Relevant bibliographies by topics / Fuzzy Measures

Academic literature on the topic 'Fuzzy Measures'

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Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Fuzzy Measures"

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Congxin Wu, Deli Zhang, Caimei Guo, and Cong Wu. "Fuzzy number fuzzy measures and fuzzy integrals. (I). Fuzzy integrals of functions with respect to fuzzy number fuzzy measures." Fuzzy Sets and Systems 98, no. 3 (September 1998): 355–60. http://dx.doi.org/10.1016/s0165-0114(96)00394-6.

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Squillante, Massimo, and Aldo G. S. Ventre. "Generating fuzzy measures." Journal of Mathematical Analysis and Applications 165, no. 2 (April 1992): 550–55. http://dx.doi.org/10.1016/0022-247x(92)90058-l.

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Glass, David H. "Fuzzy confirmation measures." Fuzzy Sets and Systems 159, no. 4 (February 2008): 475–90. http://dx.doi.org/10.1016/j.fss.2007.07.018.

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Cutello, Vincenzo, and Javier Montero. "Fuzzy rationality measures." Fuzzy Sets and Systems 62, no. 1 (February 1994): 39–54. http://dx.doi.org/10.1016/0165-0114(94)90071-x.

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Sancho-Royo, A., and J. L. Verdegay. "Fuzzy coherence measures." International Journal of Intelligent Systems 20, no. 1 (January 2005): 1–11. http://dx.doi.org/10.1002/int.20050.

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de Glas, Michel. "Fuzzy σ-fields and fuzzy measures." Journal of Mathematical Analysis and Applications 124, no. 1 (May 1987): 281–89. http://dx.doi.org/10.1016/0022-247x(87)90039-4.

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Caimei Guo, Deli Zhang, and Congxin Wu. "Fuzzy-valued fuzzy measures and generalized fuzzy integrals." Fuzzy Sets and Systems 97, no. 2 (July 1998): 255–60. http://dx.doi.org/10.1016/s0165-0114(96)00276-x.

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Maturo, Antonio. "Fuzzy measures and coherent join measures." International Journal of Intelligent Systems 26, no. 12 (October 11, 2011): 1196–205. http://dx.doi.org/10.1002/int.20512.

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Wu, Congxin, Deli Zhang, Bokan Zhang, and Caimei Guo. "Fuzzy number fuzzy measures and fuzzy integrals. (II). Fuzzy integrals of fuzzy-valued functions with respect to fuzzy number fuzzy measures on fuzzy sets." Fuzzy Sets and Systems 101, no. 1 (January 1999): 137–41. http://dx.doi.org/10.1016/s0165-0114(97)00041-9.

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CALVO, T., J. MARTIN, G. MAYOR, and J. TORRENS. "BALANCED DISCRETE FUZZY MEASURES." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 08, no. 06 (December 2000): 665–76. http://dx.doi.org/10.1142/s0218488500000484.

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First a balancing property on a fuzzy measure is introduced. After that, the conditions of when an additive fuzzy measure is balanced are given and similar results are presented for 0-1 and S–decomposable fuzzy measures where S is a continuous t–conorm. Moreover, the concept of distance between two additive fuzzy measures is presented and some results related to the distance and the balancing property are developed.
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Dissertations / Theses on the topic "Fuzzy Measures"

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Santos, H?lida Salles. "A new class of fuzzy subsethood measures." PROGRAMA DE P?S-GRADUA??O EM SISTEMAS E COMPUTA??O, 2016. https://repositorio.ufrn.br/jspui/handle/123456789/23644.

