Results 231 to 240 of about 319,994 (331)

Retinal Vessel Segmentation: A Comprehensive Review From Classical Methods to Deep Learning Advances (1982–2025)

Advanced Intelligent Systems, EarlyView.
Four decades of retinal vessel segmentation research (1982–2025) are synthesized, spanning classical image processing, machine learning, and deep learning paradigms. A meta‐analysis of 428 studies establishes a unified taxonomy and highlights performance trends, generalization capabilities, and clinical relevance.
Avinash Bansal, Jan Kubíček, Jana Bahrová, Alice Varyšová, Martin Augustýnek, Marek Penhaker, Juraj Timkovič   +6 more
wiley   +1 more source

Agentic Much? Adoption of Coding Agents on GitHub

Romain Robbes, Théo Matricon, Thomas Degueule, Andre Hora, Stefano Zacchiroli   +4 more
openalex   +1 more source

Benchmarking RNA velocity methods across 17 independent studies. [PDF]

Cell Rep Methods
Luo Y, Ren J, Yang Q, You Z, Zhou Y, Qin Q, Li Q.   +6 more
europepmc   +1 more source

Neuromorphic Denoising with Fully Analog Memristive In‐Memory Computing

Advanced Intelligent Systems, EarlyView.
This article borrows the concepts of episodic memory in human brains to experimentally implement a memristor‐based neuromorphic denoising process. A homogeneous memristor processing unit is experimentally demonstrated for both temporal storage and neural network computation, imitating the synapses in the human brain.
Daijing Shi, Teng Zhang, Yuqi Li, Yaoyu Tao, Bonan Yan, Yuchao Yang   +5 more
wiley   +1 more source

The Impact of Large Language Models on Open-source Innovation: Evidence from GitHub Copilot [PDF]

Doron Yeverechyahu, Raveesh Mayya, Gal Oestreicher-Singer   +2 more
openalex   +1 more source

Mondo: integrating disease terminology across communities. [PDF]

Genetics
Vasilevsky NA, Toro S, Matentzoglu N, Flack JE, Mullen KR, Hegde H, Gehrke S, Whetzel PL, Shwetar Y, Harris NL, Ngu MS, Alyea GL, Kane MS, Roncaglia P, Sid E, Thaxton CL, Wood V, Abraham RS, Achatz MI, Ajuyah P, Amberger JS, Babb L, Baker J, Balhoff JP, Berg JS, Bhalla A, Bofill-De Ros X, Braun IR, Broeren EC, Byer BK, Byrne AB, Callahan TJ, Carmody LC, Chan LE, Clause AR, Cohen JS, DeLuca M, Deuitch NT, Flowers M, Fraser J, Fujiwara T, Gitau V, Goldstein JL, Gration D, Groza T, Gyori BM, Hankey W, Hilton JA, Himmelstein DS, Hong SS, Hoyt CT, Huether R, Hurwitz E, Jacobsen JOB, Kikuchi A, Köhler S, Korn DR, Lagorce D, Laraway BJ, Li JY, Malheiro AJ, McLaughlin J, Meldal BHM, Mohan S, Moxon SAT, Munoz-Torres MC, Nelson TH, Nicholas FW, Ochoa D, Olson D, Oprea TI, Oskotsky TT, Osumi-Sutherland D, Paris K, Parkinson HE, Pendlington ZM, Peng XP, Pizzino A, Plon SE, Powell BC, Ratliff JC, Rehm HL, Remennik L, Riggs ER, Roberts S, Robinson PN, Ross JE, Schaper K, Schilder BM, Schmidt JL, Sharp EW, Similuk MN, Smedley D, Sneddon TP, Sparks R, Stefancsik R, Stupp GS, Sundar S, Takatsuki T, Tammen I, Tshering KC, Unni DR, Valasek E, Vanderver A, Wagner AH, Webb RF, Welter D, Yaya-Stupp D, Zankl A, Zhang XA, McMurry JA, Chute CG, Hamosh A, Mungall CJ, Haendel MA.   +114 more
europepmc   +1 more source

An Integrated and Robust Deep Learning Framework for Denoising and Analyzing Single‐Cell Spatial Transcriptomics

Advanced Intelligent Systems, EarlyView.
Single‐cell Spatial Transcriptomics Analysis and Denoising Engine is introduced as a unified deep learning framework that jointly performs denoising, clustering, and gene prioritization in spatial transcriptomics. By integrating linear and nonlinear representations within a dual‐channel architecture, it improves robustness and accuracy, uncovers ...
Yaxuan Cui, Yang Cui, Ruheng Wang, Xin Zeng, Zheyong Zhu, Ding Yi, Ruihua Guo, Tetsuya Sakurai, Kenta Nakai, Xiucai Ye, Leyi Wei, Yong Chen   +11 more
wiley   +1 more source

Beginning Git and GitHub [PDF]

Mariot Tsitoara
openalex   +1 more source

cAMRah: a scalable and portable workflow for harmonized antimicrobial resistance gene prediction from bacterial genomes. [PDF]

Bioinform Adv
Matute DL, Clarke TH, LaPointe AR, Singh I, Fouts DE.   +4 more
europepmc   +1 more source

Innovative Artificial Intelligence‐Assisted Systems for Social Science Research: Architecture Design and Applied Practice

AI &Innovation, EarlyView.
ABSTRACT The rapid advancement of large language model (LLM) technology is profoundly transforming the practice of social science research. Scholarly discussions on Artificial Intelligence (AI)'s role in social science research can be organised into three levels: AI as a research tool, AI as a methodological infrastructure and AI as a quasi‐cognitive ...
Jie Xiong
wiley   +1 more source