Current Bioinformatics - Online First

Since each dimension of a tensor can store different types of genomics data, compared to matrix methods, utilizing tensor structure can provide a deeper understanding of multi-dimensional data while also facilitating the discovery of more useful information related to cancer. However, in reality, there are issues such as insufficient utilization of prior knowledge in multi-omics data and limitations in the recovery of low-tubal-rank tensors. Therefore, the method proposed in this article was developed.

e: In this paper, we proposed a low transformed tubal rank tensor model (LTTRT) using a spatial-tubal constraint to accurately partition different types of cancer samples and provide reliable theoretical support for the identification, diagnosis, and treatment of cancer.

In the LTTRT method, the transformed tensor nuclear norm based on the transformed tensor singular value decomposition is characterized by the low-rank tensor, which can explore the global low-rank property of the tensor, resolving the challenge of the tensor nuclear norm-based method not achieving the lowest tubal rank. Additionally, the introduction of weighted total variation regularization is conducive to extracting more information from sequencing data in both spatial and tubal dimensions, exploring cross-correlation features of multiple genomic data, and addressing the problem of overlooking prior knowledge from various perspectives. In addition, the L1-norm is used to improve sparsity. A symmetric Gauss‒Seidel-based alternating direction method of multipliers (sGS-ADMM) is used to update the LTTRT model iteratively.

The experiments of sample clustering on multiple integrated cancer multi-omics datasets show that the proposed LTTRT method is better than existing methods. Experimental results validate the effectiveness of LTTRT in accurately partitioning different types of cancer samples.

The LTTRT method achieves precise segmentation of different types of cancer samples.

One of the main causes of cancer-related mortality in women is breast cancer [BC]. There were four molecular subtypes of this malignancy, and adjuvant therapy efficacy differed based on these subtypes. Gene expression profiles provide valuable information that is helpful for patients whose prognosis is not clear from clinical markers and immunohistochemistry.

In this study, we aim to predict molecular types of BC using a gene expression dataset of patients with BC and normal samples using six well-known ensemble machine-learning techniques.

Two microarray datasets were downloaded; [GSE45827] and [GSE140494] from the Gene Expression Omnibus [GEO] database. These datasets comprise 21 samples of normal tissues that were part of a cohort analysis of primary invasive breast cancer [57 basal, 36 HER2, 56 Luminal A, and 66 Luminal B]. Namely, we used AdaBoost, Random Forest [RF], Artificial Neural Network [ANN], Naïve Bayes [NB], Classification and Regression Tree [CART], and Linear Discriminant Analysis [LDA] classifiers.

The results of the data analysis show that the RF and NB classifiers outperform the other models in the prediction of the BC subtype. The RF shows superior performance with an accuracy range between 0.89 and 1.0 in contrast to its competitor NB, which has an average accuracy of 0.91. Our approach perfectly discriminates un-affected cases [normal] from the carcinoma. In this case, the RF provides perfect prediction with zero errors. Additionally, we used PCA, DHWT low-frequency, and DHWT high-frequency to perform a dimensional reduction for the numerous gene expression values. Consequently, the LDA achieves up to 95% improvement in performance through data reduction. Moreover, feature selection allowed for the best performance, which is recorded by the RF with classification accuracy 98%.

Overall, we provide a successful framework that leads to shorter computation times and smaller ML models, especially where memory and time restrictions are crucial.

Cleft lip and palate are two of the most common craniofacial congenital malformations in humans. It influences tens of millions of patients worldwide. The hazards of this disease are multifaceted, extending beyond the obvious facial malformation to encompass physiological functions, oral health, psychological well-being, and social aspects.

The primary objective of our study is to demonstrate the importance of imaging in detecting cleft lip and palate. By observing the morphological and structural abnormalities involving the lip and palate through imaging methods, this study aims to establish imaging as the primary diagnostic approach for this disease.

In this work, we proposed a novel model to analyze unilateral complete cleft lip and palate after velopharyngeal closure and non-left lip and palate patients from the Department of Stomatology of Xuzhou First People's Hospital, Conical Beam CT (CBCT) images in silicon. In order to demonstrate the generalization, the simulated dataset was constructed using the random disturbance factor, which is from the actual dataset. We extracted several raw features from CBCT images in detail. Then, we proposed a novel feature reconstruction method, including six types of reconstructed factors, to reconstruct the existing features. Then, the reconstructed features weretrained with machine learning algorithms. Finally, the testing and independent data model was utilized to analyze the performance of this work.

By comparing different operator features, the min operator, max operator, average operator, and all operators can achieve good performances in both the testing set and the independent set.

With the different operator features, the majority of classification models, including Gradient Boosting, Hist Gradient Boosting, Multilayer Perceptron, lightGBM, and broadened learning, classification algorithms can get the well-performances in the selected reconstructed feature operators.