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Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior (CAPES)
Este trabalho tem o objetivo de introduzir uma nova classe de medidas de inclus?o difusa entre conjuntos difusos. Esta nova abordagem foi baseada nas axiomatiza??es mais conhecidas com a vantagem de utilizar o m?todo de constru??o de tais medidas agregando os operadores de implica??o. Esses operadores satisfazem algumas propriedades que t?m sido amplamente investigadas na literatura, de forma que, por exemplo, a medida de inclus?o proposta por Goguen torna-se um caso particular da nossa proposta de medida de inclus?o. Apresentamos tamb?m diferentes m?todos de constru??o utilizando automorfismos e provamos que com tais medidas podemos construir n?o s? medidas de entropia, mas tamb?m dist?ncias, fun??es p?nalti e medidas de similaridade entre conjuntos difusos.
The idea of inclusion for fuzzy sets was firstly introduced by L. Zadeh in 1965 and since then many other studies proposed alternatives to indicate a degree to which a fuzzy set is included into another fuzzy set, called an inclusion degree or a subsethood measure. In this work we present a new class of fuzzy subsethood measures between fuzzy sets. We introduce a new definition of a fuzzy subsethood measure as an intersection of other axiomatizations by aggregating fuzzy implication operators. We also provide some construction methods to obtain these fuzzy subsethood measures. With our approach we recover some of the classical measures which have been discussed in the literature, as the one given by Goguen. We also show how we can use our developments to generate fuzzy entropies, fuzzy distances, penalty functions and similarity measures. Finally we study some fuzzy indexes generated from this new class of fuzzy subsethood measures.
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Parlikar, Virendra R. "Fuzzy non-radial measures of relative technical efficiency using DEA." Thesis, This resource online, 1996. http://scholar.lib.vt.edu/theses/available/etd-11182008-063112/.

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Bashon, Yasmina M. "Contributions to fuzzy object comparison and applications. Similarity measures for fuzzy and heterogeneous data and their applications." Thesis, University of Bradford, 2013. http://hdl.handle.net/10454/6305.

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This thesis makes an original contribution to knowledge in the fi eld of data objects' comparison where the objects are described by attributes of fuzzy or heterogeneous (numeric and symbolic) data types. Many real world database systems and applications require information management components that provide support for managing such imperfect and heterogeneous data objects. For example, with new online information made available from various sources, in semi-structured, structured or unstructured representations, new information usage and search algorithms must consider where such data collections may contain objects/records with di fferent types of data: fuzzy, numerical and categorical for the same attributes. New approaches of similarity have been presented in this research to support such data comparison. A generalisation of both geometric and set theoretical similarity models has enabled propose new similarity measures presented in this thesis, to handle the vagueness (fuzzy data type) within data objects. A framework of new and unif ied similarity measures for comparing heterogeneous objects described by numerical, categorical and fuzzy attributes has also been introduced. Examples are used to illustrate, compare and discuss the applications and e fficiency of the proposed approaches to heterogeneous data comparison.
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Bashon, Yasmina Massoud. "Contributions to fuzzy object comparison and applications : similarity measures for fuzzy and heterogeneous data and their applications." Thesis, University of Bradford, 2013. http://hdl.handle.net/10454/6305.

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This thesis makes an original contribution to knowledge in the fi eld of data objects' comparison where the objects are described by attributes of fuzzy or heterogeneous (numeric and symbolic) data types. Many real world database systems and applications require information management components that provide support for managing such imperfect and heterogeneous data objects. For example, with new online information made available from various sources, in semi-structured, structured or unstructured representations, new information usage and search algorithms must consider where such data collections may contain objects/records with di fferent types of data: fuzzy, numerical and categorical for the same attributes. New approaches of similarity have been presented in this research to support such data comparison. A generalisation of both geometric and set theoretical similarity models has enabled propose new similarity measures presented in this thesis, to handle the vagueness (fuzzy data type) within data objects. A framework of new and unif ied similarity measures for comparing heterogeneous objects described by numerical, categorical and fuzzy attributes has also been introduced. Examples are used to illustrate, compare and discuss the applications and e fficiency of the proposed approaches to heterogeneous data comparison.
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IONESCU, MIRCEA MARIAN. "ADAPTIVE MEASURES OF SIMILARITY - FUZZY HAMMING DISTANCE - AND ITS APPLICATIONS TO PATTERN RECOGNITION PROBLEMS." University of Cincinnati / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1163708478.

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Wagholikar, Amol S., and N/A. "Acquisition of Fuzzy Measures in Multicriteria Decision Making Using Similarity-based Reasoning." Griffith University. School of Information and Communication Technology, 2007. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20071214.152324.

