Results 101 to 110 of about 1,716,816 (278)
Molecular Oncology, EarlyView.Methods to improve antibody–drug conjugate (ADC) treatment durability in cancer therapy are needed. We utilized ADCs and immune‐stimulating antibody conjugates (ISACs), which are made from two non‐competitive antibodies, to enhance the entry of toxic payloads into cancer cells and deliver immunostimulatory agents into immune cells.
Tiexin Wang +3 more
wiley +1 more source
Tumor mutational burden as a determinant of metastatic dissemination patterns
Molecular Oncology, EarlyView.This study performed a comprehensive analysis of genomic data to elucidate whether metastasis in certain organs share genetic characteristics regardless of cancer type. No robust mutational patterns were identified across different metastatic locations and cancer types.
Eduardo Candeal +4 more
wiley +1 more source
Frontiers in Applied Mathematics and StatisticsIntroductionDropout is a major source of missing data in repeated measures studies and can bias statistical inference if not handled properly. This study compares the performance of two common methods for addressing dropout under the missing at random ...
Mohyaldein Salih +2 more
doaj +1 more source
Molecular Oncology, EarlyView.Meta‐transcriptome analysis identified FGF19 as a peptide enteroendocrine hormone associated with colorectal cancer prognosis. In vivo xenograft models showed release of FGF19 into the blood at levels that correlated with tumor volumes. Tumoral‐FGF19 altered murine liver metabolism through FGFR4, thereby reducing bile acid synthesis and increasing ...
Jordan M. Beardsley +5 more
wiley +1 more source
Molecular Oncology, EarlyView.This work identified serum proteins associated with pancreatic epithelial neoplasms (PanINs) and early‐stage PDAC. Proteomics screens assessed genetically engineered mice with abundant PanINs, KPC mice (Lox‐STOP‐Lox‐KrasG12D/+ Lox‐STOP‐Lox‐Trp53R172H/+ Pdx1‐Cre) before PDAC development and also early‐stage PDAC patients (n = 31), compared to benign ...
Hannah Mearns +10 more
wiley +1 more source
Random Indicator Imputation for Missing Not At Random Data
Imputation methods for dealing with incomplete data typically assume that the missingness mechanism is at random (MAR). These methods can also be applied to missing not at random (MNAR) situations, where the user specifies some adjustment parameters that describe the degree of departure from MAR.
Jolani, Shahab, van Buuren, Stef
openaire +3 more sources
Subtype‐specific enhancer RNAs define transcriptional regulators and prognosis in breast cancers
Molecular Oncology, EarlyView.This study employed machine learning methodologies to perform the subtype‐specific classification of RNA‐seq data sets, which are mapped on enhancers from TCGA‐derived breast cancer patients. Their integration with gene expression (referred to as ProxCReAM eRNAs) and chromatin accessibility profiles has the potential to identify lineage‐specific and ...
Aamena Y. Patel +6 more
wiley +1 more source
Improving Missing Data Imputation with Deep Generative Models
, 2019 Datasets with missing values are very common on industry applications, and they can have a negative impact on machine learning models. Recent studies introduced solutions to the problem of imputing missing values based on deep generative models. Previous
Camino, Ramiro D. +2 more
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Molecular Oncology, EarlyView.Aldehyde dehydrogenase 1A1 (ALDH1A1) is a cancer stem cell marker in several malignancies. We established a novel epithelial cell line from rectal adenocarcinoma with unique overexpression of this enzyme. Genetic attenuation of ALDH1A1 led to increased invasive capacity and metastatic potential, the inhibition of proliferation activity, and ultimately ...
Martina Poturnajova +25 more
wiley +1 more source
Multiple imputation of missing data under missing at random: including a collider as an auxiliary variable in the imputation model can induce bias. [PDF]
Front Epidemiol, 2023 Curnow E +4 more
europepmc +1 more source