Results 41 to 50 of about 979,707 (169)
Peripheral blood proteome biomarkers distinguish immunosuppressive features of cancer progression
Immune status significantly influences cancer progression. This study used plasma proteomics to analyze benign 67NR and malignant 4T1 breast tumor models at early and late tumor stages. Immune‐related proteins–osteopontin (Spp1), lactotransferrin (Ltf), calreticulin (Calr) and peroxiredoxin 2 (Prdx2)–were associated with systemic myeloid‐derived ...
Yeon Ji Park+6 more
wiley +1 more source
Decision Stream: Cultivating Deep Decision Trees
Various modifications of decision trees have been extensively used during the past years due to their high efficiency and interpretability. Tree node splitting based on relevant feature selection is a key step of decision tree learning, at the same time ...
Ignatov, Andrey, Ignatov, Dmitry
core +1 more source
Monotone Classification with Decision Trees [PDF]
In machine learning, monotone classification is concerned with a classification function to learn in order to guarantee a kind of monotonicity of the class with respect to attribute values. In this paper, we focus on rank discrimination measures to be used in decision tree induction, i.e., functions able to measure the discrimination power of an ...
Christophe, Marsala, PETTURITI, DAVIDE
openaire +5 more sources
Stochastic variation in the FOXM1 transcription program mediates replication stress tolerance
Cellular heterogeneity is a major cause of drug resistance in cancer. Segeren et al. used single‐cell transcriptomics to investigate gene expression events that correlate with sensitivity to the DNA‐damaging drugs gemcitabine and prexasertib. They show that dampened expression of transcription factor FOXM1 and its target genes protected cells against ...
Hendrika A. Segeren+4 more
wiley +1 more source
Adverse prognosis gene expression patterns in metastatic castration‐resistant prostate cancer
We aggregated a cohort of 1012 mCRPC tissue samples from 769 patients and investigated the association of gene expression‐based pathways with clinical outcomes. Loss of AR signaling, high proliferation, and a glycolytic phenotype were independently prognostic for poor outcomes, and an adverse transcriptional feature score incorporating these pathways ...
Marina N. Sharifi+26 more
wiley +1 more source
Decision Trees for Uncertain Data [PDF]
Traditional decision tree classifiers work with data whose values are known and precise. We extend such classifiers to handle data with uncertain information. Value uncertainty arises in many applications during the data collection process. Example sources of uncertainty include measurement/quantization errors, data staleness, and multiple repeated ...
Tsang, S+4 more
openaire +5 more sources
Presurgery 72‐h fasting in GB patients leads to adaptations of plasma lipids and polar metabolites. Fasting reduces lysophosphatidylcholines and increases free fatty acids, shifts triglycerides toward long‐chain TGs and increases branched‐chain amino acids, alpha aminobutyric acid, and uric acid.
Iris Divé+7 more
wiley +1 more source
ICP34.5 is one of the most important antihost response proteins. The saRNA‐encoding HSV‐1 neurovirulence protein ICP34.5 clearly mediated the eukaryotic initiation factor 2 alpha subunit (eIF2α) dephosphorylation and significant suppression of innate immune responses in vitro, leading to enhanced expression of the saRNA‐encoded gene.
Xuemin Lu+6 more
wiley +1 more source
Decision tree algorithms have been among the most popular algorithms for interpretable (transparent) machine learning since the early 1980's. The problem that has plagued decision tree algorithms since their inception is their lack of optimality, or lack
Hu, Xiyang+2 more
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