Pythagorean fuzzy sets (PFSs) were proposed by Yager in 2013 to treat imprecise and vague information in daily life more rigorously and efficiently with higher precision than intuitionistic fuzzy ...sets. In this paper, we construct new distance and similarity measures of PFSs based on the Hausdorff metric. We first develop a method to calculate a distance between PFSs based on the Hasudorff metric, along with proving several properties and theorems. We then consider a generalization of other distance measures, such as the Hamming distance, the Euclidean distance, and their normalized versions. On the basis of the proposed distances for PFSs, we give new similarity measures to compute the similarity degree of PFSs. Some examples related to pattern recognition and linguistic variables are used to validate the proposed distance and similarity measures. Finally, we apply the proposed methods to multicriteria decision‐making by constructing a Pythagorean fuzzy Technique for Order Preference by Similarity to an Ideal Solution and then present a practical example to address an important issue related to social sector. Numerical results indicate that the proposed methods are reasonable and applicable and also that they are well suited in pattern recognition, linguistic variables, and multicriteria decision‐making with PFSs.
A new program, TALOS-N, is introduced for predicting protein backbone torsion angles from NMR chemical shifts. The program relies far more extensively on the use of trained artificial neural networks ...than its predecessor, TALOS+. Validation on an independent set of proteins indicates that backbone torsion angles can be predicted for a larger, ≥90 % fraction of the residues, with an error rate smaller than ca 3.5 %, using an acceptance criterion that is nearly two-fold tighter than that used previously, and a root mean square difference between predicted and crystallographically observed (
ϕ
,
ψ
) torsion angles of ca 12º. TALOS-N also reports sidechain χ
1
rotameric states for about 50 % of the residues, and a consistency with reference structures of 89 %. The program includes a neural network trained to identify secondary structure from residue sequence and chemical shifts.
The k-means algorithm is generally the most known and used clustering method. There are various extensions of k-means to be proposed in the literature. Although it is an unsupervised learning to ...clustering in pattern recognition and machine learning, the k-means algorithm and its extensions are always influenced by initializations with a necessary number of clusters a priori. That is, the k-means algorithm is not exactly an unsupervised clustering method. In this paper, we construct an unsupervised learning schema for the k-means algorithm so that it is free of initializations without parameter selection and can also simultaneously find an optimal number of clusters. That is, we propose a novel unsupervised k-means (U-k-means) clustering algorithm with automatically finding an optimal number of clusters without giving any initialization and parameter selection. The computational complexity of the proposed U-k-means clustering algorithm is also analyzed. Comparisons between the proposed U-k-means and other existing methods are made. Experimental results and comparisons actually demonstrate these good aspects of the proposed U-k-means clustering algorithm.
Abstract
Motivation
Drug discovery demands rapid quantification of compound–protein interaction (CPI). However, there is a lack of methods that can predict compound–protein affinity from sequences ...alone with high applicability, accuracy and interpretability.
Results
We present a seamless integration of domain knowledges and learning-based approaches. Under novel representations of structurally annotated protein sequences, a semi-supervised deep learning model that unifies recurrent and convolutional neural networks has been proposed to exploit both unlabeled and labeled data, for jointly encoding molecular representations and predicting affinities. Our representations and models outperform conventional options in achieving relative error in IC50 within 5-fold for test cases and 20-fold for protein classes not included for training. Performances for new protein classes with few labeled data are further improved by transfer learning. Furthermore, separate and joint attention mechanisms are developed and embedded to our model to add to its interpretability, as illustrated in case studies for predicting and explaining selective drug–target interactions. Lastly, alternative representations using protein sequences or compound graphs and a unified RNN/GCNN-CNN model using graph CNN (GCNN) are also explored to reveal algorithmic challenges ahead.
Availability and implementation
Data and source codes are available at https://github.com/Shen-Lab/DeepAffinity.
Supplementary information
Supplementary data are available at Bioinformatics online.
Rumination is strongly and consistently correlated with depression. Although multiple studies have explored the neural correlates of rumination, findings have been inconsistent and the mechanisms ...underlying rumination remain elusive. Functional brain imaging studies have identified areas in the default mode network (DMN) that appear to be critically involved in ruminative processes. However, a meta-analysis to synthesize the findings of brain regions underlying rumination is currently lacking. Here, we conducted a meta-analysis consisting of experimental tasks that investigate rumination by using Signed Differential Mapping of 14 fMRI studies comprising 286 healthy participants. Furthermore, rather than treat the DMN as a unitary network, we examined the contribution of three DMN subsystems to rumination. Results confirm the suspected association between rumination and DMN activation, specifically implicating the DMN core regions and the dorsal medial prefrontal cortex subsystem. Based on these findings, we suggest a hypothesis of how DMN regions support rumination and present the implications of this model for treating major depressive disorder characterized by rumination.
