Using single-cell and spatial transcriptomes to understand stem cell lineage specification during early embryo development

G Peng, G Cui, J Ke, N **g - Annual review of genomics and …, 2020 - annualreviews.org
Embryonic development and stem cell differentiation provide a paradigm to understand the
molecular regulation of coordinated cell fate determination and the architecture of tissue …

Slide4N: Creating presentation slides from computational notebooks with Human-AI collaboration

F Wang, X Liu, O Liu, A Neshati, T Ma, M Zhu… - Proceedings of the 2023 …, 2023 - dl.acm.org
Data scientists often have to use other presentation tools (eg, Microsoft PowerPoint) to
create slides to communicate their analysis obtained using computational notebooks. Much …

Automated machine learning (AutoML): the future of computational intelligence

G Mengi, SK Singh, S Kumar, D Mahto… - … conference on cyber …, 2021 - Springer
Computer science controls every task in today's environment, and everything in the sector
attempts to automate the task. The basic essence of computer science is to minimize human …

Interpretable feature extraction and dimensionality reduction in ESM2 for protein localization prediction

Z Luo, R Wang, Y Sun, J Liu, Z Chen… - Briefings in …, 2024 - academic.oup.com
As the application of large language models (LLMs) has broadened into the realm of
biological predictions, leveraging their capacity for self-supervised learning to create feature …

Facing small and biased data dilemma in drug discovery with enhanced federated learning approaches

Z **ong, Z Cheng, X Lin, C Xu, X Liu, D Wang… - Science China Life …, 2021 - Springer
Artificial intelligence (AI) models usually require large amounts of high-quality training data,
which is in striking contrast to the situation of small and biased data faced by current drug …

BioAutoML: automated feature engineering and metalearning to predict noncoding RNAs in bacteria

RP Bonidia, APA Santos, BLS de Almeida… - Briefings in …, 2022 - academic.oup.com
Recent technological advances have led to an exponential expansion of biological
sequence data and extraction of meaningful information through Machine Learning (ML) …

Automated machine learning system for defect detection on cylindrical metal surfaces

YC Huang, KC Hung, JC Lin - Sensors, 2022 - mdpi.com
Metal workpieces are indispensable in the manufacturing industry. Surface defects affect the
appearance and efficiency of a workpiece and reduce the safety of manufactured products …

Computing infrastructure construction and optimization for high-performance computing and artificial intelligence

Y Su, J Zhou, J Ying, M Zhou, B Zhou - CCF Transactions on High …, 2021 - Springer
The emergence of supercomputers has brought rapid development to human life and
scientific research. Today, the new wave of artificial intelligence (AI) not only brings …

Potential predictive value of serum targeted metabolites and concurrently mutated genes for EGFR-TKI therapeutic efficacy in lung adenocarcinoma patients with …

X Han, R Luo, L Wang, L Zhang… - American Journal of …, 2020 - pmc.ncbi.nlm.nih.gov
There is a discrepancy in the efficacy of epidermal growth factor receptor-tyrosine kinase
inhibitor (EGFR-TKI) treatment for advanced lung adenocarcinoma (LUAD) patients with …

[HTML][HTML] AutoGGN: a gene graph network AutoML tool for multi-omics research

L Zhang, W Shen, P Li, C Xu, D Liu, W He, Z Xu… - Artificial Intelligence in …, 2021 - Elsevier
Omics data can be used to identify biological characteristics from genetic to phenotypic
levels during the life span of a living being, while molecular interaction networks have a …