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Continuous development has been occurring in the area of decision support systems. Modern systems focus on applying decision models that can provide intelligent support to the decision maker. These systems focus on modelling the human reasoning process in situations requiring decision. This task may be achieved by using an appropriate decision model. Multicriteria decision making (MCDM) is a common decision making approach. This research investigates and seeks a way to resolve various issues associated with the application of this model. MCDM is a formal and systematic decision making approach that evaluates a given set of alternatives against a given set of criteria. The global evaluation of alternatives is determined through the process of aggregation. It is well established that the aggregation process should consider the importance of criteria while determining the overall worth of an alternative. The importance of individual criteria and of sub-sets of the criteria affects the global evaluation. The aggregation also needs to consider the importance of the sub-set of criteria. Most decision problems involve dependent criteria and the interaction between the criteria needs to be modelled. Traditional aggregation approaches, such as weighted average, do not model the interaction between the criteria. Non-additive measures such as fuzzy measures model the interaction between the criteria. However, determination of non-additive measures in a practical application is problematic. Various approaches have been proposed to resolve the difficulty in acquisition of fuzzy measures. These approaches mainly propose use of past precedents. This research extends this notion and proposes an approach based on similarity-based reasoning. Solutions to the past problems can be used to solve the new decision problems. This is the central idea behind the proposed methodology. The methodology itself applies the theory of reasoning by analogy for solving MCDM problems. This methodology uses a repository of cases of past decision problems. This case base is used to determine the fuzzy measures for the new decision problem. This work also analyses various similarity measures. The illustration of the proposed methodology in a case-based decision support system shows that interactive models are suitable tools for determining fuzzy measures in a given decision problem. This research makes an important contribution by proposing a similarity-based approach for acquisition of fuzzy measures.
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FarinWata, Shehu Saíd. "Performance assessment of fuzzy logic control systems via stability and robustness measures." Diss., Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/14797.

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Wagholikar, Amol S. "Acquisition of Fuzzy Measures in Multicriteria Decision Making Using Similarity-based Reasoning." Thesis, Griffith University, 2007. http://hdl.handle.net/10072/365403.

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Continuous development has been occurring in the area of decision support systems. Modern systems focus on applying decision models that can provide intelligent support to the decision maker. These systems focus on modelling the human reasoning process in situations requiring decision. This task may be achieved by using an appropriate decision model. Multicriteria decision making (MCDM) is a common decision making approach. This research investigates and seeks a way to resolve various issues associated with the application of this model. MCDM is a formal and systematic decision making approach that evaluates a given set of alternatives against a given set of criteria. The global evaluation of alternatives is determined through the process of aggregation. It is well established that the aggregation process should consider the importance of criteria while determining the overall worth of an alternative. The importance of individual criteria and of sub-sets of the criteria affects the global evaluation. The aggregation also needs to consider the importance of the sub-set of criteria. Most decision problems involve dependent criteria and the interaction between the criteria needs to be modelled. Traditional aggregation approaches, such as weighted average, do not model the interaction between the criteria. Non-additive measures such as fuzzy measures model the interaction between the criteria. However, determination of non-additive measures in a practical application is problematic. Various approaches have been proposed to resolve the difficulty in acquisition of fuzzy measures. These approaches mainly propose use of past precedents. This research extends this notion and proposes an approach based on similarity-based reasoning. Solutions to the past problems can be used to solve the new decision problems. This is the central idea behind the proposed methodology. The methodology itself applies the theory of reasoning by analogy for solving MCDM problems. This methodology uses a repository of cases of past decision problems. This case base is used to determine the fuzzy measures for the new decision problem. This work also analyses various similarity measures. The illustration of the proposed methodology in a case-based decision support system shows that interactive models are suitable tools for determining fuzzy measures in a given decision problem. This research makes an important contribution by proposing a similarity-based approach for acquisition of fuzzy measures.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Information and Communication Technology
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Liu, Xiaofeng. "Machinery fault diagnostics based on fuzzy measure and fuzzy integral data fusion techniques." Thesis, Queensland University of Technology, 2007. https://eprints.qut.edu.au/16456/1/Xiaofeng_Liu_Thesis.pdf.

