Linear Neighborhood Research Articles

Subclass of Analytic Functions Defined by Miller-Ross-Type Poisson Distribution Series

It is very well-known that the special functions and integral operators play a vital role in the research of applied and mathematical sciences. The main purpose of this paper is to introduce a new subclass of analytic functions involving Miller-Ross functions and obtain coefficient inequalities, distortion theorem, convex linear combination, partial sums, convolution, and neighborhood results for this class.

A vector representation for phylogenetic trees.

Good representations for phylogenetic trees and networks are important for enhancing storage efficiency and scalability for the inference and analysis of evolutionary trees for genes, genomes and species. We propose a new representation for rooted phylogenetic trees that encodes a tree on [Formula: see text] ordered taxa as a vector of length [Formula: see text] in which each taxon appears exactly twice. Using this new tree representation, we introduce a novel tree rearrangement operator, termed an HOP, that results in a tree space of linear diameter and quadratic neighbourhood size. We also introduce a novel metric, the HOP distance, which is the minimum number of HOPs to transform a tree into another tree. The HOP distance can be computed in near-linear time-a rare instance of tree rearrangement distance that is tractable. Our experiments show that the HOP distance is better correlated to the Subtree-Prune-and-Regraft distance than the widely used Robinson-Foulds distance. We also describe how the proposed tree representation can be further generalized to tree-child networks, showcasing its versatility and potential applications in broader evolutionary analyses.This article is part of the theme issue '"A mathematical theory of evolution": phylogenetic models dating back 100 years'.

NFFGRAM: Nonlinear Multi-Feature Fusion and Gated Recurrent Self-Attention Mechanism for Traditional Chinese Medicine Formula Recommendation.

Traditional Chinese Medicine (TCM) prescriptions are derived from the distinctive thought process and clinical experiences of Chinese medical theory. With the advent of artificial intelligence (AI), there is an enhanced ability to formulate these prescriptions by analyzing symptom data. However, the inherent sparseness of herb-symptom association data still limits the efficacy of such predictive methods. This study introduces an enhanced bipartite graph diffusion algorithm coupled with a gated recurrent self-attention mechanism for predicting herb and symptom associations. The initial phase involves the reconstruction of the herb-symptom association matrix, leveraging the fractal-weighted K-nearest neighbor algorithm. Subsequently, a method is conceived to extract analogous features between herbs and symptoms, which integrates linear neighborhood similarity with Gaussian kernel similarity, both based on fractal dimensions. The next stage employs a modified bipartite graph diffusion to deduce underlying herb-symptom relationships. This process culminates with the integration of the gated recurrent self-attention mechanism and a confidence scoring system to refine the herb-symptom association predictive matrix at a granular level. We benchmark our results against leading-edge algorithms to ascertain the precision and reliability of our model. Such as improvements of precision@20 by 21.77%, recall@20 by 12.46%, and F1-score@20 by 19.28% compared with the best baseline for the TCM2 dataset. Additionally, comprehensive case studies are undertaken, evaluating recommended prescriptions using insights from contemporary medicine and network pharmacology. The proposed model provides a novel paradigm for enhancing herbal prescription methodologies and TCM herb-based treatments.

Enhancing wind power generation prediction using relevance assessment-based transfer learning

Enhancing wind power generation prediction using relevance assessment-based transfer learning

Predicting Microbe-Disease Associations Based on a Linear Neighborhood Label Propagation Method with Multi-order Similarity Fusion Learning.

