Статті в журналах: "Interaction similarity"

1

Wade, R. C., R. R. Gabdoulline, and F. De Rienzo. "Protein interaction property similarity analysis." International Journal of Quantum Chemistry 83, no. 3-4 (2001): 122–27. http://dx.doi.org/10.1002/qua.1204.

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2

Trøjelsgaard, Kristian, Pedro Jordano, Daniel W. Carstensen, and Jens M. Olesen. "Geographical variation in mutualistic networks: similarity, turnover and partner fidelity." Proceedings of the Royal Society B: Biological Sciences 282, no. 1802 (March 7, 2015): 20142925. http://dx.doi.org/10.1098/rspb.2014.2925.

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Although species and their interactions in unison represent biodiversity and all the ecological and evolutionary processes associated with life, biotic interactions have, contrary to species, rarely been integrated into the concepts of spatial β-diversity. Here, we examine β-diversity of ecological networks by using pollination networks sampled across the Canary Islands. We show that adjacent and distant communities are more and less similar, respectively, in their composition of plants, pollinators and interactions than expected from random distributions. We further show that replacement of species is the major driver of interaction turnover and that this contribution increases with distance. Finally, we quantify that species-specific partner compositions (here called partner fidelity) deviate from random partner use, but vary as a result of ecological and geographical variables. In particular, breakdown of partner fidelity was facilitated by increasing geographical distance, changing abundances and changing linkage levels, but was not related to the geographical distribution of the species. This highlights the importance of space when comparing communities of interacting species and may stimulate a rethinking of the spatial interpretation of interaction networks. Moreover, geographical interaction dynamics and its causes are important in our efforts to anticipate effects of large-scale changes, such as anthropogenic disturbances.

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3

Islam, Sumaiya, and Robert J. Pantazes. "Developing similarity matrices for antibody-protein binding interactions." PLOS ONE 18, no. 10 (October 26, 2023): e0293606. http://dx.doi.org/10.1371/journal.pone.0293606.

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The inventions of AlphaFold and RoseTTAFold are revolutionizing computational protein science due to their abilities to reliably predict protein structures. Their unprecedented successes are due to the parallel consideration of several types of information, one of which is protein sequence similarity information. Sequence homology has been studied for many decades and depends on similarity matrices to define how similar or different protein sequences are to one another. A natural extension of predicting protein structures is predicting the interactions between proteins, but similarity matrices for protein-protein interactions do not exist. This study conducted a mutational analysis of 384 non-redundant antibody–protein antigen complexes to calculate antibody-protein interaction similarity matrices. Every important residue in each antibody and each antigen was mutated to each of the other 19 commonly occurring amino acids and the percentage changes in interaction energies were calculated using three force fields: CHARMM, Amber, and Rosetta. The data were used to construct six interaction similarity matrices, one for antibodies and another for antigens using each force field. The matrices exhibited both commonalities, such as mutations of aromatic and charged residues being the most detrimental, and differences, such as Rosetta predicting mutations of serines to be better tolerated than either Amber or CHARMM. A comparison to nine previously published similarity matrices for protein sequences revealed that the new interaction matrices are more similar to one another than they are to any of the previous matrices. The created similarity matrices can be used in force field specific applications to help guide decisions regarding mutations in protein-protein binding interfaces.

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4

TERASHIMA, Chieko, Yoshiaki TANIDA, Toshio MANABE, and Hiroyuki SATO. "The Correlation between Similarity of Amino Acid Interaction Potentials and Structure Similarity." Journal of Computer Chemistry, Japan 20, no. 4 (2021): 144–46. http://dx.doi.org/10.2477/jccj.2022-0003.

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5

Guéguen, Nicolas, Angélique Martin, and Sébastien Meineri. "Similarity and Social Interaction: When Similarity Fosters Implicit Behavior Toward a Stranger." Journal of Social Psychology 151, no. 6 (November 2011): 671–73. http://dx.doi.org/10.1080/00224545.2010.522627.

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6

Miquel de Caceres, J. Villa, J. J. Lozano, and F. Sanz. "MIPSIM: similarity analysis of molecular interaction potentials." Bioinformatics 16, no. 6 (June 1, 2000): 568–69. http://dx.doi.org/10.1093/bioinformatics/16.6.568.

