Label-free proteome quantification and evaluation

Investigating cultivation strategies for enhancing protein content in Auxenochlorella pyrenoidosa FACHB-5

This study investigated the influence of yeast extract addition, carbon source, and photoperiod on the growth dynamics of Auxenochlorella pyrenoidosa FACHB-5. Employing response surface methodology, the culture strategy was optimized, resulting in the following optimal conditions: yeast extract addition at 0.75 g L-1, glucose concentration of 0.83 g L-1, and a photoperiod set at Light: Dark = 18 h: 6 h. Under these conditions, the biomass reached 1.76 g L-1 with a protein content of 750.00 g L-1, containing 40 % of essential amino acids, representing a 1.52-fold increase. Proteomic analysis revealed that the targeted cultivation strategy up-regulated genes involved in microalgal protein synthesis. The combined effect of yeast extract and glucose enhanced both the glutamine synthetase-glutamate synthetase mechanism and the free amino acid content.

MOINER: A Novel Multiomics Early Integration Framework for Biomedical Classification and Biomarker Discovery

In the context of precision medicine, multiomics data integration provides a comprehensive understanding of underlying biological processes and is critical for disease diagnosis and biomarker discovery. One commonly used integration method is early integration through concatenation of multiple dimensionally reduced omics matrices due to its simplicity and ease of implementation. However, this approach is seriously limited by information loss and lack of latent feature interaction. Herein, a novel multiomics early integration framework (MOINER) based on information enhancement and image representation learning is thus presented to address the challenges. MOINER employs the self-attention mechanism to capture the intrinsic correlations of omics-features, which make it significantly outperform the existing state-of-the-art methods for multiomics data integration. Moreover, visualizing the attention embedding and identifying potential biomarkers offer interpretable insights into the prediction results. All source codes and model for MOINER are freely available https://github.com/idrblab/MOINER.

Evaluating Machine Learning Methods of Analyzing Multiclass Metabolomics

Multiclass metabolomic studies have become popular for revealing the differences in multiple stages of complex diseases, various lifestyles, or the effects of specific treatments. In multiclass metabolomics, there are multiple data manipulation steps for analyzing raw data, which consist of data filtering, the imputation of missing values, data normalization, marker identification, sample separation, classification, and so on. In each step, several to dozens of machine learning methods can be chosen for the given data set, with potentially hundreds or thousands of method combinations in the whole data processing chain. Therefore, a clear understanding of these machine learning methods is helpful for selecting an appropriate method combination for obtaining stable and reliable analytical results of specific data. However, there has rarely been an overall introduction or evaluation of these methods based on multiclass metabolomic data. Herein, detailed descriptions of these machine learning methods in multiple data manipulation steps are reviewed. Moreover, an assessment of these methods was performed using a benchmark data set for multiclass metabolomics. First, 12 imputation methods for imputing missing values were evaluated based on the PSS (Procrustes statistical shape analysis) and NRMSE (normalized root-mean-square error) values. Second, 17 normalization methods for processing multiclass metabolomic data were evaluated by applying the PMAD (pooled median absolute deviation) value. Third, different methods of identifying markers of multiclass metabolomics were evaluated based on the CWrel (relative weighted consistency) value. Fourth, nine classification methods for constructing multiclass models were assessed using the AUC (area under the curve) value. Performance evaluations of machine learning methods are highly recommended to select the most appropriate method combination before performing the final analysis of the given data. Overall, detailed descriptions and evaluation of various machine learning methods are expected to improve analyses of multiclass metabolomic data.

SUnet: A multi-organ segmentation network based on multiple attention

Organ segmentation in abdominal or thoracic computed tomography (CT) images plays a crucial role in medical diagnosis as it enables doctors to locate and evaluate organ abnormalities quickly, thereby guiding surgical planning, and aiding treatment decision-making. This paper proposes a novel and efficient medical image segmentation method called SUnet for multi-organ segmentation in the abdomen and thorax. SUnet is a fully attention-based neural network. Firstly, an efficient spatial reduction attention (ESRA) module is introduced not only to extract image features better, but also to reduce overall model parameters, and to alleviate overfitting. Secondly, SUnet's multiple attention-based feature fusion module enables effective cross-scale feature integration. Additionally, an enhanced attention gate (EAG) module is considered by using grouped convolution and residual connections, providing richer semantic features. We evaluate the performance of the proposed model on synapse multiple organ segmentation dataset and automated cardiac diagnostic challenge dataset. SUnet achieves an average Dice of 84.29% and 92.25% on these two datasets, respectively, outperforming other models of similar complexity and size, and achieving state-of-the-art results.

scAAGA: Single cell data analysis framework using asymmetric autoencoder with gene attention

In recent years, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful technique for investigating cellular heterogeneity and structure. However, analyzing scRNA-seq data remains challenging, especially in the context of COVID-19 research. Single-cell clustering is a key step in analyzing scRNA-seq data, and deep learning methods have shown great potential in this area. In this work, we propose a novel scRNA-seq analysis framework called scAAGA. Specifically, we utilize an asymmetric autoencoder with a gene attention module to learn important gene features adaptively from scRNA-seq data, with the aim of improving the clustering effect. We apply scAAGA to COVID-19 peripheral blood mononuclear cell (PBMC) scRNA-seq data and compare its performance with state-of-the-art methods. Our results consistently demonstrate that scAAGA outperforms existing methods in terms of adjusted rand index (ARI), normalized mutual information (NMI), and adjusted mutual information (AMI) scores, achieving improvements ranging from 2.8% to 27.8% in NMI scores. Additionally, we discuss a data augmentation technology to expand the datasets and improve the accuracy of scAAGA. Overall, scAAGA presents a robust tool for scRNA-seq data analysis, enhancing the accuracy and reliability of clustering results in COVID-19 research.

Rm-LR: A long-range-based deep learning model for predicting multiple types of RNA modifications

Recent research has highlighted the pivotal role of RNA post-transcriptional modifications in the regulation of RNA expression and function. Accurate identification of RNA modification sites is important for understanding RNA function. In this study, we propose a novel RNA modification prediction method, namely Rm-LR, which leverages a long-range-based deep learning approach to accurately predict multiple types of RNA modifications using RNA sequences only. Rm-LR incorporates two large-scale RNA language pre-trained models to capture discriminative sequential information and learn local important features, which are subsequently integrated through a bilinear attention network. Rm-LR supports a total of ten RNA modification types (m6A, m1A, m5C, m5U, m6Am, Ψ, Am, Cm, Gm, and Um) and significantly outperforms the state-of-the-art methods in terms of predictive capability on benchmark datasets. Experimental results show the effectiveness and superiority of Rm-LR in prediction of various RNA modifications, demonstrating the strong adaptability and robustness of our proposed model. We demonstrate that RNA language pretrained models enable to learn dense biological sequential representations from large-scale long-range RNA corpus, and meanwhile enhance the interpretability of the models. This work contributes to the development of accurate and reliable computational models for RNA modification prediction, providing insights into the complex landscape of RNA modifications.