Computational approaches employed for predicting potential microbe-disease associations often rely on similarity information between microbes and diseases. Therefore, it is important to obtain reliable similarity information by integrating multiple types of similarity information. However, existing similarity fusion methods do not consider multi-order fusion of similarity networks. To address this problem, a novel method of linear neighborhood label propagation with multi-order similarity fusion learning (MOSFL-LNP) is proposed to predict potential microbe-disease associations. Multi-order fusion learning comprises two parts: low-order global learning and high-order feature learning. Low-order global learning is used to obtain common latent features from multiple similarity sources. High-order feature learning relies on the interactions between neighboring nodes to identify high-order similarities and learn deeper interactive network structures. Coefficients are assigned to different high-order feature learning modules to balance the similarities learned from different orders and enhance the robustness of the fusion network. Overall, by combining low-order global learning with high-order feature learning, multi-order fusion learning can capture both the shared and unique features of different similarity networks, leading to more accurate predictions of microbe-disease associations. In comparison to six other advanced methods, MOSFL-LNP exhibits superior prediction performance in the leave-one-out cross-validation and 5-fold validation frameworks. In the case study, the predicted 10 microbes associated with asthma and type 1 diabetes have an accuracy rate of up to 90% and 100%, respectively.

Exploring gene-patient association to identify personalized cancer driver genes by linear neighborhood propagation.

Driver genes play a vital role in the development of cancer. Identifying driver genes is critical for diagnosing and understanding cancer. However, challenges remain in identifying personalized driver genes due to tumor heterogeneity of cancer. Although many computational methods have been developed to solve this problem, few efforts have been undertaken to explore gene-patient associations to identify personalized driver genes. Here we propose a method called LPDriver to identify personalized cancer driver genes by employing linear neighborhood propagation model on individual genetic data. LPDriver builds personalized gene network based on the genetic data of individual patients, extracts the gene-patient associations from the bipartite graph of the personalized gene network and utilizes a linear neighborhood propagation model to mine gene-patient associations to detect personalized driver genes. The experimental results demonstrate that as compared to the existing methods, our method shows competitive performance and can predict cancer driver genes in a more accurate way. Furthermore, these results also show that besides revealing novel driver genes that have been reported to be related with cancer, LPDriver is also able to identify personalized cancer driver genes for individual patients by their network characteristics even if the mutation data of genes are hidden. LPDriver can provide an effective approach to predict personalized cancer driver genes, which could promote the diagnosis and treatment of cancer. The source code and data are freely available at https://github.com/hyr0771/LPDriver .

Assessment of centralised and localised ice cream supply chains using neighbourhood flow configuration models

Assessment of centralised and localised ice cream supply chains using neighbourhood flow configuration models

MSGCA: Drug-Disease Associations Prediction Based on Multi-Similarities Graph Convolutional Autoencoder.

Identifying drug-disease associations (DDAs) is critical to the development of drugs. Traditional methods to determine DDAs are expensive and inefficient. Therefore, it is imperative to develop more accurate and effective methods for DDAs prediction. Most current DDAs prediction methods utilize original DDAs matrix directly. However, the original DDAs matrix is sparse, which greatly affects the prediction consequences. Hence, a prediction method based on multi-similarities graph convolutional autoencoder (MSGCA) is proposed for DDAs prediction. First, MSGCA integrates multiple drug similarities and disease similarities using centered kernel alignment-based multiple kernel learning (CKA-MKL) algorithm to form new drug similarity and disease similarity, respectively. Second, the new drug and disease similarities are improved by linear neighborhood, and the DDAs matrix is reconstructed by weighted K nearest neighbor profiles. Next, the reconstructed DDAs and the improved drug and disease similarities are integrated into a heterogeneous network. Finally, the graph convolutional autoencoder with attention mechanism is utilized to predict DDAs. Compared with extant methods, MSGCA shows superior results on three datasets. Furthermore, case studies further demonstrate the reliability of MSGCA.

MSIF-LNP: microbial and human health association prediction based on matrix factorization noise reduction for similarity fusion and bidirectional linear neighborhood label propagation.