•Rumination is strongly and consistently correlated with depression.•Meta-analyze the findings of brain regions regarding to rumination.•Specifically examined the contribution of three DMN subsystems to rumination.•Rumination is specifically correlated with the DMN core regions and the dorsal medial prefrontal cortex subsystem.
Structural information about protein‐protein interactions, often missing at the interactome scale, is important for mechanistic understanding of cells and rational discovery of therapeutics. Protein ...docking provides a computational alternative for such information. However, ranking near‐native docked models high among a large number of candidates, often known as the scoring problem, remains a critical challenge. Moreover, estimating model quality, also known as the quality assessment problem, is rarely addressed in protein docking. In this study, the two challenging problems in protein docking are regarded as relative and absolute scoring, respectively, and addressed in one physics‐inspired deep learning framework. We represent protein and complex structures as intra‐ and inter‐molecular residue contact graphs with atom‐resolution node and edge features. And we propose a novel graph convolutional kernel that aggregates interacting nodes’ features through edges so that generalized interaction energies can be learned directly from 3D data. The resulting energy‐based graph convolutional networks (EGCN) with multihead attention are trained to predict intra‐ and inter‐molecular energies, binding affinities, and quality measures (interface RMSD) for encounter complexes. Compared to a state‐of‐the‐art scoring function for model ranking, EGCN significantly improves ranking for a critical assessment of predicted interactions (CAPRI) test set involving homology docking; and is comparable or slightly better for Score_set, a CAPRI benchmark set generated by diverse community‐wide docking protocols not known to training data. For Score_set quality assessment, EGCN shows about 27% improvement to our previous efforts. Directly learning from 3D structure data in graph representation, EGCN represents the first successful development of graph convolutional networks for protein docking.
In a continuous-variable quantum key distribution (CV-QKD) system, the computation speed of the post-processing procedure, including information reconciliation (IR) and privacy amplification (PA), ...inevitably affects the practical secret key rate. IR and PA can be implemented in parallel using low-density parity-check (LDPC) codes and hash functions, respectively. We achieve high-speed hardware-accelerated post-processing procedure for Gaussian symbols on a field-programmable gate array (FPGA) by taking advantage of its superior parallel processing ability. To this end, the sum-product algorithm decoders and a modified LDPC codes construction algorithm adapted to FPGA's characteristics are developed and employed. Two different structures including multiplexing and non-multiplexing are designed to achieve the trade-off between the speed and area of FPGAs, so that an optimal scheme can be adopted according to the requirement of a practical system. Simulation results show that the maximum throughput can reach 100 M symbols/s. We verified the correctness of the post-processing procedures when implemented on the Xilinx VC709 evaluation board, which is populated with the Virtex-7 XC7VX690T FPGA and provided some possible solutions to obtain better performance when more advanced FPGAs are available. The scheme can be applied readily for real-time key extraction and effectively reduce power consumption of the CV-QKD system.
Objective
Gut microbiome alterations in Parkinson disease (PD) have been reported repeatedly, but their functional relevance remains unclear. Fecal metabolomics, which provide a functional readout of ...microbial activity, have scarcely been investigated. We investigated fecal microbiome and metabolome alterations in PD, and their clinical relevance.
Methods
Two hundred subjects (104 patients, 96 controls) underwent extensive clinical phenotyping. Stool samples were analyzed using 16S rRNA gene sequencing. Fecal metabolomics were performed using two platforms, nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography–mass spectrometry.
Results
Fecal microbiome and metabolome composition in PD was significantly different from controls, with the largest effect size seen in NMR‐based metabolome. Microbiome and NMR‐based metabolome compositional differences remained significant after comprehensive confounder analyses. Differentially abundant fecal metabolite features and predicted functional changes in PD versus controls included bioactive molecules with putative neuroprotective effects (eg, short chain fatty acids SCFAs, ubiquinones, and salicylate) and other compounds increasingly implicated in neurodegeneration (eg, ceramides, sphingosine, and trimethylamine N‐oxide). In the PD group, cognitive impairment, low body mass index (BMI), frailty, constipation, and low physical activity were associated with fecal metabolome compositional differences. Notably, low SCFAs in PD were significantly associated with poorer cognition and low BMI. Lower butyrate levels correlated with worse postural instability–gait disorder scores.
Interpretation
Gut microbial function is altered in PD, characterized by differentially abundant metabolic features that provide important biological insights into gut–brain pathophysiology. Their clinical relevance further supports a role for microbial metabolites as potential targets for the development of new biomarkers and therapies in PD. ANN NEUROL 2021;89:546–559
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