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Sex Differences of Cerebellum and Cerebrum: Evidence from Graph Convolutional Network
This study tackles the sex prediction problem from a more comprehensive view, and may provide the resting-state fMRI evidence for further study of sex difference s in the cerebellum and cerebrum.Graphical Abstract (Source: Interdisciplinary Sciences, Computational Life Sciences)
Source: Interdisciplinary Sciences, Computational Life Sciences - February 1, 2022 Category: Bioinformatics Source Type: research

CNNLSTMac4CPred: A Hybrid Model for N4-Acetylcytidine Prediction
AbstractN4-Acetylcytidine (ac4C) is a highly conserved post-transcriptional and an extensively existing RNA modification, playing versatile roles in the cellular processes. Due to the limitation of techniques and knowledge, large-scale identification of ac4C is still a challenging task. RNA sequences are like sentences containing semantics in the natural language. Inspired by the semantics of language, we proposed a hybrid model for ac4C prediction. The model used long short-term memory and convolution neural network to extract the semantic features hidden in the sequences. The semantic and the two traditional features (k-...
Source: Interdisciplinary Sciences, Computational Life Sciences - February 1, 2022 Category: Bioinformatics Source Type: research

Dual Attention Mechanisms and Feature Fusion Networks Based Method for Predicting LncRNA-Disease Associations
AbstractLncRNAs play a part in numerous momentous processes of biology such as disease diagnoses, preventions and treatments. The associations between various diseases and lncRNAs are one of the crucial approaches to learn the role and status of lncRNAs in human diseases. With the researches on lncRNA and diseases, multiple methods based on neural network have been employed to predict these associations. However, the deep and complicated characteristic representations of lncRNA-disease associations were failed to be extracted, and the discriminative contributions of the interactions, correlations, and similarities among mi...
Source: Interdisciplinary Sciences, Computational Life Sciences - January 24, 2022 Category: Bioinformatics Source Type: research

Multiple Protein Subcellular Locations Prediction Based on Deep Convolutional Neural Networks with Self-Attention Mechanism
AbstractAs an important research field in bioinformatics, protein subcellular location prediction is critical to reveal the protein functions and provide insightful information for disease diagnosis and drug development. Predicting protein subcellular locations remains a challenging task due to the difficulty of finding representative features and robust classifiers. Many feature fusion methods have been widely applied to tackle the above issues. However, they still suffer from accuracy loss due to feature redundancy. Furthermore, multiple protein subcellular locations prediction is more complicated since it is fundamental...
Source: Interdisciplinary Sciences, Computational Life Sciences - January 23, 2022 Category: Bioinformatics Source Type: research

Machine Learning Applications in Drug Repurposing
This article provides a strong reasonableness for employing machine learning methods for drug repurposing, including during fighting for COVID-19 pandemic. (Source: Interdisciplinary Sciences, Computational Life Sciences)
Source: Interdisciplinary Sciences, Computational Life Sciences - January 23, 2022 Category: Bioinformatics Source Type: research

Multi-feature Fusion Method Based on Linear Neighborhood Propagation Predict Plant LncRNA –Protein Interactions
In this study, a multi-feature fusion method based on linear neighborhood propagation is develop ed 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 ex plore 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. Mor...
Source: Interdisciplinary Sciences, Computational Life Sciences - January 17, 2022 Category: Bioinformatics Source Type: research

FSCAM: CAM-Based Feature Selection for Clustering scRNA-seq
AbstractCell type determination based on transcriptome profiles is a key application of single-cell RNA sequencing (scRNA-seq). It is usually achieved through unsupervised clustering. Good feature selection is capable of improving the clustering accuracy and is a crucial component of single-cell clustering pipelines. However, most current single-cell feature selection methods are univariable filter methods ignoring gene dependency. Even the multivariable filter methods developed in recent years only consider “one-to-many” relationship between genes. In this paper, a novel single-cell feature selection method based on c...
Source: Interdisciplinary Sciences, Computational Life Sciences - January 14, 2022 Category: Bioinformatics Source Type: research

EnANNDeep: An Ensemble-based lncRNA –protein Interaction Prediction Framework with Adaptive k-Nearest Neighbor Classifier and Deep Models
AbstractlncRNA –protein interactions (LPIs) prediction can deepen the understanding of many important biological processes. Artificial intelligence methods have reported many possible LPIs. However, most computational techniques were evaluated mainly on one dataset, which may produce prediction bias. More import antly, they were validated only under cross validation on lncRNA–protein pairs, and did not consider the performance under cross validations on lncRNAs and proteins, thus fail to search related proteins/lncRNAs for a new lncRNA/protein. Under an ensemble learning framework (EnANNDeep) composed of adaptivek-near...
Source: Interdisciplinary Sciences, Computational Life Sciences - January 10, 2022 Category: Bioinformatics Source Type: research