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With growing demands for reliability, availability, safety and cost efficiency in modern machinery, accurate fault diagnosis is becoming of paramount importance so that potential failures can be better managed. Although various methods have been applied to machinery condition monitoring and fault diagnosis, the diagnostic accuracy that can be attained is far from satisfactory. As most machinery faults lead to increases in vibration levels, vibration monitoring has become one of the most basic and widely used methods to detect machinery faults. However, current vibration monitoring methods largely depend on signal processing techniques. This study is based on the recognition that a multi-parameter data fusion approach to diagnostics can produce more accurate results. Fuzzy measures and fuzzy integral data fusion theory can represent the importance of each criterion and express certain interactions among them. This research developed a novel, systematic and effective fuzzy measure and fuzzy integral data fusion approach for machinery fault diagnosis, which comprises feature set selection schema, feature level data fusion schema and decision level data fusion schema for machinery fault diagnosis. Different feature selection and fault diagnostic models were derived from these schemas. Two fuzzy measures and two fuzzy integrals were employed: the 2-additive fuzzy measure, the fuzzy measure, the Choquet fuzzy integral and the Sugeno fuzzy integral respectively. The models were validated using rolling element bearing and electrical motor experiments. Different features extracted from vibration signals were used to validate the rolling element bearing feature set selection and fault diagnostic models, while features obtained from both vibration and current signals were employed to assess electrical motor fault diagnostic models. The results show that the proposed schemas and models perform very well in selecting feature set and can improve accuracy in diagnosing both the rolling element bearing and electrical motor faults.
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Liu, Xiaofeng. "Machinery fault diagnostics based on fuzzy measure and fuzzy integral data fusion techniques." Queensland University of Technology, 2007. http://eprints.qut.edu.au/16456/.

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With growing demands for reliability, availability, safety and cost efficiency in modern machinery, accurate fault diagnosis is becoming of paramount importance so that potential failures can be better managed. Although various methods have been applied to machinery condition monitoring and fault diagnosis, the diagnostic accuracy that can be attained is far from satisfactory. As most machinery faults lead to increases in vibration levels, vibration monitoring has become one of the most basic and widely used methods to detect machinery faults. However, current vibration monitoring methods largely depend on signal processing techniques. This study is based on the recognition that a multi-parameter data fusion approach to diagnostics can produce more accurate results. Fuzzy measures and fuzzy integral data fusion theory can represent the importance of each criterion and express certain interactions among them. This research developed a novel, systematic and effective fuzzy measure and fuzzy integral data fusion approach for machinery fault diagnosis, which comprises feature set selection schema, feature level data fusion schema and decision level data fusion schema for machinery fault diagnosis. Different feature selection and fault diagnostic models were derived from these schemas. Two fuzzy measures and two fuzzy integrals were employed: the 2-additive fuzzy measure, the fuzzy measure, the Choquet fuzzy integral and the Sugeno fuzzy integral respectively. The models were validated using rolling element bearing and electrical motor experiments. Different features extracted from vibration signals were used to validate the rolling element bearing feature set selection and fault diagnostic models, while features obtained from both vibration and current signals were employed to assess electrical motor fault diagnostic models. The results show that the proposed schemas and models perform very well in selecting feature set and can improve accuracy in diagnosing both the rolling element bearing and electrical motor faults.
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Books on the topic "Fuzzy Measures"

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Beliakov, Gleb, Simon James, and Jian-Zhang Wu. Discrete Fuzzy Measures. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-15305-2.

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Ohlan, Anshu, and Ramphul Ohlan. Generalizations of Fuzzy Information Measures. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45928-8.

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Intuitionistic fuzzy measures: Theory and applications. New York: Nova Science Publishers, 2006.

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Hassall, John. Evaluating information systems using fuzzy measures. Wolverhampton: Management Research Centre, Wolverhampton Business School, 1998.

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P, Klement E., ed. Triangular norm-based measures and games with fuzzy coalitions. Dordrecht: Kluwer Academic, 1993.

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Farhadinia, Bahram, and Zeshui Xu. Information Measures for Hesitant Fuzzy Sets and Their Extensions. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3729-1.

