15 papers · ranked by Valyu relevance
Ahmad Lutfi, Zhuo A. Chen, Lutz Fischer, Juri Rappsilber
Mass spectrometry (MS) experiments generate rich acquisition metadata that are essential for reproducibility, data sharing, and quality control (QC). Because these metadata are typically stored only in vendor-specific formats, they often remain difficult to access. MetaXtract is a lightweight tool that extracts…
Michail Lazaratos, Neele Haacke, Jasmin Gaugel, Miriam Ulz + 4 more
metaKEGG is a comprehensive software package designed to streamline the visualization and integration of pathway enrichment results from multi-omics data, providing accessible and detailed insights into the molecular mechanisms driving health and disease. Unlike standard pipeline approaches, metaKEGG incorporates novel…
Ramon van der Zwaan, Berdien van Olst, Mark C.M. van Loosdrecht, Martin Pabst
Microbial community proteomics is rapidly gaining traction as it allows exploration of functional processes in microbial ecosystems. Consequently, there is a growing need for user-friendly tools that enable performance evaluation and interactive visualization of the increasingly complex community proteomics data. We…
Adrián Rošinec, Terézia Slanináková, Tomáš Pavlík, Róbert Randiak + 8 more
The volume of molecular dynamics (MD) simulation data shared via public repositories is rapidly increasing; however, fragmentation across multiple independent repositories, each employing distinct dataset identifiers and metadata schemas, hinders the efficient exploration and reuse of these data. In this study, we…
Antonios Saravanos, John Pazarzis, Stavros Zervoudakis, Dongnanzi Zheng
Python libraries often need to maintain a stable public API even as internal implementations evolve, gain new backends, or depend on heavy optional libraries. In Python, where internal objects are easy to inspect and import, users can come to rely on "reachable internals" that were never intended to be public, making…
Xiaowen Sun, Jiahao Li, Guiying Yan, Renmin Han + 1 more
In our study, the early AD diagnosis task is modeled as a subject-based node classification problem, and a meta-learning paradigm is adopted to strengthen the use of subject data and improve the problem of uneven distribution of subjects as much as possible. Graph convolutional network also has a outstanding structure.…
Zishang Qiu, Xinan Chen, Rong Qu, Ruibin Bai
Large Language Models (LLMs) have advanced Automatic Heuristic Design (AHD) by enabling heuristic generation through reasoning and code synthesis. Existing LLM-based AHD architectures mainly follow two paradigms: Natural Evolution, which uses crossover and mutation to explore heuristic programs, and Metacognitive…
Hsieh-Ting Lin, Jiunn-Tyng Yeh
Objective: To describe the architecture and design rationale of meta-pipe, an open-source large language model (LLM)-agent pipeline that integrates the complete systematic review and meta-analysis (SR/MA) workflow -- from literature search through statistical analysis, manuscript generation, and quality assurance --…
P. Travis Thompson (PTT), Hunter N.B. Moseley (HNBM)
The Metabolomics Workbench (MW) is a public scientific data repository consisting of experimental data and metadata from metabolomics studies collected with mass spectroscopy (MS) and nuclear magnetic resonance (NMR) analyses. Although not as rapidly as in the past, MW has steadily evolved; updating its mwTab and JSON…
Saim Bokhari, Yazan Bdour, Ribal Georges Sabat, Giuseppe Brunetti + 1 more
Large-area metasurfaces were fabricated via a tunable pyramidal interference lithography (PIL) technique, which uses custom-built 2-faced, 3-faced, and 4-faced pyramidal prisms to create metasurfaces with customizable nano- and micro-scale surface feature periodicities. The 2-faced prism produced linear surface relief…
Dao Sy Duy Minh, Tran Chi Nguyen, Trung Kiet Huynh, Pham Phu Hoa + 2 more
We present MEMRES, an agentic system for Python dependency resolution that introduces a multi-level confidence cascade where the LLM serves as the last resort. Our system combines: (1) a Self-Evolving Memory that accumulates reusable resolution patterns via tips and shortcuts; (2) an Error Pattern Knowledge Base with…
Kai Cheng, Daniel Figeys
Metaproteomic peptide identification is constrained by the structure and size of the protein search space. Pooled gene catalogues provide coverage but obscure genome-level evidence, and current workflows for data-dependent (DDA) and data-independent (DIA) acquisition diverge in their database strategies. We present…
Nicholas Sunderland, David A Hughes, Mathew A Lee, Alec McKinlay + 2 more
High-throughput multiplex assays for metabolomics and proteomics offer opportunities for biomarker discovery and disease stratification in epidemiological research. The complexity of these datasets requires robust, standardized and transparent preprocessing workflows to ensure reproducibility and comparability across…
Qiuhao Zeng
In few-shot learning, classifiers are expected to generalize to unseen classes given only a small number of instances of each new class. One of the popular solutions to few-shot learning is metric-based meta-learning. However, it highly depends on the deep metric learned on seen classes, which may overfit to seen…
Ricardo Inácio, Vitor Cerqueira, Marília Barandas, Carlos Soares
Deep learning methods have achieved state-of-the-art in time series forecasting, yet their accuracy varies considerably across samples, as some instances remain inherently difficult to predict. Reject option mechanisms, which allow models to abstain from high-risk predictions, are well established in classification and…