Results 51 to 60 of about 222,810 (285)
Diversity and complexity in neural organoids
FEBS Letters, EarlyView.Neural organoid research aims to expand genetic diversity on one side and increase tissue complexity on the other. Chimeroids integrate multiple donor genomes within single organoids. Self‐organising multi‐identity organoids, exogenous cell seeding, or enforced assembly of region‐specific organoids contribute to tissue complexity.
Ilaria Chiaradia, Madeline A. Lancaster
wiley +1 more source
Imbalanced data classification using MapReduce and relief
Journal of Information and Telecommunication, 2018 Classification of imbalanced data has been reported to require modification of standard classification algorithms and lately has attracted a lot of attention due to practical applications in industry, banking and finance.
Joanna Jedrzejowicz +3 more
doaj +1 more source
MEBoost: Mixing Estimators with Boosting for Imbalanced Data Classification
, 2017 Class imbalance problem has been a challenging research problem in the fields of machine learning and data mining as most real life datasets are imbalanced.
Ahmed, Sajid +6 more
core +1 more source
Hyperosmotic stress induces PARP1‐mediated HPF1‐dependent mono(ADP‐ribosyl)ation
FEBS Letters, EarlyView.Sorbitol‐induced hyperosmotic stress rapidly induces reversible mono(ADP‐ribosyl)ation (MARylation) on PARP1 without the signs of genotoxic signaling. We show that PARP1 autoMARylation is HPF1 dependent and forms hydroxylamine‐resistant O‐glycosidic linkages.
Anna Georgina Kopasz +11 more
wiley +1 more source
Resampling imbalanced data for network intrusion detection datasets
Journal of Big Data, 2021 Machine learning plays an increasingly significant role in the building of Network Intrusion Detection Systems. However, machine learning models trained with imbalanced cybersecurity data cannot recognize minority data, hence attacks, effectively.
Sikha Bagui, Kunqi Li
doaj +1 more source
Efficient posterior sampling for high-dimensional imbalanced logistic regression
, 2019 High-dimensional data are routinely collected in many areas. We are particularly interested in Bayesian classification models in which one or more variables are imbalanced.
Dunson, David +3 more
core +1 more source
Tumour–host interactions in Drosophila: mechanisms in the tumour micro‐ and macroenvironment
Molecular Oncology, EarlyView.This review examines how tumour–host crosstalk takes place at multiple levels of biological organisation, from local cell competition and immune crosstalk to organism‐wide metabolic and physiological collapse. Here, we integrate findings from Drosophila melanogaster studies that reveal conserved mechanisms through which tumours hijack host systems to ...
José Teles‐Reis, Tor Erik Rusten
wiley +1 more source
Toward a Balanced Feature Space for the Deep Imbalanced Regression
IEEE Access, 2023 Regression with imbalanced data has been regarded as a more realistic scenario due to the difficulty of data acquisition and label annotations. However, it has not been extensively studied compared to the imbalanced classification.
Jangho Lee
doaj +1 more source
Feature selected cost-sensitive twin SVM for imbalanced data [PDF]
MATEC Web of Conferences, 2020 In this paper, we propose a cost-sensitive twin SVM (cs-tsvm) and apply it to imbalanced data. A weight is added to each instance according to its cost of misclassification which is related to its position. In preprocessing part, features are selected by
Li Xiaopeng, Zhang Xianrong
doaj +1 more source
MCMC for Imbalanced Categorical Data
, 2017 Many modern applications collect highly imbalanced categorical data, with some categories relatively rare. Bayesian hierarchical models combat data sparsity by borrowing information, while also quantifying uncertainty.
Dunson, David B. +3 more
core +2 more sources