MatchIt: Nonparametric Preprocessing for Parametric Causal Inference
Journal of Statistical Software, 2011 MatchIt implements the suggestions of Ho, Imai, King, and Stuart (2007) for improving parametric statistical models by preprocessing data with nonparametric matching methods.
Daniel Ho +3 more
doaj +2 more sources
Ultrafast one‐pass FASTQ data preprocessing, quality control, and deduplication using fastp
iMeta, 2023 A large amount of sequencing data is generated and processed every day with the continuous evolution of sequencing technology and the expansion of sequencing applications.
Shifu Chen
semanticscholar +1 more source
TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning [PDF]
Comput. Methods Programs Biomed., 2020 Highlights • Open-source Python library for preprocessing, augmentation and sampling of medical images for deep learning.• Support for 2D, 3D and 4D images such as X-ray, histopathology, CT, ultrasound and diffusion MRI.• Modular design inspired by the ...
Fernando Pérez-García +2 more
semanticscholar +1 more source
Frontiers in Energy Research, 2021 The rapid development in data science and the increasing availability of building operational data have provided great opportunities for developing data-driven solutions for intelligent building energy management.
C. Fan +4 more
semanticscholar +1 more source
Information Systems, 2023 With the advent of the modern pre-trained Transformers, the text preprocessing has started to be neglected and not specifically addressed in recent NLP literature. However, both from a linguistic and from a computer science point of view, we believe that
Marco Siino, I. Tinnirello, M. Cascia
semanticscholar +1 more source
Modular, efficient and constant-memory single-cell RNA-seq preprocessing
Nature Biotechnology, 2021 We describe a workflow for preprocessing of single-cell RNA-sequencing data that balances efficiency and accuracy. Our workflow is based on the kallisto and bustools programs, and is near optimal in speed with a constant memory requirement providing ...
P. Melsted +10 more
semanticscholar +1 more source
Imbalanced data preprocessing techniques for machine learning: a systematic mapping study
Knowledge and Information Systems, 2022 Machine Learning (ML) algorithms have been increasingly replacing people in several application domains—in which the majority suffer from data imbalance.
Vitor Werner de Vargas +4 more
semanticscholar +1 more source
Fair preprocessing: towards understanding compositional fairness of data transformers in machine learning pipeline [PDF]
ESEC/SIGSOFT FSE, 2021 In recent years, many incidents have been reported where machine learning models exhibited discrimination among people based on race, sex, age, etc. Research has been conducted to measure and mitigate unfairness in machine learning models.
Sumon Biswas, Hridesh Rajan
semanticscholar +1 more source
Preprocessing Strategies for Sparse Infrared Spectroscopy: A Case Study on Cartilage Diagnostics
Molecules, 2022 The aim of the study was to optimize preprocessing of sparse infrared spectral data. The sparse data were obtained by reducing broadband Fourier transform infrared attenuated total reflectance spectra of bovine and human cartilage, as well as of ...
Valeria Tafintseva +14 more
doaj +1 more source
Automatic identification of charcoal origin based on deep learning
Maderas: Ciencia y Tecnología, 2021 The differentiation between the charcoal produced from (Eucalyptus) plantations and native forests is essential to control, commercialization, and supervision of its production in Brazil.
Ricardo Rodrigues de Oliveira Neto +9 more
doaj +1 more source