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Butnariu, Dan, and Erich Peter Klement. Triangular Norm-Based Measures and Games with Fuzzy Coalitions. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-017-3602-2.

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Controlling accidents and insurers' risks in construction: A fuzzy knowledge-based approach. Hauppauge, NY: Nova Science Publishers, 2009.

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Andersson, Leif. The theory of possibility and fuzzy sets: New ideas for risk analysis and decision making. Stockholm: Swedish Council for Building Research, 1988.

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Wang, Zhenyuan, and George J. Klir. Fuzzy Measure Theory. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4757-5303-5.

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Book chapters on the topic "Fuzzy Measures"

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Trillas, Enric, and Luka Eciolaza. "Fuzzy Measures." In Fuzzy Logic, 159–74. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14203-6_7.

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Wang, Zhenyuan, and George J. Klir. "Fuzzy Measures." In Fuzzy Measure Theory, 39–71. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4757-5303-5_3.

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Beliakov, Gleb, Simon James, and Jian-Zhang Wu. "Fuzzy Integrals." In Discrete Fuzzy Measures, 89–133. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15305-2_5.

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Beliakov, Gleb, Simon James, and Jian-Zhang Wu. "Learning Fuzzy Measures." In Discrete Fuzzy Measures, 205–39. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15305-2_8.

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Beliakov, Gleb, Simon James, and Jian-Zhang Wu. "Introduction." In Discrete Fuzzy Measures, 1–39. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15305-2_1.

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Beliakov, Gleb, Simon James, and Jian-Zhang Wu. "Types of Fuzzy Measures." In Discrete Fuzzy Measures, 41–54. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15305-2_2.

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Beliakov, Gleb, Simon James, and Jian-Zhang Wu. "Value and Interaction Indices." In Discrete Fuzzy Measures, 55–73. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15305-2_3.

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Beliakov, Gleb, Simon James, and Jian-Zhang Wu. "Representations." In Discrete Fuzzy Measures, 75–87. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15305-2_4.

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Beliakov, Gleb, Simon James, and Jian-Zhang Wu. "Symmetric Fuzzy Measures: OWA." In Discrete Fuzzy Measures, 135–92. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15305-2_6.

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Beliakov, Gleb, Simon James, and Jian-Zhang Wu. "k–Order Fuzzy Measures and k–Order Aggregation Functions." In Discrete Fuzzy Measures, 193–203. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15305-2_7.

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Conference papers on the topic "Fuzzy Measures"

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Iyakaremye, Cesar, Pasi Luukka, and David Koloseni. "Feature selection using Yu's similarity measure and fuzzy entropy measures." In 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2012. http://dx.doi.org/10.1109/fuzz-ieee.2012.6250817.

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Sanchez, Jose Luis Gonzalez, Ramon Gonzalez del Campo, Luis Garmendia, and Ronald R. Yager. "Comparing families of measures of k-specificity. measure of crispness." In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2017. http://dx.doi.org/10.1109/fuzz-ieee.2017.8015517.

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Hu, Xiaoli, Shunyang Wei, Mingjing Hou, and Jun Li. "Fuzzy measures defined by addition of fuzzy measures." In 2016 12th International Conference on Natural Computation and 13th Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). IEEE, 2016. http://dx.doi.org/10.1109/fskd.2016.7603297.

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Adel, Naeemeh, Keeley Crockett, Joao P. Carvalho, and Valerie Cross. "Fuzzy Influence in Fuzzy Semantic Similarity Measures." In 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2021. http://dx.doi.org/10.1109/fuzz45933.2021.9494535.

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Nguyen, Hung T., Vladik Kreinovich, Joe Lorkowski, and Saiful Abu. "Why Sugeno λ-measures." In 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2015. http://dx.doi.org/10.1109/fuzz-ieee.2015.7337833.

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Baccour, Leila, Adel M. Alimi, and Robert I. John. "Relationship between intuitionistic fuzzy similarity measures." In 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2011. http://dx.doi.org/10.1109/fuzzy.2011.6007518.

