Where did the BLOSUM62 alignment score matrix come from?

2004 ◽  
Vol 22 (8) ◽  
pp. 1035-1036 ◽  
Author(s):  
Sean R Eddy
Keyword(s):  
Alignment Score ◽  
Score Matrix

scholarly journals ASM-Clust: classifying functionally diverse protein families using alignment score matrices

2019 ◽  
Author(s):  
Daan R. Speth ◽  
Victoria J. Orphan
Keyword(s):  
De Novo ◽  
Alignment Score ◽  
Small Subset ◽  
Sequencing Technology ◽  
Large Protein ◽  
The Family ◽  
Complex Protein ◽  
Functionally Diverse ◽  
Score Matrix

AbstractRapid advances in sequencing technology have resulted in the availability of genomes from organisms across the tree of life. Accurately interpreting the function of proteins in these genomes is a major challenge, as annotation transfer based on homology frequently results in misannotation and error propagation. This challenge is especially pressing for organisms whose genomes are directly obtained from environmental samples, as interpretation of their physiology and ecology is often based solely on the genome sequence. For complex protein (super)families containing a large number of sequences, classification can be used to determine whether annotation transfer is appropriate, or whether experimental evidence for function is lacking. Here we present a novel computational approach for de novo classification of large protein (super)families, based on clustering an alignment score matrix obtained by aligning all sequences in the family to a small subset of the data. We evaluate our approach on the enolase family in the Structure Function Linkage Database.Availability and implementationASM-Clust is implemented in bash with helper scripts in perl. Scripts comprising ASM-Clust are available for download from https://github.com/dspeth/bioinfo_scripts/tree/master/ASM_clust/

A Score Matrix for Thurstone's Box Problem

1975 ◽  
Vol 10 (1) ◽  
pp. 17-26 ◽  
Author(s):  
Henry F. Kaiser ◽  
Paul Horst
Keyword(s):  
Score Matrix

Time-Weighted Collaborative Filtering Algorithm Based on Improved Mini Batch K-Means Clustering

2021 ◽  
Vol 105 ◽  
pp. 309-317
Author(s):  
Xue Han ◽  
Zhong Wang ◽  
Hui Jun Xu
Keyword(s):  
Collaborative Filtering ◽  
Sparse Matrix ◽  
Pearson Correlation ◽  
Absolute Error ◽  
User Interest ◽  
User Similarity ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Low Efficiency ◽  
Score Matrix

The traditional collaborative filtering recommendation algorithm has the defects of sparse score matrix, weak scalability and user interest deviation, which lead to the low efficiency of algorithm and low accuracy of score prediction. Aiming at the above problems, this paper proposed a time-weighted collaborative filtering algorithm based on improved Mini Batch K-Means clustering. Firstly, the algorithm selected the Pearson correlation coefficient to improve the Mini Batch K-Means clustering, and used the improved Mini Batch K-Means algorithm to cluster the sparse scoring matrix, calculated the user interest score to complete the filling of the sparse matrix. Then, considering the influence of user interest drift with time, the algorithm introduced the Newton cooling time-weighted to improve user similarity. And then calculated user similarity based on the filled score matrix, which helped to get the last predicted score of unrated items The experimental results show that, compared with the traditional collaborative filtering algorithms, the mean absolute error of Proposed improved algorithm is d, and the Precision, Recall and F1 value of MBKT-CF also get a large improvement, which has a higher rating prediction accuracy.

scholarly journals Ranking-Theory Methods for Solving Multicriteria Decision-Making Problems

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Joseph Gogodze
Keyword(s):  
Decision Making ◽  
Multicriteria Decision Making ◽  
Pareto Optimal ◽  
Materials Selection ◽  
Ranking Methods ◽  
New Approach ◽  
Multicriteria Decision ◽  
Multicriteria Decision Making Problems ◽  
Ranking Theory ◽  
Score Matrix

