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Văn Nhung Lê on Muck Rack

Văn Nhung Lê

As seen in: Wiley Online Library, arXiv, Frontiers in Immunology, MDPI, Dialnet - Universidad de La Rioja, PitchBook, Cancer Research, International Journal of Social Economics, Vietgiaitri.com, baophapluat.vn and

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Articles

The role of teachers in assessing and evaluating high school pupils’ learning outcomes

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By Bao Nguyen, bảo nguyên, Van Le, Vân Anh Lê, Văn Nhung Lê, Ninh Van Le
| Dialnet - Universidad de La Rioja
español El propósito de este estudio es clarificar el rol del profesorado en la evaluación de los resultados de aprendizaje del alumnado de secundaria mediante un enfoque de garantía de calidad. La investigación no solo identifica los requisitos básicos de la evaluación orientada a la calidad, sino que también busca identificar las dificultades que enfrentan los docentes en su implementación práctica.
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Abstract P25: MS CETSA deep functional proteomics uncovers novel DNA repair programs leading to Gemcitabine resistance Free

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By Van Le, Vân Anh Lê, Văn Nhung Lê, Vivian Teo, Hui kok min
| Cancer Research Verified
Mechanisms for resistance to cytotoxic cancer drugs are dependent on dynamic changes in the biochemistry of cellular pathways, information which is hard to obtain at the systems level. Here we use a deep functional proteomics implementation of the Cellular Thermal Shift Assay to reveal a range of induced biochemical responses to gemcitabine in resistant and sensitive diffuse large B cell lymphoma cell lines.
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Lightweight Machine Learning for Oyster-Reef Environmental Forecasting: A Random-Forest Baseline with Classical and Deep Benchmarks

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By Van Le, Vân Anh Lê, Văn Nhung Lê, Ransford Antwi, Tan D. Le, Tấn Thành Lê
| preprints.org
You are already at the latest version Short-term forecasting of coastal environmental conditions is critical for supporting oyster-reef management, early detection of stress events, and informed decision-making in dynamic estuarine systems. While deep learning models have gained traction in environmental prediction, lightweight machine learning approaches remain compelling due to their low computational cost, robustness to noisy sensor data, and interpretability.
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