Results 51 to 60 of about 4,665,872 (364)
BLESS: bagged logistic regression for biomarker identification. [PDF]
Front GenetThe traditional single nucleotide polymorphism (SNP)-wise approach in genome-wide association studies is focused on examining the marginal association between each SNP with the outcome separately and applying multiple testing adjustments to the resulting
Gardiner K, Zhang X, Xing L.
europepmc +2 more sources
Semi-Parallel logistic regression for GWAS on encrypted data
BMC Medical Genomics, 2020 The sharing of biomedical data is crucial to enable scientific discoveries across institutions and improve health care. For example, genome-wide association studies (GWAS) based on a large number of samples can identify disease-causing genetic variants ...
Miran Kim +3 more
semanticscholar +1 more source
On Data-Enriched Logistic Regression
MathematicsBiomedical researchers typically investigate the effects of specific exposures on disease risks within a well-defined population. The gold standard for such studies is to design a trial with an appropriately sampled cohort.
Cheng Zheng +4 more
doaj +1 more source
Enabling Equal Opportunity in Logistic Regression Algorithm
Management, 2023 Research Question: This paper aims at adjusting the logistic regression algorithm to mitigate unwanted discrimination shown towards race, gender, etc. Motivation: Decades of research in the field of algorithm design have been dedicated to making a better
Sandro Radovanović, Marko Ivić
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Molecular Oncology, Volume 19, Issue 5, Page 1422-1435, May 2025.The LEXOVE prospective study evaluated plasma cell‐free extracellular vesicle (cfEV) dynamics using Bradford assay and dynamic light scattering in metastatic non‐small cell lung cancer patients undergoing first‐line treatments, correlating a ∆cfEV < 20% with improved median progression‐free survival in responders versus non‐responders.
Valerio Gristina +17 more
wiley +1 more source
Structured Learning via Logistic Regression [PDF]
, 2014 A successful approach to structured learning is to write the learning objective as a joint function of linear parameters and inference messages, and iterate between updates to each.
Domke, Justin
core
Measuring overlap in logistic regression [PDF]
Research Papers in Economics, 1999 In this paper we show that the recent notion of regression depth can be used as a data-analytic tool to measure the amount of separation between successes and failures in the binary response framework. Extending this algorithm allows us to compute the overlap in data sets which are commonly fitted by logistic regression models.
Christmann, Andreas, Rousseeuw, Peter J.
openaire +5 more sources
Read this paper if you want to learn logistic regression
Revista de Sociologia e Política, 2020 Introduction: What if my response variable is binary categorical? This paper provides an intuitive introduction to logistic regression, the most appropriate statistical technique to deal with dichotomous dependent variables.
A. Fernandes +3 more
semanticscholar +1 more source
Hidden Markov Model Based on Logistic Regression
Mathematics, 2023 A hidden Markov model (HMM) is a useful tool for modeling dependent heterogeneous phenomena. It can be used to find factors that affect real-world events, even when those factors cannot be directly observed.
Byeongheon Lee, Joowon Park, Yongku Kim
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Molecular Oncology, EarlyView.We quantified and cultured circulating tumor cells (CTCs) of 62 patients with various cancer types and generated CTC‐derived tumoroid models from two salivary gland cancer patients. Cellular liquid biopsy‐derived information enabled molecular genetic assessment of systemic disease heterogeneity and functional testing for therapy selection in both ...
Nataša Stojanović Gužvić +31 more
wiley +1 more source