The Pareto optimality is a widely used concept for the multicriteria decision-making problems. However, this concept has a significant drawback—the set of Pareto optimal alternatives usually is large. Correspondingly, the problem of choosing a specific Pareto optimal alternative for the decision implementation is arising. This study proposes a new approach to select an “appropriate” alternative from the set of Pareto optimal alternatives. The proposed approach is based on ranking-theory methods used for ranking participants in sports tournaments. In the framework of the proposed approach, we build a special score matrix for a given multicriteria problem, which allows the use of the mentioned ranking methods and to choose the corresponding best-ranked alternative from the Pareto set as a solution of the problem. The proposed approach is particularly useful when no decision-making authority is available, or when the relative importance of various criteria has not been evaluated previously. The proposed approach is tested on an example of a materials-selection problem for a sailboat mast.

scholarly journals Collaborative filtering recommendation algorithm based on user correlation and evolutionary clustering

2019 ◽  
Vol 6 (1) ◽  
pp. 147-156 ◽  
Author(s):  
Jianrui Chen ◽  
Chunxia Zhao ◽  
Uliji ◽  
Lifang Chen
Keyword(s):  
Dimension Reduction ◽  
Collaborative Filtering ◽  
Data Clustering ◽  
Real Life ◽  
Nearest Neighbors ◽  
User Group ◽  
Recommendation Algorithm ◽  
Evolutionary Clustering ◽  
The Relationship ◽  
Score Matrix

Abstract In recent years, application of recommendation algorithm in real life such as Amazon, Taobao is getting universal, but it is not perfect yet. A few problems need to be solved such as sparse data and low recommended accuracy. Collaborative filtering is a mature algorithm in the recommended systems, but there are still some problems. In this paper, a novel collaborative filtering recommendation algorithm based on user correlation and evolutionary clustering is presented. Firstly, score matrix is pre-processed with normalization and dimension reduction, to obtain denser score data. Based on these processed data, clustering principle is generated and dynamic evolutionary clustering is implemented. Secondly, the search for the nearest neighbors with highest similar interest is considered. A measurement about the relationship between users is proposed, called user correlation, which applies the satisfaction of users and the potential information. In each user group, user correlation is applied to choose the nearest neighbors to predict ratings. The proposed method is evaluated using the Movielens dataset. Diversity experimental results demonstrate that the proposed method has outstanding performance in predicted accuracy and recommended precision.

scholarly journals STA: Spatial-Temporal Attention for Large-Scale Video-Based Person Re-Identification

2019 ◽  
Vol 33 ◽  
pp. 8287-8294 ◽  
Author(s):  
Yang Fu ◽  
Xiaoyang Wang ◽  
Yunchao Wei ◽  
Thomas Huang
Keyword(s):  
Large Scale ◽  
Feature Representation ◽  
Temporal Attention ◽  
Video Clips ◽  
The Arts ◽  
Temporal Dimensions ◽  
Pose Variation ◽  
Inter Frame ◽  
Score Matrix ◽  
Discriminative Parts

In this work, we propose a novel Spatial-Temporal Attention (STA) approach to tackle the large-scale person reidentification task in videos. Different from the most existing methods, which simply compute representations of video clips using frame-level aggregation (e.g. average pooling), the proposed STA adopts a more effective way for producing robust clip-level feature representation. Concretely, our STA fully exploits those discriminative parts of one target person in both spatial and temporal dimensions, which results in a 2-D attention score matrix via inter-frame regularization to measure the importances of spatial parts across different frames. Thus, a more robust clip-level feature representation can be generated according to a weighted sum operation guided by the mined 2-D attention score matrix. In this way, the challenging cases for video-based person re-identification such as pose variation and partial occlusion can be well tackled by the STA. We conduct extensive experiments on two large-scale benchmarks, i.e. MARS and DukeMTMCVideoReID. In particular, the mAP reaches 87.7% on MARS, which significantly outperforms the state-of-the-arts with a large margin of more than 11.6%.

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