EnANNDeep: An Ensemble-based lncRNA protein Interaction Prediction Framework with Adaptive k-Nearest Neighbor Classifier and Deep Models
AbstractlncRNA protein interactions (LPIs) prediction can deepen the understanding of many important biological processes. Artificial intelligence methods have reported many possible LPIs. However, most computational techniques were evaluated mainly on one dataset, which may produce prediction bias. More import antly, they were validated only under cross validation on lncRNAprotein pairs, and did not consider the performance under cross validations on lncRNAs and proteins, thus fail to search related proteins/lncRNAs for a new lncRNA/protein. Under an ensemble learning framework (EnANNDeep) composed of adaptivek-nearest ...
Source: Interdisciplinary Sciences, Computational Life Sciences - January 10, 2022 Category: Bioinformatics Source Type: research

Prediction of Potential MicroRNA Disease Association Using Kernelized Bayesian Matrix Factorization
AbstractMicroRNA (miRNA) molecules, which are effective in the formation and progression of many different diseases, are 18 22 nucleotides in length and make up a type of non-coding RNA. Predicting disease-related microRNAs is crucial for understanding the pathogenesis of disease and for diagnosis, treatment, and prevention of diseases. Many computational techniques have been studied and developed, as the experimental techniques used to find novel miRNAdisease associations in biology are costly. In this paper, a Kernelized Bayesian Matrix Factorization (KBMF) technique was suggested to predict new relations among miRNAs ...
Source: Interdisciplinary Sciences, Computational Life Sciences - December 1, 2021 Category: Bioinformatics Source Type: research

RSCMDA: Prediction of Potential miRNA Disease Associations Based on a Robust Similarity Constraint Learning Method
In this study, we proposed a new method based on similarity constrained learning (RSCMDA) to infer disease-associated miRNAs. Considering the problems of noise and incomplete information in current biological datasets, we designed a new framework RSCMDA, which can learn a new disease similarity network and miRNA similarity network based on the existing biological information, and then update the predicted miRNA disease associations using robust similarity constraint learning method. Consequently, the AUC scores obtained in the global and local cross-validation of RSCMDA are 0.9465 and 0.8494, respectively, which are super...
Source: Interdisciplinary Sciences, Computational Life Sciences - December 1, 2021 Category: Bioinformatics Source Type: research

Discovery of Genetic Biomarkers for Alzheimer s Disease Using Adaptive Convolutional Neural Networks Ensemble and Genome-Wide Association Studies
ConclusionThis approach overcomes the limitations associated with the impact of subjective factors and dependence on prior knowledge while adaptively achieving more robust and effective candidate biomarkers in a data-driven way.SignificanceThe approach is promising to facilitate discovering effective candidate genetic biomarkers for brain disorders, as well as to help improve the effectiveness of identified candidate neuroimaging biomarkers for brain diseases. (Source: Interdisciplinary Sciences, Computational Life Sciences)
Source: Interdisciplinary Sciences, Computational Life Sciences - December 1, 2021 Category: Bioinformatics Source Type: research

Correction to: HD5 and LL ?37 Inhibit SARS?CoV and SARS?CoV?2 Binding to Human ACE2 by Molecular Simulation
(Source: Interdisciplinary Sciences, Computational Life Sciences)
Source: Interdisciplinary Sciences, Computational Life Sciences - December 1, 2021 Category: Bioinformatics Source Type: research

SMART v1.0: A Database for Small Molecules with Functional Implications in Plants
AbstractWe developed SMART v1.0 (http://smart.omicstudio.cloud), the first database for small molecules with functional implications in plants. The SMART database is devoted to providing and managing small molecules and their associated structural data, chemoinformatic data, protein targets, pathways and induced phenotype/function information. Currently, SMART v1.0 encompasses 1218 unique small molecules which are involved in multiple biological pathways. SMART v1.0 is featured with user-friendly interfaces, through which pathway-centered visualization of small molecules can be efficiently performed, and multiple types of ...
Source: Interdisciplinary Sciences, Computational Life Sciences - October 14, 2021 Category: Bioinformatics Source Type: research

Anti-cancer Peptide Recognition Based on Grouped Sequence and Spatial Dimension Integrated Networks
AbstractThe diversification of the characteristic sequences of anti-cancer peptides has imposed difficulties on research. To effectively predict new anti-cancer peptides, this paper proposes a more suitable feature grouping sequence and spatial dimension-integrated network algorithm for anti-cancer peptide sequence prediction called GRCI-Net. The main process is as follows: First, we implemented the fusion reduction of binary structure features and K-mer sparse matrix features through principal component analysis and generated a set of new features; second, we constructed a new bidirectional long- and short-term memory net...
Source: Interdisciplinary Sciences, Computational Life Sciences - October 12, 2021 Category: Bioinformatics Source Type: research