Results 61 to 70 of about 27,824 (260)

Forecasting the monthly incidence rate of brucellosis in west of Iran using time series and data mining from 2010 to 2019.

PLoS ONE, 2020
BACKGROUND:The identification of statistical models for the accurate forecast and timely determination of the outbreak of infectious diseases is very important for the healthcare system.
Hadi Bagheri, Leili Tapak, Manoochehr Karami, Zahra Hosseinkhani, Hamidreza Najari, Safdar Karimi, Zahra Cheraghi   +6 more
doaj   +1 more source

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

Advanced 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, Jiahui Xie, Yi Liao, Junyu Li, Longqi Liu, Xin Gao, Hongmin Cai   +6 more
wiley   +1 more source

Multilayer perceptrons and data compression

Comput. Artif. Intell., 2007
This paper investigates the feasibility of using artificial neural networks as a tool for data compression. More precisely, the paper measures compression capabilities of the standard multilayer perceptrons. An outline of a possible "neural" data compression method is given.
Manger, Robert, Puljić, Krunoslav
openaire   +3 more sources

Mixture of Multilayer Perceptron Regressions

Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods, 2019
This paper investigates mixture of multilayer perceptron (MLP) regressions. Although mixture of MLP regressions (MoMR) can be a strong fitting model for noisy data, the research on it has been rare. We employ soft mixture approach and use the Expectation-Maximization (EM) algorithm as a basic learning method. Our learning method goes in a double-looped
Ryohei Nakano, Seiya Satoh
openaire   +1 more source

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

Advanced 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, Shuli Sun, Zhengliang Lv, Xinyu Liu, Yihua Wang, Bingqiang Liu   +5 more
wiley   +1 more source

Diffusion‐Based Generative Model With Scaffold‐Hopping Strategy Yields Highly Potent Bioactive Molecules

Advanced Science, EarlyView.
SMarT‐Diff introduces a multi‐objective generative paradigm that integrates scaffold hopping with structure‐aware scoring to enable controlled exploration beyond the training distribution. The framework consistently balances drug‐likeness, synthesizes accessibility and bioactivity, yielding chemically diverse candidates with enhanced properties.
Yuwei Yang, Xiaoqing Gong, Shukai Gu, Jing Li, Bo Liu, Yanan Tian, Qianqian Zhang, Xiaojun Yao, Huanxiang Liu   +8 more
wiley   +1 more source

From Label‐Free Multiphoton Imaging to Pathological Reports: A Vision‐Language Breast Cancer Margin Pathological Diagnosis System

Advanced Science, EarlyView.
MarginPath is a novel vision‐language system that automates breast cancer margin assessment using a single label‐free multiphoton microscopy image. By integrating tumor‐associated collagen signatures with virtual H&E imaging, it generates accurate margin heatmaps and comprehensive diagnostic reports.
Shu Wang, Jingze Su, Xiahui Han, Deyong Kang, Xiao Zhang, Fei Xu, Changzu Liu, Junlin Pan, Xingfu Wang, Qiaohui Zhan, Aimin Wang, Feng Huang, Heping Cheng, Wenxi Liu, Ruolan Lin, Jianxin Chen   +15 more
wiley   +1 more source

Universal Equivariant Multilayer Perceptrons

CoRR, 2020
Group invariant and equivariant Multilayer Perceptrons (MLP), also known as Equivariant Networks, have achieved remarkable success in learning on a variety of data structures, such as sequences, images, sets, and graphs. Using tools from group theory, this paper proves the universality of a broad class of equivariant MLPs with a single hidden layer. In
openaire   +3 more sources

PlantGFM: A Genomic Foundation Model for Discovery and Creation of Plant Genes

Advanced Science, EarlyView.
A plant genomic foundation model pre‐trained on 12 species enables both accurate gene prediction and de novo gene design. Through AI‐human knowledge screening, seven designed sequences showed transcriptional activity in plants, with two expressing stable proteins—demonstrating the first DNA‐RNA‐protein expression of LLM‐generated genes in plants and ...
Changhao Li, Qizhe Zhang, Hanchen Chen, Kepeng Lin, Chengfang Luo, Mengying Yang, Wei Xu, Fan Yao, Jianbing Yan, Qing‐Yong Yang, Xuehai Hu   +10 more
wiley   +1 more source

Topology‐Aware Deep Learning on Higher‐Order Structures for Drug Response Prediction

Advanced Science, EarlyView.
We present TopDr, a topology‐aware deep learning framework that encodes both drugs and cell lines as multiscale simplicial complexes, capturing interactions at the 0‐, 1‐, and 2‐simplex levels. By jointly integrating local higher‐order neighborhoods and global topological structures, TopDr generates enriched representations for sensitivity prediction ...
Cong Shen, Guancen Lin, Chuan‐Shen Hu, Yong Wang   +3 more
wiley   +1 more source