Studies have shown that microbes are closely related to human health. Clarifying the relationship between microbes and diseases that cause health problems can provide new solutions for the treatment, diagnosis, and prevention of diseases, and provide strong protection for human health. Currently, more and more similarity fusion methods are available to predict potential microbe-disease associations. However, existing methods have noise problems in the process of similarity fusion. To address this issue, we propose a method called MSIF-LNP that can efficiently and accurately identify potential connections between microbes and diseases, and thus clarify the relationship between microbes and human health. This method is based on matrix factorization denoising similarity fusion (MSIF) and bidirectional linear neighborhood propagation (LNP) techniques. First, we use non-linear iterative fusion to obtain a similarity network for microbes and diseases by fusing the initial microbe and disease similarities, and then reduce noise by using matrix factorization. Next, we use the initial microbe-disease association pairs as label information to perform linear neighborhood label propagation on the denoised similarity network of microbes and diseases. This enables us to obtain a score matrix for predicting microbe-disease relationships. We evaluate the predictive performance of MSIF-LNP and seven other advanced methods through 10-fold cross-validation, and the experimental results show that MSIF-LNP outperformed the other seven methods in terms of AUC. In addition, the analysis of Cystic fibrosis and Obesity cases further demonstrate the predictive ability of this method in practical applications.

NetPro: Neighborhood Interaction-based Drug Repositioning via Label Propagation.

Drug repositioning is an important approach for predicting new disease indications of the existing drugs in drug discovery. A great progress has been achieved in drug repositioning. However, effectively utilizing the localized neighborhood interaction features of drug and disease in drug-disease associations remains challenging. This paper proposes a neighborhood interaction-based method called NetPro for drug repositioning via label propagation. In NetPro, we first formulate the known drug-disease associations, various disease and drug similarities from different perspectives to construct drug-drug and disease-disease networks. Meanwhile we employ the nearest neighbors and their interactions in the constructed networks to devise a new approach for computing drug similarity and disease similarity. To implement the prediction of new drugs or diseases, a preprocessing step is applied to renew the known drug-disease associations using our calculated drug and disease similarities. We then employ a label propagation model to predict drug-disease associations by the drug and disease linear neighborhood similarities derived from the renewed drug-disease associations. The experimental results on three benchmark datasets show that NetPro can effectively identify potential drug-disease associations and achieve better prediction performance than the existing methods. Case studies further demonstrate that NetPro is capable of predicting promising candidate disease indications for drugs.

Brain Tumor Classification using SLIC Segmentation with Superpixel Fusion, GoogleNet, and Linear Neighborhood Semantic Segmentation

Brain tumor is an abnormal tissue mass resultant of uncontrolled growth of cells. Brain tumors often reduce life expectancy and cause death in the later stages. Automatic detection of brain tumors is a challenging and important task in computer-aided disease diagnosis systems. This paper presents a deep learning-based approach to the classification of brain tumors. The noise in the brain MRI image is removed using Edge Directional Total Variation Denoising. The brain MRI image is segmented using SLIC segmentation with superpixel fusion. The segments are given to a trained GoogleNet model, which identifies the tumor parts in the image. Once the tumor is identified, a Convolution Neural Network (CNN) based modified semantic segmentation model is used to classify the pixels along the edges of the tumor segments. The modified sematic segmentation uses a linear neighborhood of the pixel for better classification. The final tumor identified is accurate as pixels at the border are classified precisely. The experimental results show that the proposed method has produced an accuracy of 97.3% with GoogleNet classification model, and the linear neighborhood semantic segmentation has delivered an accuracy of 98%.

Predicting lncRNA-disease associations based on combining selective similarity matrix fusion and bidirectional linear neighborhood label propagation.

Recent studies have revealed that long noncoding RNAs (lncRNAs) are closely linked to several human diseases, providing new opportunities for their use in detection and therapy. Many graph propagation and similarity fusion approaches can be used for predicting potential lncRNA-disease associations. However, existing similarity fusion approaches suffer from noise and self-similarity loss in the fusion process. To address these problems, a new prediction approach, termed SSMF-BLNP, based on organically combining selective similarity matrix fusion (SSMF) and bidirectional linear neighborhood label propagation (BLNP), is proposed in this paper to predict lncRNA-disease associations. In SSMF, self-similarity networks of lncRNAs and diseases are obtained by selective preprocessing and nonlinear iterative fusion. The fusion process assigns weights to each initial similarity network and introduces a unit matrix that can reduce noise and compensate for the loss of self-similarity. In BLNP, the initial lncRNA-disease associations are employed in both lncRNA and disease directions as label information for linear neighborhood label propagation. The propagation was then performed on the self-similarity network obtained from SSMF to derive the scoring matrix for predicting the relationships between lncRNAs and diseases. Experimental results showed that SSMF-BLNP performed better than seven other state of-the-art approaches. Furthermore, a case study demonstrated up to 100% and 80% accuracy in 10 lncRNAs associated with hepatocellular carcinoma and 10 lncRNAs associated with renal cell carcinoma, respectively. The source code and datasets used in this paper are available at: https://github.com/RuiBingo/SSMF-BLNP.