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7

Hamill, David N., and S. Joseph Wright. "Interspecific Interaction and Similarity in Species Composition." American Naturalist 131, no. 3 (March 1988): 412–23. http://dx.doi.org/10.1086/284798.

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8

Lukatsky, D. B., B. E. Shakhnovich, J. Mintseris, and E. I. Shakhnovich. "Structural Similarity Enhances Interaction Propensity of Proteins." Journal of Molecular Biology 365, no. 5 (February 2007): 1596–606. http://dx.doi.org/10.1016/j.jmb.2006.11.020.

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9

López, Daniela N., Patricio A. Camus, Nelson Valdivia, and Sergio A. Estay. "Integrating species and interactions into similarity metrics: a graph theory-based approach to understanding community similarity." PeerJ 7 (May 31, 2019): e7013. http://dx.doi.org/10.7717/peerj.7013.

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Community similarity is often assessed through similarities in species occurrences and abundances (i.e., compositional similarity) or through the distribution of species interactions (i.e., interaction similarity). Unfortunately, the joint empirical evaluation of both is still a challenge. Here, we analyze community similarity in ecological systems in order to evaluate the extent to which indices based exclusively on species composition differ from those that incorporate species interactions. Borrowing tools from graph theory, we compared the classic Jaccard index with the graph edit distance (GED), a metric that allowed us to combine species composition and interactions. We found that similarity measures computed using only taxonomic composition could differ strongly from those that include composition and interactions. We conclude that new indices that incorporate community features beyond composition will be more robust for assessing similitude between natural systems than those purely based on species occurrences. Our results have therefore important conceptual and practical consequences for the analysis of ecological communities.

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10

Yan, Xiao-Ying, Shao-Wu Zhang, and Song-Yao Zhang. "Prediction of drug–target interaction by label propagation with mutual interaction information derived from heterogeneous network." Molecular BioSystems 12, no. 2 (2016): 520–31. http://dx.doi.org/10.1039/c5mb00615e.

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By implementing label propagation on drug/target similarity network with mutual interaction information derived from drug–target heterogeneous network, LPMIHN algorithm identifies potential drug–target interactions.

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11

Ta, Vivian P., and William Ickes. "Latent Semantic Similarity in Initial Computer-Mediated Interactions." International Journal of Interactive Communication Systems and Technologies 10, no. 1 (January 2020): 51–71. http://dx.doi.org/10.4018/ijicst.2020010104.

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The development of latent semantic similarity (LSS; the extent to which interaction partners use words in the same way) was investigated in the initial computer-mediated interactions of 120 same-sex dyads in Study 1 and 111 same-sex dyads in Study 2. The significant effects in Study 2 replicated those obtained in Study 1. In both studies, the female-female dyads achieved higher LSS than the male-male dyads. Across all dyads, LSS decreased—rather than increased—over time. Comparisons of word usage over the course of the interactions suggested that the dyads were more motivated to achieve higher levels of LSS during the earliest phase of their initial interaction, but that this motivation tended to wane over time. An exception to this trend occurred in high extraversion dyads, where the level of LSS remained relatively high and consistent across the three time periods studied. A motivational interpretation of these findings is both plausible and parsimonious, and the present study is—to the best of our knowledge—the first to find evidence of motivational influences on LSS.

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12

Lin, Xiaoli, Shuai Xu, Xuan Liu, Xiaolong Zhang, and Jing Hu. "Detecting Drug–Target Interactions with Feature Similarity Fusion and Molecular Graphs." Biology 11, no. 7 (June 27, 2022): 967. http://dx.doi.org/10.3390/biology11070967.

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The key to drug discovery is the identification of a target and a corresponding drug compound. Effective identification of drug–target interactions facilitates the development of drug discovery. In this paper, drug similarity and target similarity are considered, and graphical representations are used to extract internal structural information and intermolecular interaction information about drugs and targets. First, drug similarity and target similarity are fused using the similarity network fusion (SNF) method. Then, the graph isomorphic network (GIN) is used to extract the features with information about the internal structure of drug molecules. For target proteins, feature extraction is carried out using TextCNN to efficiently capture the features of target protein sequences. Three different divisions (CVD, CVP, CVT) are used on the standard dataset, and experiments are carried out separately to validate the performance of the model for drug–target interaction prediction. The experimental results show that our method achieves better results on AUC and AUPR. The docking results also show the superiority of the proposed model in predicting drug–target interactions.

