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Background: Adaptor proteins play a key role in intercellular signal transduction, and dysfunctional adaptor proteins result in diseases. Understanding its structure is the first step to tackling the associated conditions, spurring ongoing interest in research into adaptor proteins with bioinformatics and computational biology. Our study aims to introduce a small, new, and superior model for protein classification, pushing the boundaries with new machine learning algorithms. Results: We propose a novel transformer based model which includes convolutional block and fully connected layer. We input protein sequences from a database, extract PSSM features, then process it via our deep learning model. The proposed model is efficient and highly compact, achieving state-of-the-art performance in terms of area under the receiver operating characteristic curve, Matthew's Correlation Coefficient and Receiver Operating Characteristics curve. Despite merely 20 hidden nodes translating to approximately 1% of the complexity of previous best known methods, the proposed model is still superior in results and computational efficiency. Conclusions: The proposed model is the first transformer model used for recognizing adaptor protein, and outperforms all existing methods, having PSSM profiles as inputs that comprises convolutional blocks, transformer and fully connected layers for the use of classifying adaptor proteins. [ABSTRACT FROM AUTHOR]

Unsupervised k -nearest-neighbor-based outlier detectors play a vital role in data science research. However, the detectors' performance relies on the choice of the parameter k. However, autonomous selection of the optimal k is poorly documented in literature as it is very challenging. Conventional methods prove ineffective and lack universality as they fail to account for both application and detector factors simultaneously. This article proposes neighborhood consistency , a new concept which tackles the existing issues of selecting the optimal k by considering both application and detector factors. This concept was used to develop a method termed k finder based on neighborhood c onsistency (KFC). KFC does not rely on any extra parameter and has linear time complexity. Simulations show that KFC outperformed baselines and had a good generality to different datasets and detectors. The implementation of the proposed methods can be found on www.OutlierNet.com for reproducibility. • Neighborhood consistency (NC) that an object and its k-NN should have consistent outlier scores is introduced. • Based on NC, we proposed a framework called KFC for automatically selecting k for k-NN-based outlier detectors. • We reviewed the optimum-k-searching methods published between 2000 and 2022. [ABSTRACT FROM AUTHOR]

The incorrect affiliation is: 2 School of Marine Science and Technology, Northwestern Polytechnical University and Singapore Institute of Technology, 710072 Xi'an, China. B Correction to: BMC Bioinformatics (2022) 23:461 b https://doi.org/10.1186/s12859-022-05000-6 Following the publication of the original article [[1]], the authors identified an error in affiliation 2. [Extracted from the article]

Trajectory outlier detection is one of the fundamental data mining techniques used to analyze the trajectory data of the Global Positioning System. A comprehensive literature review of trajectory outlier detectors published between 2000 and 2022 led to a conclusion that conventional trajectory outlier detectors suffered from drawbacks, either due to the detectors themselves or the pre-processing methods for the variable-length trajectory inputs utilized by detectors. To address these issues, we proposed a feature extraction method called middle polar coordinates (MiPo). MiPo extracted tabular features from trajectory data prior to the application of conventional outlier detectors to detect trajectory outliers. By representing variable-length trajectory data as fixed-length tabular data, MiPo granted tabular outlier detectors the ability to detect trajectory outliers, which was previously impossible. Experiments with real-world datasets showed that MiPo outperformed all baseline methods with 0.99 AUC on average; however, it only required approximately 10% of the computing time of the existing industrial best. MiPo exhibited linear time and space complexity. The features extracted by MiPo may aid other trajectory data mining tasks. We believe that MiPo has the potential to revolutionize the field of trajectory outlier detection. [ABSTRACT FROM AUTHOR]