Generalizing Aggregation Functions in GNNs: Building High Capacity and Robust GNNs via Nonlinear Aggregation.

The main aspect powering GNNs is the multi-layer network architecture to learn the nonlinear representation for graph learning task. The core operation in GNNs is the message propagation in which each node updates its information by aggregating the information from its neighbors. Existing GNNs usually adopt either linear neighborhood aggregation (e.g. mean, sum) or max aggregator in their message propagation. 1) For linear aggregators, the whole nonlinearity and network's capacity of GNNs are generally limited because deeper GNNs usually suffer from the over-smoothing issue due to their inherent information propagation mechanism. Also, linear aggregators are usually vulnerable to the spatial perturbations. 2) For max aggregator, it usually fails to be aware of the detailed information of node representations within neighborhood. To overcome these issues, we re-think the message propagation mechanism in GNNs and develop the new general nonlinear aggregators for neighborhood information aggregation in GNNs. One main aspect of our nonlinear aggregators is that they all provide the optimally balanced aggregator between max and mean/sum aggregators. Thus, they can inherit both i) high nonlinearity that enhances network's capacity, robustness and ii) detail-sensitivity that is aware of the detailed information of node representations in GNNs' message propagation. Promising experiments show the effectiveness, high capacity and robustness of the proposed methods.

BMPMDA: Prediction of MiRNA-Disease Associations Using a Space Projection Model Based on Block Matrix.

With the high-quality development of bioinformatics technology, miRNA-disease associations (MDAs) are gradually being uncovered. At present, convenient and efficient prediction methods, which solve the problem of resource-consuming in traditional wet experiments, need to be further put forward. In this study, a space projection model based on block matrix is presented for predicting MDAs (BMPMDA). Specifically, two block matrices are first composed of the known association matrix and similarity to increase comprehensiveness. For the integrity of information in the heterogeneous network, matrix completion (MC) is utilized to mine potential MDAs. Considering the neighborhood information of data points, linear neighborhood similarity (LNS) is regarded as a measure of similarity. Next, LNS is projected onto the corresponding completed association matrix to derive the projection score. Finally, the AUC and AUPR values for BMPMDA reach 0.9691 and 0.6231, respectively. Additionally, the majority of novel MDAs in three disease cases are identified in existing databases and literature. It suggests that BMPMDA can serve as a reliable prediction model for biological research.

Mathematical programming formulations and metaheuristics for biological sample transportation problems in healthcare

Mathematical programming formulations and metaheuristics for biological sample transportation problems in healthcare

Anomaly Detection For Time Series Data Based on Multi-granularity Neighbor Residual Network

Anomaly Detection For Time Series Data Based on Multi-granularity Neighbor Residual Network

Improving high frequency image features of deep learning reconstructions via k-space refinement with null-space kernel.

Deep learning (DL) based reconstruction using unrolled neural networks has shown great potential in accelerating MRI. However, one of the major drawbacks is the loss of high-frequency details and textures in the output. The purpose of the study is to propose a novel refinement method that uses null-space kernel to refine k-space and improve blurred image details and textures. The proposed method constrains the output of the DL to comply to the linear neighborhood relationship calibrated in the auto-calibration lines. To demonstrate efficacy, we tested our refinement method on the DL reconstruction under a variety of conditions (i.e., dataset, unrolled neural networks, and under-sampling scheme). Specifically, the method was tested on three large-scale public datasets (knee and brain) from fastMRI's multi-coil track. The proposed scheme visually reduces the structural error in the k-space domain, enhance the homogeneity of the k-space intensity. Consequently, reconstructed image shows sharper images with enhanced details and textures. The proposed method is also successful in improving high-frequency image details (SSIM, GMSD) without sacrificing overall image error (PSNR). Our findings imply that refining DL output using the proposed method may generally improve DL reconstruction as tested with various large-scale dataset and networks.