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13

Chowdhury, Archana, Pratyusha Rakshit, and Amit Konar. "Prediction of protein–protein interaction network using a multi-objective optimization approach." Journal of Bioinformatics and Computational Biology 14, no. 03 (June 2016): 1650008. http://dx.doi.org/10.1142/s0219720016500086.

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Protein–Protein Interactions (PPIs) are very important as they coordinate almost all cellular processes. This paper attempts to formulate PPI prediction problem in a multi-objective optimization framework. The scoring functions for the trial solution deal with simultaneous maximization of functional similarity, strength of the domain interaction profiles, and the number of common neighbors of the proteins predicted to be interacting. The above optimization problem is solved using the proposed Firefly Algorithm with Nondominated Sorting. Experiments undertaken reveal that the proposed PPI prediction technique outperforms existing methods, including gene ontology-based Relative Specific Similarity, multi-domain-based Domain Cohesion Coupling method, domain-based Random Decision Forest method, Bagging with REP Tree, and evolutionary/swarm algorithm-based approaches, with respect to sensitivity, specificity, and F1 score.

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14

Liu, Di, Daniel Percival, and Stephen Fienberg. "User Interest and Interaction Structure in Online Forums." Proceedings of the International AAAI Conference on Web and Social Media 4, no. 1 (May 16, 2010): 283–86. http://dx.doi.org/10.1609/icwsm.v4i1.14059.

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We present a new similarity measure tailored to posts in an online forum. Our measure takes into account all the available information about user interest and interaction — the content of posts, the threads in the forum, and the author of the posts. We use this post similarity to build a similarity between users, based on principal coordinate analysis. This allows easy visualization of the user activity as well. Similarity between users has numerous applications, such as clustering or classification. We show that including the author of a post in the post similarity has a smoothing effect on principal coordinate projections. We demonstrate our method on real data drawn from an internal corporate forum, and compare our results to those given by a standard document classification method. We conclude our method gives a more detailed picture of both the local and global network structure.

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15

Hasnita, Hasnita, Farit Mochamad Afendi, and Anwar Fitrianto. "PERBANDINGAN BEBERAPA METODE KLASIFIKASI DALAM MEMPREDIKSI INTERAKSI FARMAKODINAMIK." Indonesian Journal of Statistics and Its Applications 4, no. 1 (February 28, 2020): 11–21. http://dx.doi.org/10.29244/ijsa.v4i1.328.

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One mechanism for Drug-Drug Interaction (DDI) is pharmacodynamic (PD) interactions. They are interactions by which the effects of a drug are changed by other drugs at the site of receptor. The interactions can be predicted based on Side Effects Similarity (SES), Chemical Similarity (CS) and Target Protein Connectedness (TPC). This study aims to find the best classification technique by first applying the scaling process, variable interaction, discretization and resampling technique. We used Random Forest, Support Vector Machines (SVM) and Binary Logistic Regression for the classification. Out the three classification methods, we found the SVM classification method produces the highest Area Under Cover (AUC) value compared to the other, which is 67.91%.

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16

Vilar, Santiago, Rave Harpaz, Eugenio Uriarte, Lourdes Santana, Raul Rabadan, and Carol Friedman. "Drug—drug interaction through molecular structure similarity analysis." Journal of the American Medical Informatics Association 19, no. 6 (November 2012): 1066–74. http://dx.doi.org/10.1136/amiajnl-2012-000935.

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17

Zhao, Nan, Bin Pang, Chi-Ren Shyu, and Dmitry Korkin. "Structural Similarity and Classification of Protein Interaction Interfaces." PLoS ONE 6, no. 5 (May 12, 2011): e19554. http://dx.doi.org/10.1371/journal.pone.0019554.

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18

Penner, Orion, Vishal Sood, Gabriel Musso, Kim Baskerville, Peter Grassberger, and Maya Paczuski. "Node similarity within subgraphs of protein interaction networks." Physica A: Statistical Mechanics and its Applications 387, no. 14 (June 2008): 3801–10. http://dx.doi.org/10.1016/j.physa.2008.02.043.

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19

Kamimura, Ryotaro. "Similarity interaction in information-theoretic self-organizing maps." International Journal of General Systems 42, no. 3 (April 2013): 239–67. http://dx.doi.org/10.1080/03081079.2012.723209.

