OPTIMA: A New Score Function for the Detection of Remote Homologs

Optimization of a new score function for the detection of remote homologs

Proteins Structure Function and Bioinformatics ◽

10.1002/1097-0134(20001201)41:4<498::aid-prot70>3.0.co;2-3 ◽

2000 ◽

Vol 41 (4) ◽

pp. 498-503 ◽

Cited By ~ 24

Author(s):

Maricel Kann ◽

Bin Qian ◽

Richard A. Goldstein

Keyword(s):

Score Function ◽

Remote Homologs

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Non-Negative Symmetric Low-Rank Representation Graph Regularized Method for Cancer Clustering Based on Score Function

Frontiers in Genetics ◽

10.3389/fgene.2019.01353 ◽

2020 ◽

Vol 10 ◽

Author(s):

Conghai Lu ◽

Juan Wang ◽

Jinxing Liu ◽

Chunhou Zheng ◽

Xiangzhen Kong ◽

...

Keyword(s):

Score Function ◽

Low Rank ◽

Low Rank Representation ◽

Regularized Method

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TransET: Knowledge Graph Embedding with Entity Types

Electronics ◽

10.3390/electronics10121407 ◽

2021 ◽

Vol 10 (12) ◽

pp. 1407

Author(s):

Peng Wang ◽

Jing Zhou ◽

Yuzhang Liu ◽

Xingchen Zhou

Keyword(s):

Link Prediction ◽

State Of The Art ◽

Score Function ◽

Graph Embedding ◽

Vector Spaces ◽

Knowledge Graph ◽

Semantic Features ◽

Knowledge Graphs ◽

Real World Datasets ◽

Low Dimensional

Knowledge graph embedding aims to embed entities and relations into low-dimensional vector spaces. Most existing methods only focus on triple facts in knowledge graphs. In addition, models based on translation or distance measurement cannot fully represent complex relations. As well-constructed prior knowledge, entity types can be employed to learn the representations of entities and relations. In this paper, we propose a novel knowledge graph embedding model named TransET, which takes advantage of entity types to learn more semantic features. More specifically, circle convolution based on the embeddings of entity and entity types is utilized to map head entity and tail entity to type-specific representations, then translation-based score function is used to learn the presentation triples. We evaluated our model on real-world datasets with two benchmark tasks of link prediction and triple classification. Experimental results demonstrate that it outperforms state-of-the-art models in most cases.

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A general model of G protein-coupled receptor sequences and its application to detect remote homologs

Protein Science ◽

10.1110/ps.051745906 ◽

2006 ◽

Vol 15 (3) ◽

pp. 509-521 ◽

Cited By ~ 112

Author(s):

Markus Wistrand ◽

Lukas Käll ◽

Erik L.L. Sonnhammer

Keyword(s):

G Protein ◽

General Model ◽

G Protein Coupled Receptor ◽

Remote Homologs ◽

G Protein Coupled

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The Impact of Mergers and Acquisitions and Sustainability on Company Performance in the Pharmaceutical Sector

Sustainability ◽

10.3390/su13126525 ◽

2021 ◽

Vol 13 (12) ◽

pp. 6525

Author(s):

Diana Marieta Mihaiu ◽

Radu-Alexandru Șerban ◽

Alin Opreana ◽

Mihai Țichindelean ◽

Vasile Brătian ◽

...

Keyword(s):

Stock Market ◽

Mergers And Acquisitions ◽

Performance Improvement ◽

Score Function ◽

Pharmaceutical Companies ◽

Company Performance ◽

Direct Impact ◽

Pharmaceutical Sector ◽

Financial Measures ◽

The Impact

The primary goal of this study was to determine the impact of mergers and acquisitions (M&A) and the environmental, social, and governance (ESG) sustainability scores of companies. In this regard, efforts to measure and analyze the evolution of a company’s performance, taking into account financial and non-financial measures using a score function, are adapted to the pharmaceutical sector. The sample consisted of 100 leading pharmaceutical companies, ranked by stock market capitalization, who registered 30% (n = 492) of the total M&A transactions over the study period (2010–2020). There was a direct and positive link between the M&A process and the evolution of company performance. The ESG score, as an indicator for measuring sustainability, has a positive and direct impact on company performance, indicating that a high ESG score determines an increase in company performance. A similar impact is identified for companies involved in M&A processes, meaning that companies in the pharmaceutical sector tend to register a performance improvement.

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Complex q-rung orthopair fuzzy Schweizer–Sklar Muirhead mean aggregation operators and their application in multi-criteria decision-making

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-202440 ◽

2021 ◽

pp. 1-23

Author(s):

Peide Liu ◽

Tahir Mahmood ◽

Zeeshan Ali

Keyword(s):

Decision Making ◽

Comparative Analysis ◽

Imaginary Part ◽

Fuzzy Set ◽

Score Function ◽

Aggregation Operators ◽

Unit Interval ◽

Multi Criteria Decision Making ◽

Accuracy Function ◽

Special Cases

Complex q-rung orthopair fuzzy set (CQROFS) is a proficient technique to describe awkward and complicated information by the truth and falsity grades with a condition that the sum of the q-powers of the real part and imaginary part is in unit interval. Further, Schweizer–Sklar (SS) operations are more flexible to aggregate the information, and the Muirhead mean (MM) operator can examine the interrelationships among the attributes, and it is more proficient and more generalized than many aggregation operators to cope with awkward and inconsistence information in realistic decision issues. The objectives of this manuscript are to explore the SS operators based on CQROFS and to study their score function, accuracy function, and their relationships. Further, based on these operators, some MM operators based on PFS, called complex q-rung orthopair fuzzy MM (CQROFMM) operator, complex q-rung orthopair fuzzy weighted MM (CQROFWMM) operator, and their special cases are presented. Additionally, the multi-criteria decision making (MCDM) approach is developed by using the explored operators based on CQROFS. Finally, the advantages and comparative analysis are also discussed.

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Multi-criteria decision-making algorithm based on aggregation operators under the complex interval-valued q-rung orthopair uncertain linguistic information

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-210442 ◽

2021 ◽

pp. 1-30

Author(s):

Harish Garg ◽

Zeeshan Ali ◽

Zaoli Yang ◽

Tahir Mahmood ◽

Sultan Aljahdali

Keyword(s):

Decision Making ◽

Brain Tumors ◽

Score Function ◽

Aggregation Operators ◽

Multi Criteria Decision Making ◽

Linguistic Features ◽

Interval Numbers ◽

Fundamental Properties ◽

Interval Valued

The paper aims to present a concept of a Complex interval-valued q-rung orthopair uncertain linguistic set (CIVQROULS) and investigated their properties. In the presented set, the membership grades are considered in terms of the interval numbers under the complex domain while the linguistic features are added to address the uncertainties in the data. To further discuss more, we have presented the operation laws and score function for CIVQROULS. In addition to them, we present some averaging and geometric operators to aggregate the different pairs of the CIVQROULS. Some fundamental properties of the proposed operators are stated. Afterward, an algorithm for solving the decision-making problems is addressed based on the proposed operator using the CIVQROULS features. The applicability of the algorithm is demonstrated through a case study related to brain tumors and their effectiveness is compared with the existing studies.

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