MILNP: Plant lncRNA-miRNA Interaction Prediction Based on Improved Linear Neighborhood Similarity and Label Propagation.

Knowledge of the interactions between long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) is the basis of understanding various biological activities and designing new drugs. Previous computational methods for predicting lncRNA–miRNA interactions lacked for plants, and they suffer from various limitations that affect the prediction accuracy and their applicability. Research on plant lncRNA–miRNA interactions is still in its infancy. In this paper, we propose an accurate predictor, MILNP, for predicting plant lncRNA–miRNA interactions based on improved linear neighborhood similarity measurement and linear neighborhood propagation algorithm. Specifically, we propose a novel similarity measure based on linear neighborhood similarity from multiple similarity profiles of lncRNAs and miRNAs and derive more precise neighborhood ranges so as to escape the limits of the existing methods. We then simultaneously update the lncRNA–miRNA interactions predicted from both similarity matrices based on label propagation. We comprehensively evaluate MILNP on the latest plant lncRNA-miRNA interaction benchmark datasets. The results demonstrate the superior performance of MILNP than the most up-to-date methods. What’s more, MILNP can be leveraged for isolated plant lncRNAs (or miRNAs). Case studies suggest that MILNP can identify novel plant lncRNA–miRNA interactions, which are confirmed by classical tools. The implementation is available on https://github.com/HerSwain/gra/tree/MILNP.

An Iterative Method for Predicting Essential Proteins Based on Multifeature Fusion and Linear Neighborhood Similarity.

Growing evidence have demonstrated that many biological processes are inseparable from the participation of key proteins. In this paper, a novel iterative method called linear neighborhood similarity-based protein multifeatures fusion (LNSPF) is proposed to identify potential key proteins based on multifeature fusion. In LNSPF, an original protein-protein interaction (PPI) network will be constructed first based on known protein-protein interaction data downloaded from benchmark databases, based on which, topological features will be further extracted. Next, gene expression data of proteins will be adopted to transfer the original PPI network to a weighted PPI network based on the linear neighborhood similarity. After that, subcellular localization and homologous information of proteins will be integrated to extract functional features for proteins, and based on both functional and topological features obtained above. And then, an iterative method will be designed and carried out to predict potential key proteins. At last, for evaluating the predictive performance of LNSPF, extensive experiments have been done, and compare results between LNPSF and 15 state-of-the-art competitive methods have demonstrated that LNSPF can achieve satisfactory recognition accuracy, which is markedly better than that achieved by each competing method.

Multi-feature Fusion Method Based on Linear Neighborhood Propagation Predict Plant LncRNA-Protein Interactions.

Long non-coding RNAs (lncRNAs) have attracted extensive attention due to their important roles in various biological processes, among which lncRNA-protein interaction plays an important regulatory role in plant immunity and life activities. Laboratory methods are time consuming and labor-intensive, so that many computational methods have gradually emerged as auxiliary tools to assist relevant research. However, there are relatively few methods to predict lncRNA-protein interaction of plant. Due to the lack of experimentally verified interactions data, there is an imbalance between known and unknown interaction samples in plant data sets. In this study, a multi-feature fusion method based on linear neighborhood propagation is developed to predict plant unobserved lncRNA-protein interaction pairs through known interaction pairs, called MPLPLNP. The linear neighborhood similarity of the feature space is calculated and the results are predicted by label propagation. Meanwhile, multiple feature training is integrated to better explore the potential interaction information in the data. The experimental results show that the proposed multi-feature fusion method can improve the performance of the model, and is superior to other state-of-the-art approaches. Moreover, the proposed approach has better performance and generalization ability on various plant datasets, which is expected to facilitate the related research of plant molecular biology.