Current Bioinformatics - Online First

Long non-coding RNAs (lncRNAs) are a category of more extended RNA strands that lack protein-coding abilities. Although they are not involved in the translation of proteins, studies have shown that they play essential regulatory functions in cells, regulating gene expression and cell biological processes. However, it is both costly and inefficient to determine the associations between lncRNAs and diseases through biological experiments. Therefore, there is an urgent need to develop convenient and fast computational methods to predict lncRNA-disease associations (LDAs) more efficiently.

Predicting disease-associated lncRNAs can help explore the mechanisms of action of lncRNAs in diseases, and this is crucial for early intervention and treatment of diseases.

In this paper, we propose an enhanced heterogeneous graph representation method for predicting LDAs, named GCGALDA. The GCGALDA first obtains the topological structure features of nodes by a biased random walk. Based on this, the neighboring nodes of a node are weighted using the attention mechanism to further mine the semantic association relationships between nodes in the graph data. Then, a graph convolution network (GCN) is used to transfer the neighborhood features of the node to the central node and combine them with the node's features so that the final node representation contains not only structural information but also semantic association information. Finally, the association score between lncRNA and disease is obtained by multilayer perceptron (MLP).

As evidenced by the experimental findings, the GCGALDA outperforms other advanced models in terms of prediction accuracy on openly accessible databases. In addition, case studies on several human diseases further confirm the predictive ability of the GCGALDA.

In conclusion, the proposed GCGALDA model extracts multi-perspective features, such as topology, semantic association, and node attributes, obtains high-quality heterogeneous graph node representations, and effectively improves the performance of the LDA prediction model.

Cancers routinely exhibit chromosomal instability that results in copy number variants (CNVs), namely changes in the abundance of genomic material. Unfortunately, the detection of these variants in cancer genomes is difficult.

We present Ploidetect, a software package that effectively identifies CNVs within whole-genome sequenced tumors. Ploidetect utilizes a coarse-to-fine segmentation approach which yields highly contiguous segments while allowing for focal CNVs to be detected with high sensitivity.

We benchmark Ploidetect against popular CNV tools using synthetic data, cell line data, and real-world metastatic tumor data and demonstrate strong performance in all tests. We show that high quality CNVs from Ploidetect enable the identification of recurrent homozygous deletions and genes associated with chromosomal instability in a multi-cancer cohort of 687 patients. Using highly contiguous CNV calls afforded by Ploidetect, we also demonstrate the use of segment N50 as a novel metric for the measurement of chromosomal instability within tumor biopsies.

We propose that the increasingly accurate determination of CNVs is critical for their productive study in cancer, and our work demonstrates advances made possible by progress in this regard.

The root system plays an irreplaceable role in plant growth. Its improvement can increase crop productivity. However, such a system is still mysterious for us. The underlying mechanism has not been fully uncovered. The investigation on proteins related to the root system is an important means to complete this task. In the previous time, lack of root-related proteins makes it impossible to adopt machine learning methods for designing efficient models for the discovery of novel root-related proteins. Recently, a public database on root-related proteins was set up and machine learning methods can be applied in this field.

The purpose of this study was to design an efficient computational method to predict root-associated proteins in three plants: maize, sorghum, and soybean.

In this study, we proposed a machine learning based model, named Graph-Root, for the identification of root-related proteins in maize, sorghum, and soybean. The features derived from protein sequences, functional domains, and one network were extracted, where the first type of features were processed by graph convolutional neural network and multi-head attention, the second type of features reflected the essential functions of proteins, and the third type of features abstracted the linkage between proteins. These features were fed into the fully connected layer to make predictions.

The 5-fold cross-validation and independent tests suggested its acceptable performance. It also outperformed the only previous model, SVM-Root. Furthermore, the importance of each feature type and component in the proposed model was investigated.

Graph-Root had a good performance and can be a useful tool to identify novel root-related proteins. BLOSUM62 features were found to be important in determining root-related proteins.

Breast Cancer (BC) is a significant cause of high mortality amongst women globally and probably will remain a disease posing challenges about its detectability. Advancements in medical imaging technology have improved the accuracy and efficiency of breast cancer classification. However, tumor features' complexity and imaging data variability still pose challenges.

This study proposes the Ensemble Residual-VGG-16 model as a novel combination of the Deep Residual Network (DRN) and VGG-16 architecture. This model is purposely engineered with maximal precision for the task of breast cancer diagnosis based on mammography images. We assessed its performance by accuracy, recall, precision, and the F1-Score. All these metrics indicated the high performance of this Residual-VGG-16 model. The diagnostic residual-VGG16 performed exceptionally well with an accuracy of 99.6%, precision of 99.4%, recall of 99.7%, F1 score of 98.6%, and Mean Intersection over Union (MIoU) of 99.8% with MIAS datasets.

Similarly, the INBreast dataset achieved an accuracy of 93.8%, a precision of 94.2%, a recall of 94.5%, and an F1-score of 93.4%.

The proposed model is a significant advancement in breast cancer diagnosis, with high accuracy and potential as an automated grading.

Biopanning, or phage display technology, has gained considerable research attention for discovering peptides, and antibodies, and understanding protein interactions, which are crucial for developing targeted therapeutics. The Biopanning Data Bank (BDB, http://i.uestc.edu.cn/bdb) serves as a repository for peptide biopanning results. However, its last significant update was in 2018, highlighting a research gap that needs urgent attention.

This study aims to update BDB with the most recent data and enhance the identification of Target-Unrelated Peptides (TUPs).

A search of PubMed was conducted for recent articles related to “phage display” published between January 2018 and May 2023. Relevant data were manually curated and added to BDB. Each peptide’s target was identified using MimoSearch, while TUPScan was used to detect new TUPs.

As of October 2023, BDB contains 3,682 biopanning datasets from 1,771 papers. These datasets included 124 NGPD datasets and 3,558 conventional biopanning datasets, featuring 34,078 peptide sequences, 593 templates, 2,231 targets, 524 peptide libraries, and 324 crystal structures. Our analysis identified 1,110 possible TUPs and 60 highly reliable TUPs, including 26 novel discoveries.

This update addresses critical research gaps by incorporating recent peptide data and introducing novel TUPs. BDB remains the most comprehensive resource for biopanning, playing a crucial role in peptide library research and supporting the development of new TUP predictors and mimotope decoding tools.

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.