrew S. Liss, Yusuke Mizukami, Takahide Yokoi   +10 more
wiley   +1 more source

Comparing self‐reported race and genetic ancestry for identifying potential differentially methylated sites in endometrial cancer: insights from African ancestry proportions using machine learning models

Molecular Oncology, EarlyView.
Integrating ancestry, differential methylation analysis, and machine learning, we identified robust epigenetic signature genes (ESGs) and Core‐ESGs in Black and White women with endometrial cancer. Core‐ESGs (namely APOBEC1 and PLEKHG5) methylation levels were significantly associated with survival, with tumors from high African ancestry (THA) showing ...
Huma Asif, J. Julie Kim
wiley   +1 more source

A large‐scale retrospective study in metastatic breast cancer patients using circulating tumour DNA and machine learning to predict treatment outcome and progression‐free survival

Molecular Oncology, EarlyView.
There is an unmet need in metastatic breast cancer patients to monitor therapy response in real time. In this study, we show how a noninvasive and affordable strategy based on sequencing of plasma samples with longitudinal tracking of tumour fraction paired with a statistical model provides valuable information on treatment response in advance of the ...
Emma J. Beddowes, Mario Ortega Duran, Solon Karapanagiotis, Alistair Martin, Meiling Gao, Riccardo Masina, Ramona Woitek, James Tanner, Fleur Tippin, Justine Kane, Jonathan Lay, Anja Brouwer, Stephen‐John Sammut, Suet‐Feung Chin, Davina Gale, Dana W. Y. Tsui, Sarah‐Jane Dawson, Nitzan Rosenfeld, Maurizio Callari, Oscar M. Rueda, Carlos Caldas   +20 more
wiley   +1 more source

Multi‐omic characterization of consensus molecular subtype 1 (CMS1) colorectal cancer with dampened immune response improves precision medicine

Molecular Oncology, EarlyView.
This study highlights the importance of multi‐omic analyses in characterizing colorectal cancers. Indeed, our analysis revealed a rare CMS1 exhibiting dampened immune activation, including reduced PD‐1 expression, moderate CD8+ T‐cell infiltration, and suppressed JAK/STAT pathway.
Livia Concetti, Manuel Scimeca, Julia Bischof, Jonathan Woodsmith, Massimiliano Agostini, Cristina Fiorani, Yufang Shi, Eleonora Candi, Gerry Melino, Alessandro Mauriello, Giuseppe S. Sica   +10 more
wiley   +1 more source

Unsupervised Selection of Color Factor Weight and Segmentation Scale Parameter for Successful Segmentation of Urban Land Use/Land Cover [PDF]

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Image segmentation is a crucial step in object-based image analysis of urban remote sensing data. Its primary goal is to divide a digital image into meaningful objects that are internally homogeneous and clearly distinguishable from neighboring segments.
G. B. Ikokou, K. M. Malale
doaj   +1 more source

Segmenting Dermoscopic Images

, 2017
We propose an automatic algorithm, named SDI, for the segmentation of skin lesions in dermoscopic images, articulated into three main steps: selection of the image ROI, selection of the segmentation band, and segmentation. We present extensive experimental results achieved by the SDI algorithm on the lesion segmentation dataset made available for the ...
M. R. Guarracino, L. Maddalena
openaire   +3 more sources

Detecting homologous recombination deficiency for breast cancer through integrative analysis of genomic data

Molecular Oncology, EarlyView.
This study develops a semi‐supervised classifier integrating multi‐genomic data (1404 training/5893 validation samples) to improve homologous recombination deficiency (HRD) detection in breast cancer. Our method demonstrates prognostic value and predicts chemotherapy/PARP inhibitor sensitivity in HRD+ tumours.
Rong Zhu, Katherine Eason, Suet‐Feung Chin, Paul A. W. Edwards, Raquel Manzano Garcia, Richard Moulange, Jia Wern Pan, Soo Hwang Teo, Sach Mukherjee, Maurizio Callari, Carlos Caldas, Stephen‐John Sammut, Oscar M. Rueda   +12 more
wiley   +1 more source

Data‐driven discovery of gene expression markers distinguishing pediatric acute lymphoblastic leukemia subtypes

Molecular Oncology, EarlyView.
This study investigates gene expression differences between two major pediatric acute lymphoblastic leukemia (ALL) subtypes, B‐cell precursor ALL, and T‐cell ALL, using a data‐driven approach consisting of biostatistics and machine learning methods. Following analysis of a discovery dataset, we find a set of 14 expression markers differentiating the ...
Mona Nourbakhsh, Nikola Tom, Anna Schrøder Lassen, Helene Brasch Lind Petersen, Ulrik Kristoffer Stoltze, Karin Wadt, Kjeld Schmiegelow, Matteo Tiberti, Elena Papaleo   +8 more
wiley   +1 more source

Semisupervised Soft Mumford-Shah Model for MRI Brain Image Segmentation

Applied Computational Intelligence and Soft Computing, 2016
One challenge of unsupervised MRI brain image segmentation is the central gray matter due to the faint contrast with respect to the surrounding white matter.
Hong-Yuan Wang, Fuhua Chen
doaj   +1 more source