Results 41 to 50 of about 15,135,820 (245)

Adversarial Learning for Cross-Project Semi-Supervised Defect Prediction

IEEE Access, 2020
Cross-project defect prediction (CPDP) aims to build a prediction model on existing source projects and predict the labels of target project. The data distribution difference between different projects makes CPDP very challenging.
Ying Sun, Xiao-Yuan Jing, Fei Wu, Juanjuan Li, Danlei Xing, Haowen Chen, Yanfei Sun   +6 more
doaj   +1 more source

Spatial and single‐nuclei transcriptomics reveals idiosyncratic and generic patterns in papillary and anaplastic thyroid cancers

Molecular Oncology, EarlyView.
Matched spatial transcriptomics and single‐nuclei RNA‐seq were generated for anaplastic and BRAFV600E papillary thyroid cancers revealing generic and tumor‐specific states occurring in cancer cells and in the tumor microenvironment. In this context, cancer dedifferentiation mirrored organoid maturation through ordered thyroid marker gain/loss ...
Adrien Tourneur, Joel Rodrigues Vitória, Manuel Saiselet, Ligia Craciun, Denis Larsimont, Anne Lefort, Frédérick Libert, Carine Maenhaut, Sabine Costagliola, Maxime Tarabichi, Mirian Romitti, Vincent Detours   +11 more
wiley   +1 more source

Multi-Objective Cross-Project Defect Prediction

, 2013
Cross-project defect prediction is very appealing because (i) it allows predicting defects in projects for which the availability of data is limited, and (ii) it allows producing generalizable prediction models.
Annibale Panichella, Oliveto, Rocco, M. Di Penta, Massimiliano Di Penta, DE LUCIA, Andrea, Andrea De Lucia, PANICHELLA, ANNIBALE, S. Panichella, Rocco Oliveto, Sebastiano Panichella, G. Canfora, Gerardo Canfora, R. Oliveto   +12 more
core   +1 more source

UiO‐66 metal–organic frameworks in biomedicine: From structural tunability to bioimaging, photodiagnostics, and photodynamic cancer therapy

FEBS Open Bio, EarlyView.
UiO‐66(Zr) metal–organic frameworks are chemically stable, biocompatible, and highly tunable nanomaterials. Their modular structure enables controlled drug delivery, multimodal bioimaging, and light‐activated photodynamic therapy, supporting integrated diagnostic and therapeutic (theranostic) applications in cancer and biomedical research.
Veronika Huntošová, Grigorii Rakhalskii, Miroslav Almáši   +2 more
wiley   +1 more source

A benchmark study on the effectiveness of search-based data selection and feature selection for cross project defect prediction [PDF]

, 2017
Context: Previous studies have shown that steered training data or dataset selection can lead to better performance for cross project defect prediction(CPDP). On the other hand, feature selection and data quality are issues to consider in CPDP.Objective:
Seyedrebvar Hosseini, Turhan, Burak, Mika Mäntylä, Mäntylä, Mika, Burak Turhan, Hosseini, Seyedrebvar   +5 more
core   +1 more source

HDA: Cross-Project Defect Prediction via Heterogeneous Domain Adaptation With Dictionary Learning

IEEE Access, 2018
Cross-Project Defect Prediction (CPDP) is an active topic for predicting defects on projects (target projects) with scarce-labeled data by reusing the classification models from other projects (source projects).
Zhou Xu, Peipei Yuan, Tao Zhang, Yutian Tang, Shuai Li, Zhen Xia   +5 more
doaj   +1 more source

KDAC6 alters cell morphology and motility through positive and negative modulation of F‐actin distribution

FEBS Open Bio, EarlyView.
KDAC6 has been associated with cell motility and actin structures, but specific domain contributions are not established. The two catalytic domains have differential effects on motility and F‐actin regulation, affecting cortical F‐actin density, motility, stress fibers, and cell spreading.
Taylor V. Joseph, Andrea B. Pham, Kiara E. Bornes, Razan H. Hammad, Chelsea A. Rousseve‐Ross, Thomas M. Huckaba, Terry J. Watt, Tasha B. Toro   +7 more
wiley   +1 more source

Performance Evaluation of Convolutional Neural Network for Multi-Class in Cross Project Defect Prediction

Applied Sciences, 2022
Cross-project defect prediction (CPDP) is a practical approach for finding software defects in projects which have incomplete or fewer data. Improvements to the defect prediction accuracy of CPDP—such as the PROMISE repository, the correct classification
Sundas Noreen, Rizwan Bin Faiz, Sultan Alyahya, Mohamed Maddeh   +3 more
doaj   +1 more source

Evolution‐guided yeast complementation reveals functional differences in human PSPH variants

FEBS Open Bio, EarlyView.
Ancient genomes can help guide which human genetic variants are tested experimentally. This study applies that idea to PSPH, a gene involved in serine biosynthesis, and uses high‐throughput yeast complementation to compare variant function. The findings reveal measurable differences among selected alleles and illustrate the value of evolution‐guided ...
Mauricio Campa‐Álvarez, Diana Ascencio, Miguel Vallebueno‐Estrada, Eduardo González‐Orozco, Christian Eduardo Martínez‐Guerrero, Rafael Montiel, Alexander DeLuna   +6 more
wiley   +1 more source

Cross-Project Defect Prediction with Metrics Selection and Balancing Approach

Applied Computer Systems, 2022
In software development, defects influence the quality and cost in an undesirable way. Software defect prediction (SDP) is one of the techniques which improves the software quality and testing efficiency by early identification of defects(bug/fault/error)
Nevendra Meetesh, Singh Pradeep
doaj   +1 more source