Results 31 to 40 of about 161,172 (233)

On Data-Enriched Logistic Regression

Mathematics
Biomedical 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, Sayan Dasgupta, Yuxiang Xie, Asad Haris, Ying-Qing Chen   +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
doaj   +1 more source

Cellular liquid biopsy provides unique chances for disease monitoring, preclinical model generation and therapy adjustment in rare salivary gland cancer patients

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, Florian Lüke, Steffi Treitschke, Andrea Coluccio, Martin Hoffmann, Giancarlo Feliciello, Adithi Ravikumar Varadarajan, Xin Lu, Kathrin Weidele, Catherine Botteron, Silvia Materna–Reichelt, Felix Keil, Katja Evert, Florian Weber, Thomas Schamberger, Michael Althammer, Jirka Grosse, Dirk Hellwig, Christian Schulz, Stephan Seitz, Peter Ugocsai, Anke Schlenska‐Lange, Roman Mayr, Ulrich Kaiser, Wolfgang Dietmaier, Bernhard Polzer, Jens Warfsmann, Kamran Honarnejad, Tobias Pukrop, Daniel Heudobler, Christoph A. Klein, Christian Werno   +31 more
wiley   +1 more source

Supporting Regularized Logistic Regression Privately and Efficiently. [PDF]

PLoS ONE, 2016
As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on.
Wenfa Li, Hongzhe Liu, Peng Yang, Wei Xie   +3 more
doaj   +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
doaj   +1 more source

Targeted metabolomics reveals novel diagnostic biomarkers for colorectal cancer

Molecular Oncology, EarlyView.
This study employed targeted metabolomic profiling to identify 302 distinct metabolites present in platelet‐rich plasma (PRP), revealing aberrant metabolic profiles amongst individuals diagnosed with colorectal cancer (CRC). Compared to carcinoembryonic antigen (CEA) and cancer antigen 19‐9 (CA199), our metabolite panel showed improved sensitivity ...
Zuojian Hu, Fenglin Shen, Yang Liu, Ziqing Zhong, Yongling Chen, Zhiyuan Xia, Cuiju Mo, Hongxiu Yu   +7 more
wiley   +1 more source

Distributed Parallel Sparse Multinomial Logistic Regression

IEEE Access, 2019
Sparse Multinomial Logistic Regression (SMLR) is widely used in the field of image classification, multi-class object recognition, and so on, because it has the function of embedding feature selection during classification.
Dajiang Lei, Meng Du, Hao Chen, Zhixing Li, Yu Wu   +4 more
doaj   +1 more source

Clinical significance of stratifying prostate cancer patients through specific circulating genes

Molecular Oncology, EarlyView.
We tested a specific panel of genes representative of luminal, neuroendocrine and stem‐like cells in the blood of prostate cancer patients, showing predictive value from diagnosis to late stages of disease. This approach allows monitoring of treatment responses and outcomes at specific time points in trajectories.
Seta Derderian, Edouard Jarry, Arynne Santos, Quentin Vesval, Lucie Hamel, Rafael Sanchez‐Salas, Alexis Rompré‐Brodeur, Wassim Kassouf, Raghu Rajan, Fadi Brimo, Marie Duclos, Armen Aprikian, Simone Chevalier   +12 more
wiley   +1 more source

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

Response to neoadjuvant chemotherapy in early breast cancers is associated with epithelial–mesenchymal transition and tumor‐infiltrating lymphocytes

Molecular Oncology, EarlyView.
Epithelial–mesenchymal transition (EMT) and tumor‐infiltrating lymphocytes (TILs) are associated with early breast cancer response to neoadjuvant chemotherapy (NAC). This study evaluated EMT and TIL shifts, with immunofluorescence and RNA sequencing, at diagnosis and in residual tumors as potential biomarkers associated with treatment response.
Françoise Derouane, Jérôme Ambroise, Cédric van Marcke, Mieke Van Bockstal, Martine Berlière, Christine Galant, Hélène Dano, Médina Lougué, Elena Benidovskaya, Guy Jerusalem, Vincent Bours, Claire Josse, Jérôme Thiry, Aurélie Daumerie, Caroline Bouzin, Cyril Corbet, François P. Duhoux   +16 more
wiley   +1 more source