Results 21 to 30 of about 1,371,617 (304)

Algorithmic statistics, prediction and machine learning

CoRR, 2015
Algorithmic statistics considers the following problem: given a binary string $x$ (e.g., some experimental data), find a "good" explanation of this data. It uses algorithmic information theory to define formally what is a good explanation. In this paper we extend this framework in two directions.
openaire   +4 more sources

Machine Learning as Statistical Data Assimilation

CoRR, 2017
We identify a strong equivalence between neural network based machine learning (ML) methods and the formulation of statistical data assimilation (DA), known to be a problem in statistical physics. DA, as used widely in physical and biological sciences, systematically transfers information in observations to a model of the processes producing the ...
Henry D. I. Abarbanel, Paul J. Rozdeba, Sasha Shirman   +2 more
openaire   +2 more sources

Developmental programmes drive cellular plasticity, disease progression and therapy resistance in lung adenocarcinoma

Molecular Oncology, EarlyView.
This study shows that lung adenocarcinomas exploit developmental branching morphogenesis to acquire a therapy resistant basal‐like tumour cell state. This process was found to be regulated by combined TP53 loss‐of‐function and type‐I interferon signalling, identifying a novel axis for biomarker and therapeutic target discovery.
Kamila J Bienkowska, Stephany Gallardo, Nur S. Zainal, Leena Arora, Matthew Ellis, Maria‐Antoinette Lopez, Judith Austine, Sai Pittla, Serena J Chee, Aiman Alzetani, Emily C Shaw, Christian H Ottensmeier, Gareth J Thomas, Christopher J Hanley   +13 more
wiley   +1 more source

Generalization Bounds in the Presence of Outliers: a Median-of-Means Study [PDF]

, 2021
In contrast to the empirical mean, the Median-of-Means (MoM) is an estimator of the mean theta of a square integrable r.v. Z, around which accurate nonasymptotic confidence bounds can be built, even when Z does not exhibit a sub-Gaussian tail behavior ...
Laforgue, P, Staerman, G, Clemencon, S
core  

Patient therapy outcome modeling in cancer organoids is improved by cancer‐associated fibroblasts and organoid assembly convolution

Molecular Oncology, EarlyView.
Patient‐derived organoids (PDOs) from pancreatic, colorectal, and gastric cancers were used to evaluate standard and experimental therapies. Incorporating cancer‐associated fibroblasts (CAFs) into organoid cultures improved patient therapy outcome prediction.
Marcin Grochowski, Liudmyla Dolinchuk, Michał Jerzak, Albert Gandurski, Tomasz Grochowski, Weronika Wojtyś, Maciej Zadrożny, Wojciech Kaźmierczak, Małgorzata Lenarcik, Marta Matejak‐Górska, Radosław Samsel, Tomasz Olesiński, Dawid Walerych   +12 more
wiley   +1 more source

DPVIm: Differentially Private Variational Inference Improved

, 2023
Differentially private (DP) release of multidimensional statistics typically considers an aggregate sensitivity, e.g. the vector norm of a high-dimensional vector.
Kaski, Samuel, Prediger, Lukas, Honkela, Antti, Jälkö, Joonas   +3 more
core  

Geomstats: A Python Package for Riemannian Geometry in Machine Learning

, 2020
We introduce Geomstats, an open-source Python toolbox for computations and statistics on nonlinear manifolds, such as hyperbolic spaces, spaces of symmetric positive definite matrices, Lie groups of transformations, and many more.
GUIGUI, Nicolas, MATHE, Johan, SHEWMAKE, Christian, Hajri, H, LE BRIGANT, Alice, Kainz, B, Cabanes, Y, Pennec, X, Peltre, O, Hou, B, Holmes, S, Donnat, C, CHAUCHAT, Paul, Zaatiti, H, Brooks, D, MIOLANE, Nina, GERALD, Thomas, PELTRE, Olivier, Brooks, Daniel, Shewmake, C, HEYDER, Stefan, HOLMES, Susan, DONNAT, Claire, PENNEC, Xavier, Gerald, T, CABANES, Yann, Mathe, J, KOEP, Niklas, ZAATITI, Hadi, Koep, N, Guigui, N, KAINZ, Bernhard, Thanwerdas, Y, HAJRI, Hatem, Miolane, N, HOU, Benjamin, THANWERDAS, Yann, Le Brigant, A, Heyder, S, Chauchat, P   +39 more
core   +1 more source

Statistical Robustness of Empirical Risks in Machine Learning

J. Mach. Learn. Res., 2020
This paper studies convergence of empirical risks in reproducing kernel Hilbert spaces (RKHS). A conventional assumption in the existing research is that empirical training data do not contain any noise but this may not be satisfied in some practical circumstances.
Shaoyan Guo, Huifu Xu, Liwei Zhang
openaire   +4 more sources

Large‐scale bidirectional arrayed genetic screens identify OXR1 and EMC4 as modifiers of αSynuclein aggregation

FEBS Open Bio, EarlyView.
Activation of the mitochondrial protein OXR1 increases pSyn129 αSynuclein aggregation by lowering ATP levels and altering mitochondrial membrane potential, particularly in response to MSA‐derived fibrils. In contrast, ablation of the ER protein EMC4 enhances autophagic flux and lysosomal clearance, broadly reducing α‐synuclein aggregates.
Sandesh Neupane, Lea Nikolić, Lorenzo Maraio, Thomas Goiran, Nathan Karpilovsky, Stefano Sellitto, Vangelis Bouris, Jiang‐An Yin, Ronald Melki, Edward A Fon, Adriano Aguzzi, Elena De Cecco   +11 more
wiley   +1 more source

Discovering biomarkers associated and predicting cardiovascular disease with high accuracy using a novel nexus of machine learning techniques for precision medicine

bioRxiv, 2023
Personalized interventions are deemed vital given the intricate characteristics, advancement, inherent genetic composition, and diversity of cardiovascular diseases (CVDs). The appropriate utilization of artificial intelligence (AI) and machine learning (
William DeGroat, Habiba Abdelhalim, Kush Patel, Dinesh Mendhe, Saman Zeeshan, Zeeshan Ahmed   +5 more
semanticscholar   +1 more source