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20

Lin, C. Y., and C. S. Lin. "Investigation of genotype-environment interaction by cluster analysis in animal experiments." Canadian Journal of Animal Science 74, no. 4 (December 1, 1994): 607–12. http://dx.doi.org/10.4141/cjas94-089.

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The conventional ANOVA (F ratio of GE interaction mean squares to error mean square) provides a means to test if GE interaction is significant, but it does not tell us which factor levels are significantly different or how they are interacting. To answer the latter question, plant researchers developed a technique to group genotypes for similarity of GE interactions and through the resulting groups to explore the GE interaction structure. The basic idea of the technique is to stratify genotypes (or environments) into subgroups such that GE interactions among genotypes (or environments) are homogeneous within groups but heterogeneous among groups. This technique is introduced in this paper using an animal experiment as an example for illustration. The possibilities and limitations of applying this technique to animal data are also discussed. Key words: Genotype-environment interaction, cluster analysis

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21

Melkus, Gatis, Peteris Rucevskis, Edgars Celms, Kārlis Čerāns, Karlis Freivalds, Paulis Kikusts, Lelde Lace, Mārtiņš Opmanis, Darta Rituma, and Juris Viksna. "Network motif-based analysis of regulatory patterns in paralogous gene pairs." Journal of Bioinformatics and Computational Biology 18, no. 03 (June 2020): 2040008. http://dx.doi.org/10.1142/s0219720020400089.

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Current high-throughput experimental techniques make it feasible to infer gene regulatory interactions at the whole-genome level with reasonably good accuracy. Such experimentally inferred regulatory networks have become available for a number of simpler model organisms such as S. cerevisiae, and others. The availability of such networks provides an opportunity to compare gene regulatory processes at the whole genome level, and in particular, to assess similarity of regulatory interactions for homologous gene pairs either from the same or from different species. We present here a new technique for analyzing the regulatory interaction neighborhoods of paralogous gene pairs. Our central focus is the analysis of S. cerevisiae gene interaction graphs, which are of particular interest due to the ancestral whole-genome duplication (WGD) that allows to distinguish between paralogous transcription factors that are traceable to this duplication event and other paralogues. Similar analysis is also applied to E. coli and C. elegans networks. We compare paralogous gene pairs according to the presence and size of bi-fan arrays, classically associated in the literature with gene duplication, within other network motifs. We further extend this framework beyond transcription factor comparison to obtain topology-based similarity metrics based on the overlap of interaction neighborhoods applicable to most genes in a given organism. We observe that our network divergence metrics show considerably larger similarity between paralogues, especially those traceable to WGD. This is the case for both yeast and C. elegans, but not for E. coli regulatory network. While there is no obvious cross-species link between metrics, different classes of paralogues show notable differences in interaction overlap, with traceable duplications tending toward higher overlap compared to genes with shared protein families. Our findings indicate that divergence in paralogous interaction networks reflects a shared genetic origin, and that our approach may be useful for investigating structural similarity in the interaction networks of paralogous genes.

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22

Chen, Yifei, Yuxing Sun, and Bing-Qing Han. "Improving Classification of Protein Interaction Articles Using Context Similarity-Based Feature Selection." BioMed Research International 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/751646.

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Protein interaction article classification is a text classification task in the biological domain to determine which articles describe protein-protein interactions. Since the feature space in text classification is high-dimensional, feature selection is widely used for reducing the dimensionality of features to speed up computation without sacrificing classification performance. Many existing feature selection methods are based on the statistical measure of document frequency and term frequency. One potential drawback of these methods is that they treat features separately. Hence, first we design a similarity measure between the context information to take word cooccurrences and phrase chunks around the features into account. Then we introduce the similarity of context information to the importance measure of the features to substitute the document and term frequency. Hence we propose new context similarity-based feature selection methods. Their performance is evaluated on two protein interaction article collections and compared against the frequency-based methods. The experimental results reveal that the context similarity-based methods perform better in terms of theF1measure and the dimension reduction rate. Benefiting from the context information surrounding the features, the proposed methods can select distinctive features effectively for protein interaction article classification.

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23

Hong, Fuxing, Dongbo Huang, and Ge Chen. "Interaction-Aware Factorization Machines for Recommender Systems." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3804–11. http://dx.doi.org/10.1609/aaai.v33i01.33013804.

