FREE: Enhanced Feature Representation for Isotopic Envelope Evaluation in Top-Down Mass Spectra Deconvolution.

The aim of deconvolution of top-down mass spectra is to recognize monoisotopic peaks from the experimental envelopes in raw mass spectra. So accurate assessment of similarity between theoretical and experimental envelopes is a critical step in mass spectra data deconvolution. Existing evaluation methods primarily rely on intensity differences and / similarity, potentially lacking a comprehensive assessment.

Group-IIIA element doped BaSnS as a high efficiency absorber for intermediate band solar cell from a first-principles insight.

The quest for high-performance solar cell absorbers has garnered significant attention in the field of photovoltaic research in recent years. To overcome the Shockley-Queisser (SQ) limit of ∼31% for single junction solar cell and realize higher power conversion efficiency, the concept of an intermediate band solar cell (IBSC) has been proposed. This involves the incorporation of an intermediate band (IB) to assist the three band-edge absorptions within the single absorber layer.

Diagnosis of Autism Spectrum Disorder (ASD) Using Recursive Feature Elimination-Graph Neural Network (RFE-GNN) and Phenotypic Feature Extractor (PFE).

Autism spectrum disorder (ASD) poses as a multifaceted neurodevelopmental condition, significantly impacting children's social, behavioral, and communicative capacities. Despite extensive research, the precise etiological origins of ASD remain elusive, with observable connections to brain activity. In this study, we propose a novel framework for ASD detection, extracting the characteristics of functional magnetic resonance imaging (fMRI) data and phenotypic data, respectively.

DAHNGC: A Graph Convolution Model for Drug-Disease Association Prediction by Using Heterogeneous Network.

In the field of drug development and repositioning, the prediction of drug-disease associations is a critical task. A recently proposed method for predicting drug-disease associations based on graph convolution relies heavily on the features of adjacent nodes within the homogeneous network for characterizing information. However, this method lacks node attribute information from heterogeneous networks, which could hardly provide valuable insights for predicting drug-disease associations.

DNRLCNN: A CNN Framework for Identifying MiRNA-Disease Associations Using Latent Feature Matrix Extraction with Positive Samples.

Emerging evidence indicates that miRNAs have strong relationships with many human diseases. Investigating the associations will contribute to elucidating the activities of miRNAs and pathogenesis mechanisms, and providing new opportunities for disease diagnosis and drug discovery. Therefore, it is of significance to identify potential associations between miRNAs and diseases.

A novel essential protein identification method based on PPI networks and gene expression data.

Background: Some proposed methods for identifying essential proteins have better results by using biological information. Gene expression data is generally used to identify essential proteins. However, gene expression data is prone to fluctuations, which may affect the accuracy of essential protein identification.

iCDA-CMG: identifying circRNA-disease associations by federating multi-similarity fusion and collective matrix completion.

Circular RNAs (circRNAs) are a special class of non-coding RNAs with covalently closed-loop structures. Studies prove that circRNAs perform critical roles in various biological processes, and the aberrant expression of circRNAs is closely related to tumorigenesis. Therefore, identifying potential circRNA-disease associations is beneficial to understand the pathogenesis of complex diseases at the circRNA level and helps biomedical researchers and practitioners to discover diagnostic biomarkers accurately.

An in-silico method with graph-based multi-label learning for large-scale prediction of circRNA-disease associations.

Circular RNAs (circRNAs) have been proved to be implicated in various pathological processes and play vital roles in tumors. Increasing evidence has shown that circRNAs can serve as an important class of regulators, which have great potential to become a new type of biomarkers for tumor diagnosis and treatment. However, their biological functions remain largely unknown, and it is costly and tremendously laborious to investigate the molecular mechanisms of circRNAs in human diseases based on conventional wet-lab experiments.

Proteoform characterization based on top-down mass spectrometry.

Proteins are dominant executors of living processes. Compared to genetic variations, changes in the molecular structure and state of a protein (i.e.

Network Embedding the Protein-Protein Interaction Network for Human Essential Genes Identification.

Essential genes are a group of genes that are indispensable for cell survival and cell fertility. Studying human essential genes helps scientists reveal the underlying biological mechanisms of a human cell but also guides disease treatment. Recently, the publication of human essential gene data makes it possible for researchers to train a machine-learning classifier by using some features of the known human essential genes and to use the classifier to predict new human essential genes.

XGBFEMF: An XGBoost-Based Framework for Essential Protein Prediction.

Essential proteins as a vital part of maintaining the cells' life play an important role in the study of biology and drug design. With the generation of large amounts of biological data related to essential proteins, an increasing number of computational methods have been proposed. Different from the methods which adopt a single machine learning method or an ensemble machine learning method, this paper proposes a predicting framework named by XGBFEMF for identifying essential proteins, which includes a SUB-EXPAND-SHRINK method for constructing the composite features with original features and obtaining the better subset of features for essential protein prediction, and also includes a model fusion method for getting a more effective prediction model.

A novel method of predicting microRNA-disease associations based on microRNA, disease, gene and environment factor networks.

MicroRNAs have been reported to have close relationship with diseases due to their deregulation of the expression of target mRNAs. Detecting disease-related microRNAs is helpful for disease therapies. With the development of high throughput experimental techniques, a large number of microRNAs have been sequenced.

Protein Inference from the Integration of Tandem MS Data and Interactome Networks.

Since proteins are digested into a mixture of peptides in the preprocessing step of tandem mass spectrometry (MS), it is difficult to determine which specific protein a shared peptide belongs to. In recent studies, besides tandem MS data and peptide identification information, some other information is exploited to infer proteins. Different from the methods which first use only tandem MS data to infer proteins and then use network information to refine them, this study proposes a protein inference method named TMSIN, which uses interactome networks directly.

Sprites: detection of deletions from sequencing data by re-aligning split reads.

Motivation: Advances of next generation sequencing technologies and availability of short read data enable the detection of structural variations (SVs). Deletions, an important type of SVs, have been suggested in association with genetic diseases. There are three types of deletions: blunt deletions, deletions with microhomologies and deletions with microsinsertions.

ClusterViz: A Cytoscape APP for Cluster Analysis of Biological Network.

Cluster analysis of biological networks is one of the most important approaches for identifying functional modules and predicting protein functions. Furthermore, visualization of clustering results is crucial to uncover the structure of biological networks. In this paper, ClusterViz, an APP of Cytoscape 3 for cluster analysis and visualization, has been developed.

Predicting protein functions by using unbalanced bi-random walk algorithm on protein-protein interaction network and functional interrelationship network.

Accurate annotation of protein functions is still a big challenge for understanding life in the post-genomic era. Recently, some methods have been developed to solve the problem by incorporating functional similarity of GO terms into protein-protein interaction (PPI) network, which are based on the observation that a protein tends to share some common functions with proteins that interact with it in PPI network, and two similar GO terms in functional interrelationship network usually co-annotate some common proteins. However, these methods annotate functions of proteins by considering at the same level neighbors of proteins and GO terms respectively, and few attempts have been made to investigate their difference.

Predicting Essential Proteins Based on Weighted Degree Centrality.

Essential proteins are vital for an organism's viability under a variety of conditions. There are many experimental and computational methods developed to identify essential proteins. Computational prediction of essential proteins based on the global protein-protein interaction (PPI) network is severely restricted because of the insufficiency of the PPI data, but fortunately the gene expression profiles help to make up the deficiency.

Prediction of essential proteins based on gene expression programming.

Background: Essential proteins are indispensable for cell survive. Identifying essential proteins is very important for improving our understanding the way of a cell working. There are various types of features related to the essentiality of proteins.