Results 91 to 100 of about 105,441 (263)

Combining Spatial Multi‐Omics Data to Decipher Spatial Domains and Elucidate Cell Heterogeneity Based on Self‐Supervised Graph Learning

open access: yesAdvanced Science, EarlyView.
A self‐supervised multi‐view graph fusion framework integrates spatial multi‐omics, excelling in domain identification and denoising. It reconstructs spatial pseudo‐expression, jointly analyzes multi‐omics data, infers RNA velocity, predicts spatial omics features from single‐cell multi‐omics, and detects spatially dark genes and transcription factors,
Yuejing Lu   +8 more
wiley   +1 more source

Inferential statistics and the graphics calculator

open access: yes, 1998
Recent teaching and learning of elementary statistics have been influenced by the use of statistical packages on microcomputers, which have permitted data storage and flexible data analysis.
Kissane, B., Barry Kissane
core   +1 more source

Decoding Spatial Heterogeneity and Multi‐Omics Regulation with Hierarchical Graph Learning

open access: yesAdvanced Science, EarlyView.
ABSTRACT Recent advances in spatial multi‐omics technologies have enabled the simultaneous profiling of multiple molecular layers within the same tissue slice, providing unprecedented opportunities to investigate tissue spatial organization. However, most existing computational methods identify spatial domains in a purely data‐driven manner, rarely ...
Jiazhou Chen   +6 more
wiley   +1 more source

Risk in science and society: towards new pedagogies of probability bayes rules

open access: yes, 2011
The core ideas of probability are taught in schools and universities in a rather abstract sense, particularly since probability is usually taught as a branch of mathematics and rarely linked to realistic contexts.
Kapadia, Ramesh
core  

STAID: A Self‐Refining Deep Learning Framework for Spatial Cell‐Type Deconvolution with Biologically Informed Modeling

open access: yesAdvanced Science, EarlyView.
STAID is a unified deep learning framework that couples iterative pseudo‐spot refinement with neural network training through a feedback loop and exploits gene co‐expression information to model higher‐order interactions, achieving accurate and robust cell‐type deconvolution in spatial transcriptomics.
Jixin Liu   +5 more
wiley   +1 more source

A Data‐Driven Inverse Design Methodology for Magnetic Soft Millirobots Navigating in Confined Spaces

open access: yesAdvanced Science, EarlyView.
A data‐efficient inverse design framework automates the optimization of magnetic soft millirobots for confined‐space navigation. Integrating a physics‐based Cosserat rod model with Bayesian optimization efficiently identifies high‐performance geometries.
Ziyu Ren   +5 more
wiley   +1 more source

Probability Distribution and Option Pricing for Drawdown in a Stochastic Volatility Environment ( Revised in May 2009; Electronic version of an article will be published in "International Journal of Theoretical and Applied Finance". [copyright world Scientific Publishing Company][http://www.worldscinet.com/ijtaf/] ) [PDF]

open access: yes
This paper studies the probability distribution and option pricing for drawdown in a stochastic volatility environment. Their analytical approximation formulas are derived by the application of a singular perturbation method (Fouque et al. [7]).
Kyo Yamamoto   +2 more
core  

Interpretable Machine Learning Framework for Nb─Si Based Alloy Design with Enhanced Fracture Toughness

open access: yesAdvanced Science, EarlyView.
An interpretable machine learning framework integrating SHAP and PDP analysis identifies critical design descriptors from 139 physicochemical features for Nb─Si alloys. The framework achieves <7% prediction error and guides the discovery of Nb38.5Ti38.5Si3Zr18V2 alloy with 22.791 MPa·m1/2 fracture toughness, breaking the 20 MPa·m1/2 barrier.
Dezhi Chen   +7 more
wiley   +1 more source

Probability and Typicality in Deterministic Physics [PDF]

open access: yes, 2010
Recently it has been argued that typicality considerations play a crucial explanatory role in deterministic theories in physics (e.g. classical statistical mechanics and Bohmian mechanics).
Hemmo, Meir, Shenker, Orly R.
core  

How Advanced Artificial Intelligence Technologies Shape Drug–Drug and Drug–Target Interaction Modeling

open access: yesAdvanced Science, EarlyView.
This review explores the convergence of artificial intelligence technologies in modeling drug–drug and drug–target interactions. By evaluating advanced feature engineering, architectural innovations, and learning paradigms reveals shared evolutionary trends and critical challenges, such as cold‐start settings and shortcut learning.
Xin Sun, Tong Wang
wiley   +1 more source

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