S-nitrosylation (SNO) is one of the most universal reversible post-translational modifications involved in many biological processes. Malfunction or dysregulation SNO leads to a series severe diseases, such as developmental abnormalities and various diseases. Therefore, identification sites (SNOs) provides insights into disease progression drug development. In this paper, new bioinformatics tool, named PSNO, proposed identify SNOs from protein sequences. Firstly, we explore promising...

Protein-protein interactions (PPIs) govern cellular pathways and processes, by significantly influencing the functional expression of proteins. Therefore, accurate identification protein-protein interaction binding sites has become a key step in analysis However, since most computational methods are designed based on biological features, there no available protein language models to directly encode amino acid sequences into distributed vector representations model their characteristics for...

Abstract Circular RNAs (circRNAs) are widely expressed in eukaryotes. The genome-wide interactions between circRNAs and RNA-binding proteins (RBPs) can be probed from cross-linking immunoprecipitation with sequencing data. Therefore, computational methods have been developed for identifying RBP binding sites on circRNAs. Unfortunately, those often suffer the low discriminative power of feature representations, numerical instability poor scalability. To address limitations, we propose a novel...

As one of the most widespread protein post-translational modifications, phosphorylation is involved in many biological processes such as cell cycle, apoptosis. Identification phosphorylated substrates and their corresponding sites will facilitate understanding molecular mechanism phosphorylation. Comparing with labor-intensive time-consuming experiment approaches, computational prediction much desirable due to convenience fast speed. In this paper, a new bioinformatics tool named...

B-cell epitopes are regions of the antigen surface which can be recognized by certain antibodies and elicit immune response. Identification for a given chain finds vital applications in vaccine drug research. Experimental prediction is time-consuming resource intensive, may benefit from computational approaches to identify epitopes. In this paper, novel cost-sensitive ensemble algorithm proposed predicting antigenic determinant residues then spatial clustering adopted potential Firstly, we...

Glutarylation is a type of post-translational modification that occurs on lysine residues. It plays an irreplaceable role in various cellular functions. Therefore, identification glutarylation sites significant for understanding the molecular mechanism glutarylation. In this study, we proposed method named DEXGB_Glu to identify using XGBoost as classifier which was optimized by differential evolution algorithm. Aiming at imbalance between positive samples and negative samples,...

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Knowledge of protein-protein interactions (PPIs) plays an important role in constructing protein interaction networks and understanding the general machineries biological systems. In this study, a new method is proposed to predict PPIs using comprehensive set 930 features based only on sequence information, these measure between residues certain distant apart sequences from different aspects. To achieve better performance, principal component analysis (PCA) first employed obtain optimized...

Gabor filters are widely utilized to detect iris texture information in several state-of-the-art recognition systems. However, the proper kernels and generative pattern of features need be predetermined application. The traditional empirical shallow encoding ways incapable dealing with such complex variations imaging including illumination, aging, deformation, device variations. Thereby, an adaptive filter selection strategy deep learning architecture presented. We first employ particle...

Lysine succinylation is a new kind of post-translational modification which plays key role in protein conformation regulation and cellular function control. To understand the mechanism profoundly, it necessary to identify sites proteins accurately. However, traditional methods, experimental approaches, are labor-intensive time-consuming. Computational prediction methods have been proposed recent years, they popular because their convenience high speed. In this study, we developed method...

Abstract Motivation Quantitative determination of protein thermodynamic stability is a critical step in and drug design. Reliable prediction changes caused by point variations contributes to developing-related fields. Over the past decades, dozens structure-based sequence-based methods have been proposed, showing good performance. Despite impressive progress, it necessary explore wild-type variant representations address problem how represent change view global sequence. With development...

Abstract Background DNA-binding proteins (DBPs) play fundamental roles in many biological processes. Therefore, the developing of effective computational tools for identifying DBPs is becoming highly desirable. Results In this study, we proposed an accurate method prediction DBPs. Firstly, focused on challenge improving DBP accuracy with information solely from sequence. Secondly, used multiple informative features to encode protein. These included evolutionary conservation profile,...

DNA-binding proteins play an important role in most cellular processes, such as gene regulation, recombination, repair, replication, and DNA modification. In this article, optimal Chou’s pseudo amino acid composition (PseAAC) based on physicochemical characters of is proposed to represent for identifying DNAbinding proteins. Six acids are utilized generate the sequence features via web server PseAAC. The values two parameters (correlation factor λ weighting w) about PseAAC determined get...

Protein pupylation is a type of post-translation modification, which plays crucial role in cellular function bacterial organisms prokaryotes. To have better insight the mechanisms underlying an initial, but important, step to identify sites. date, several computational methods been established for prediction sites usually artificially design negative samples using verified proteins train classifiers. However, if this process not properly done it can affect performance final predictor...

Cancer gene expression data is of great importance in cancer subtype diagnosis and drug discovery. Many computational methods have been proposed to classify subtypes using those data. However, most the previous suffer from poor interpretability, experimental noises, low diagnostic quality. To address problems, multiobjective ensemble cuckoo search based on decomposition (MOECSA) optimize four objectives simultaneously including number features, accuracy, two entropy-based measures: relevance...

Path-planning for uninhabited combat air vehicles (UCAV) is a typically complicated global optimization problem. It seeks superior flight path in complex battlefield environment, taking into various constraints. Many swarm intelligence (SI) algorithms have recently gained remarkable attention due to their capability address problems. However, different SI present performances UCAV path-planning since each algorithm has its own strengths and weaknesses. Therefore, this study provides an...