FSL-Kla: A few-shot learning-based multi-feature hybrid system for lactylation site prediction

Jiang, Peiran; Ning, Wanshan; Shi, Yunshu; Liu, Chuan; Mo, Saijun; Zhou, Haoran; Liu, Kangdong; Guo, Yaping

doi:10.1016/j.csbj.2021.08.013

Cited by 20 publications

(14 citation statements)

References 58 publications

(67 reference statements)

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“…Interestingly, this consensus sequence showed a high similarity to that recently reported in a computational study for lactylation site prediction. 53 For example, lysine residues were enriched at positions −4 and +4 in both consensus sequences. Moreover, small amino acid residues such as alanine and glycine were preferred at positions near the lactylation site ( e.g.…”

Section: Resultsmentioning

confidence: 99%

A bioorthogonal chemical reporter for the detection and identification of protein lactylation

2022

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Section: Resultsmentioning

confidence: 99%

A bioorthogonal chemical reporter for the detection and identification of protein lactylation

2022

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“…Few-shot learning is a specific approach that provides eigenvalue extraction and classification within small training datasets [ 29 , 31 , 32 , 34 ]. This technique aims to establish a classifier to recognize unseen features (target domain) from existing models (source domain).…”

Section: Discussionmentioning

confidence: 99%

“…In order to overcome the limitation of the few patients, we suggest a novel application of few-shot learning algorithm for predict the prognosis after MRgFUS palliative treatment. The few-shot learning approach was specified to extract eigenvalues and classify hidden subsets [ 31 , 32 , 34 , 39 ]. The algorithm embedded a Bayesian framework that minimized training samples by prior parameter selections.…”

Section: Discussionmentioning

confidence: 99%

“…However, the complexity of cytokine combinations is a bottleneck of traditional methods to elucidate the local control rate. Such multidimensional analysis requires more samples to achieve statistical significance [ 29 , 30 , 31 ]. The few-shot learning approach was used for high-throughput omic profiling to interpret genomewide data in a small-scale study population [ 30 ].…”

Section: Introductionmentioning

confidence: 99%

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A Few-Shot Learning Approach Assists in the Prognosis Prediction of Magnetic Resonance-Guided Focused Ultrasound for the Local Control of Bone Metastatic Lesions

Hsu

Lee

Chen

et al. 2022

Cancers

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Magnetic resonance-guided focused ultrasound surgery (MRgFUS) constitutes a noninvasive treatment strategy to ablate deep-seated bone metastases. However, limited evidence suggests that, although cytokines are influenced by thermal necrosis, there is still no cytokine threshold for clinical responses. A prediction model to approximate the postablation immune status on the basis of circulating cytokine activation is thus needed. IL-6 and IP-10, which are proinflammatory cytokines, decreased significantly during the acute phase. Wound-healing cytokines such as VEGF and PDGF increased after ablation, but the increase was not statistically significant. In this phase, IL-6, IL-13, IP-10, and eotaxin expression levels diminished the ongoing inflammatory progression in the treated sites. These cytokine changes also correlated with the response rate of primary tumor control after acute periods. The few-shot learning algorithm was applied to test the correlation between cytokine levels and local control (p = 0.036). The best-fitted model included IL-6, IL-13, IP-10, and eotaxin as cytokine parameters from the few-shot selection, and had an accuracy of 85.2%, sensitivity of 88.6%, and AUC of 0.95. The acceptable usage of this model may help predict the acute-phase prognosis of a patient with painful bone metastasis who underwent local MRgFUS. The application of machine learning in bone metastasis is equivalent or better than the current logistic regression.

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“…In other words, it will provide the user with an understanding of the manner of data organization in a high-dimensional space. This method has been introduced in 2008 by Laurens van der Maatens and Geoffery Hinton [36]. The main difference between this method and principal component analysis (PCA) is that PCA is a method of reducing the linear dimensions which attempts to maximize the variance and preserve the large distance between the pares, while t-SNE preserves PCA in preserving the small distance between pares by using local similarities.…”

Section: Distributed Stochastic Neighbor Embedding (T-sne)mentioning

confidence: 99%

An Accurate Heart Disease Prognosis Using Machine Intelligence and IoMT

Pirgazi

Sorkhi

Mobarkeh

2022

Wireless Communications and Mobile Computing

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In recent years, Internet of Medical Things (IoMT) and machine learning (ML) have played a major role in the healthcare industry and prediction of in time diagnosis of diseases. Heart disease has long been considered one of the most common and lethal causes of death. Accordingly, in this paper, a multiple-step method using IoMT and ML has been proposed for diagnosis of heart disease based on image and numerical resources. In the first step, transfer learning based on convolutional neural network (CNN) is used for feature extraction. In the second step, three methods of distributed stochastic neighbor embedding (t-SNE), F-score, and correlation-based feature selection (CFS) are utilized to select the best features. In the end, a combination of outputs of three classifiers including Gaussian Bayes (GB), support vector machine (SVM), and random forest (RF) according to the majority voting is employed for diagnosis of the conditions of heart disease patients. The results were evaluated on the two UCI datasets. The results indicate the improvement of performance compared to other methods.

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FSL-Kla: A few-shot learning-based multi-feature hybrid system for lactylation site prediction

Abstract: Graphical abstract

Cited by 20 publications

References 58 publications

A bioorthogonal chemical reporter for the detection and identification of protein lactylation

A bioorthogonal chemical reporter for the detection and identification of protein lactylation

A Few-Shot Learning Approach Assists in the Prognosis Prediction of Magnetic Resonance-Guided Focused Ultrasound for the Local Control of Bone Metastatic Lesions

An Accurate Heart Disease Prognosis Using Machine Intelligence and IoMT

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