Results 31 to 40 of about 2,017,337 (259)

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

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

Model Logistic Regression dalam Penentuan Kebijakan Dividen Perusahaan di Indonesia [PDF]

, 2012
This paper investagates dividend policy decision in Indonesian Stock Exchange (IDX) through studying non-financial firms. Panel data were obtained from 1490 non-financial firms over the five year period from 2006 to 2010, where 310 firms pay dividend and
Satmoko, A. (Agung), Sudarman, S. (Sudarman)   +1 more
core  

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

Modeling Tenant’s Credit Scoring Using Logistic Regression

SAGE Open, 2023
This study implements the multivariable logistic regression to develop a credit scoring model based on tenants’ characteristics. The credit history of tenant is not considered.
Kim Sia Ling, Siti Suhana Jamaian, Syahira Mansur, Alwyn Kwan Hoong Liew   +3 more
doaj   +1 more source

Group Logistic Regression Models with l_p,q Regularization

Mathematics, 2022
In this paper, we proposed a logistic regression model with lp,q regularization that could give a group sparse solution. The model could be applied to variable-selection problems with sparse group structures. In the context of big data, the solutions for
Yanfang Zhang, Chuanhua Wei, Xiaolin Liu
doaj   +1 more source

Tumor clusters with divergent inflammation and human retroelement expression determine the clinical outcome of patients with serous ovarian cancer

Molecular Oncology, EarlyView.
Analysis of treatment‐naïve high‐grade serous ovarian carcinoma (HGSOC) and control tissues for ERVs, LINE‐1 (L1), inflammation, and immune checkpoints identified five clusters with diverse patient recurrence‐free survivals. An inflammation score was calculated and correlated with retroelement expression, where one novel cluster (Triple‐I) with high ...
Laura Glossner, Markus Eckstein, Christoph Mark, Matthias W. Beckmann, Arndt Hartmann, Pamela L. Strissel, Reiner Strick   +6 more
wiley   +1 more source

Expectation-maximization for logistic regression [PDF]

, 2013
We present a family of expectation-maximization (EM) algorithms for binary and negative-binomial logistic regression, drawing a sharp connection with the variational-Bayes algorithm of Jaakkola and Jordan (2000).
Scott, James G., Sun, Liang
core  

Association of high‐dose radioactive iodine therapy with PPM1D‐mutated clonal hematopoiesis in older individuals

Molecular Oncology, EarlyView.
In thyroid cancer patients, high‐dose (≥7.4 GBq) radioactive iodine therapy (RAIT) was associated with a higher prevalence of clonal hematopoiesis (variant allele frequency >2%) in individuals aged ≥50 years (OR = 2.44). In silico analyses showed that truncating PPM1D mutations conferred a selective advantage under these conditions.
Jaeryuk Kim, Sungwoo Bae, Jaeyong Choi, Sun‐Wha Im, Bukyoung Cha, Gyeongseo Jung, Sun Wook Cho, Eul‐Ju Seo, Young Ah Lee, Jin Chul Paeng, Young Joo Park, Jong‐Il Kim   +11 more
wiley   +1 more source