PreCanCell: An ensemble learning algorithm for predicting cancer and non-cancer cells from single-cell transcriptomes

Yang, Tao; Yan, Qiyu; Long, R. E.; Liu, Zhixian; Wang, Xiaosheng

doi:10.1016/j.csbj.2023.07.009

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…the identification of distinct clusters corresponding to different cell types. 42 The nearest neighbor approach is utilized to determine the optimal projection of a new query cell onto a reference data set (Table 1). Seurat is a widely utilized toolkit for single-cell data analysis, adopts an approach based on canonical correlation analysis and mutual nearest neighbors.…”

Section: Ai In Auxiliary Diagnosis Of Infectious Diseasesmentioning

confidence: 99%

“…Another study focused on subtype‐specific predictive biomarker discovery, applicable to disease diagnosis and treatment 41 . Scmap employs a graph‐based clustering technique to assess the maximum similarity between cells in both reference and query data, enabling the identification of distinct clusters corresponding to different cell types 42 . The nearest neighbor approach is utilized to determine the optimal projection of a new query cell onto a reference data set (Table 1).…”

Section: The Application Of Ai In the Field Of Infectious Diseasesmentioning

confidence: 99%

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Artificial Intelligence in battling infectious diseases: A transformative role

Li,

Ye,

Jiang

et al. 2024

Journal of Medical Virology

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It is widely acknowledged that infectious diseases have wrought immense havoc on human society, being regarded as adversaries from which humanity cannot elude. In recent years, the advancement of Artificial Intelligence (AI) technology has ushered in a revolutionary era in the realm of infectious disease prevention and control. This evolution encompasses early warning of outbreaks, contact tracing, infection diagnosis, drug discovery, and the facilitation of drug design, alongside other facets of epidemic management. This article presents an overview of the utilization of AI systems in the field of infectious diseases, with a specific focus on their role during the COVID‐19 pandemic. The article also highlights the contemporary challenges that AI confronts within this domain and posits strategies for their mitigation. There exists an imperative to further harness the potential applications of AI across multiple domains to augment its capacity in effectively addressing future disease outbreaks.

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Section: Ai In Auxiliary Diagnosis Of Infectious Diseasesmentioning

confidence: 99%

Section: The Application Of Ai In the Field Of Infectious Diseasesmentioning

confidence: 99%

Artificial Intelligence in battling infectious diseases: A transformative role

Li,

Ye,

Jiang

et al. 2024

Journal of Medical Virology

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A Short Analysis of Hybrid Approaches in COVID‑19 for Detection and Diagnosing

Simić,

Banković,

Villar

et al. 2024

Lecture Notes in Computer Science

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Multi-modal feature fusion with multi-head self-attention for epileptic EEG signals

Huang,

Xi,

Jiao

et al. 2024

MBE

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<p>It is important to classify electroencephalography (EEG) signals automatically for the diagnosis and treatment of epilepsy. Currently, the dominant single-modal feature extraction methods cannot cover the information of different modalities, resulting in poor classification performance of existing methods, especially the multi-classification problem. We proposed a multi-modal feature fusion (MMFF) method for epileptic EEG signals. First, the time domain features were extracted by kernel principal component analysis, the frequency domain features were extracted by short-time Fourier extracted transform, and the nonlinear dynamic features were extracted by calculating sample entropy. On this basis, the features of these three modalities were interactively learned through the multi-head self-attention mechanism, and the attention weights were trained simultaneously. The fused features were obtained by combining the value vectors of feature representations, while the time, frequency, and nonlinear dynamics information were retained to screen out more representative epileptic features and improve the accuracy of feature extraction. Finally, the feature fusion method was applied to epileptic EEG signal classifications. The experimental results demonstrated that the proposed method achieves a classification accuracy of 92.76 ± 1.64% across the five-category classification task for epileptic EEG signals. The multi-head self-attention mechanism promotes the fusion of multi-modal features and offers an efficient and novel approach for diagnosing and treating epilepsy.</p>

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PreCanCell: An ensemble learning algorithm for predicting cancer and non-cancer cells from single-cell transcriptomes

Cited by 3 publications

References 30 publications

Artificial Intelligence in battling infectious diseases: A transformative role

Artificial Intelligence in battling infectious diseases: A transformative role

A Short Analysis of Hybrid Approaches in COVID‑19 for Detection and Diagnosing

Multi-modal feature fusion with multi-head self-attention for epileptic EEG signals

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