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Beliakov, Gleb, Gang Li, Huy Quan Vu, and Tim Wilkin. "Fuzzy measures of pixel cluster compactness." In 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2014. http://dx.doi.org/10.1109/fuzz-ieee.2014.6891754.

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Coletti, Giulianella, and Bernadette Bouchon-Meunier. "Fuzzy similarity measures and measurement theory." In 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2019. http://dx.doi.org/10.1109/fuzz-ieee.2019.8858793.

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Baccour, Leila, and Adel M. Alimi. "Applications and comparisons of fuzzy similarity measures." In 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2010. http://dx.doi.org/10.1109/fuzzy.2010.5584276.

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Popov, A. T. "Fuzzy morphology and fuzzy convexity measures." In Proceedings of 13th International Conference on Pattern Recognition. IEEE, 1996. http://dx.doi.org/10.1109/icpr.1996.546896.

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Reports on the topic "Fuzzy Measures"

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Klir, George, and Zhenyuan Wang. Constructing Fuzzy Measures by Transformations. Fort Belvoir, VA: Defense Technical Information Center, March 1995. http://dx.doi.org/10.21236/ada300624.

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Zwick, Rami, Edward Carlstein, and David Budescu. Measures of Similarity between Fuzzy Concepts: A Comparative Analysis. Fort Belvoir, VA: Defense Technical Information Center, December 1987. http://dx.doi.org/10.21236/ada189430.

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Goodman, I. R. Applications of Random Set Representations of Fuzzy Sets to Determining Measures of Central Tendency. Fort Belvoir, VA: Defense Technical Information Center, November 1995. http://dx.doi.org/10.21236/ada305661.

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Chang, Shing I., E. S. Lee, and Steven R. Hanna. A Comparative Study of Multivariate Analysis for Selection and Classification Using Fuzzy Measures and Reasoning. Fort Belvoir, VA: Defense Technical Information Center, December 2001. http://dx.doi.org/10.21236/ada397639.

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Dimitrov, Evgeni, Hayden Schaeffer, David Wen, Sandra Rankovic, Kizza Nandyose, and Olivier Thonnard. The Construction of a Vague Fuzzy Measure Through L1 Parameter Optimization. Fort Belvoir, VA: Defense Technical Information Center, August 2012. http://dx.doi.org/10.21236/ada567409.

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Matthews, Lisa, Guanming Wu, Robin Haw, Timothy Brunson, Nasim Sanati, Solomon Shorser, Deidre Beavers, Patrick Conley, Lincoln Stein, and Peter D'Eustachio. Illuminating Dark Proteins using Reactome Pathways. Reactome, October 2022. http://dx.doi.org/10.3180/poster/20221027matthews.

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Abstract:
Diseases are often the consequence of proteins or protein complexes that are non-functional or that function improperly. An active area of research has focused on the identification of molecules that can interact with defective proteins and restore their function. While 22% percent of human proteins are estimated to be druggable, less than fifteen percent are targeted by FDA-approved drugs, and the vast majority of untargeted proteins are understudied or so-called "dark" proteins. Elucidation of the function of these dark proteins, particularly those in commonly drug-targeted protein families, may offer therapeutic opportunities for many diseases. Reactome is the most comprehensive, open-access pathway knowledgebase covering 2585 pathways and including 14246 reactions, 11088 proteins, 13984 complexes, and 1093 drugs. Placing dark proteins in the context of Reactome pathways provides a framework of reference for these proteins facilitating the generation of hypotheses for experimental biologists to develop targeted experiments, unravel the potential functions of these proteins, and then design drugs to manipulate them. To this end, we have trained a random forest with 106 protein/gene pairwise features collected from multiple resources to predict functional interactions between dark proteins and proteins annotated in Reactome and then developed three scores to measure the interactions between dark proteins and Reactome pathways based on enrichment analysis and fuzzy logic simulations. Literature evidence via manual checking and systematic NLP-based analysis support predicted interacting pathways for dark proteins. To visualize dark proteins in the context of Reactome pathways, we have also developed a new website, idg.reactome.org, by extending the Reactome web application with new features illustrating these proteins together with tissue-specific protein and gene expression levels and drug interactions.
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