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Factorization Machine (FM) is a widely used supervised learning approach by effectively modeling of feature interactions. Despite the successful application of FM and its many deep learning variants, treating every feature interaction fairly may degrade the performance. For example, the interactions of a useless feature may introduce noises; the importance of a feature may also differ when interacting with different features. In this work, we propose a novel model named Interaction-aware Factorization Machine (IFM) by introducing Interaction-Aware Mechanism (IAM), which comprises the feature aspect and the field aspect, to learn flexible interactions on two levels. The feature aspect learns feature interaction importance via an attention network while the field aspect learns the feature interaction effect as a parametric similarity of the feature interaction vector and the corresponding field interaction prototype. IFM introduces more structured control and learns feature interaction importance in a stratified manner, which allows for more leverage in tweaking the interactions on both feature-wise and field-wise levels. Besides, we give a more generalized architecture and propose Interaction-aware Neural Network (INN) and DeepIFM to capture higher-order interactions. To further improve both the performance and efficiency of IFM, a sampling scheme is developed to select interactions based on the field aspect importance. The experimental results from two well-known datasets show the superiority of the proposed models over the state-of-the-art methods.

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24

Xie, Bingjun, Jia Zhou, and Huilin Wang. "How Influential Are Mental Models on Interaction Performance? Exploring the Gap between Users’ and Designers’ Mental Models through a New Quantitative Method." Advances in Human-Computer Interaction 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/3683546.

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The objective of this study is to investigate the effect of the gap between two different mental models on interaction performance through a quantitative way. To achieve that, an index called mental model similarity and a new method called path diagram to elicit mental models were introduced. There are two kinds of similarity: directionless similarity calculated from card sorting and directional similarity calculated from path diagram. An experiment was designed to test their influence. A total of 32 college students participated and their performance was recorded. Through mathematical analysis of the results, three findings were derived. Frist, the more complex the information structures, the lower the directional similarity. Second, directional similarity (rather than directionless similarity) had significant influence on user performance, indicating that it is more effective in eliciting mental models using path diagram than card sorting. Third, the relationship between information structures and user performance was partially mediated by directional similarity. Our findings provide practitioners with a new perspective of bridging the gap between users’ and designers’ mental models.

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25

Wang, Mengran, Johanna B. Withers, Piero Ricchiuto, Ivan Voitalov, Michael McAnally, Helia N. Sanchez, Alif Saleh, Viatcheslav R. Akmaev, and Susan Dina Ghiassian. "A systems-based method to repurpose marketed therapeutics for antiviral use: a SARS-CoV-2 case study." Life Science Alliance 4, no. 5 (February 16, 2021): e202000904. http://dx.doi.org/10.26508/lsa.202000904.

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This study describes two complementary methods that use network-based and sequence similarity tools to identify drug repurposing opportunities predicted to modulate viral proteins. This approach could be rapidly adapted to new and emerging viruses. The first method built and studied a virus–host–physical interaction network; a three-layer multimodal network of drug target proteins, human protein–protein interactions, and viral–host protein–protein interactions. The second method evaluated sequence similarity between viral proteins and other proteins, visualized by constructing a virus–host–similarity interaction network. Methods were validated on the human immunodeficiency virus, hepatitis B, hepatitis C, and human papillomavirus, then deployed on SARS-CoV-2. Comparison of virus–host–physical interaction predictions to known antiviral drugs had AUCs of 0.69, 0.59, 0.78, and 0.67, respectively, reflecting that the scores are predictive of effective drugs. For SARS-CoV-2, 569 candidate drugs were predicted, of which 37 had been included in clinical trials for SARS-CoV-2 (AUC = 0.75, P-value 3.21 × 10−3). As further validation, top-ranked candidate antiviral drugs were analyzed for binding to protein targets in silico; binding scores generated by BindScope indicated a 70% success rate.

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26

Beven, K. J., and S. W. Franks. "Functional similarity in landscape scale SVAT modelling." Hydrology and Earth System Sciences 3, no. 1 (March 31, 1999): 85–93. http://dx.doi.org/10.5194/hess-3-85-1999.

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Abstract. In this study, it is shown that the complexity of Soil Vegetation Atmosphere Transfer (SVAT) models leads to an equifinality of functional behaviour - many parameterizations from many areas of the parameter space lead to very similar responses. Individual parameters derived by calibration (i.e. model inversion) against limited measurements are, therefore, highly uncertain. Due to the non-linear internal behaviour of SVAT models, aggregation of uncertainly known parameter fields to parameterize landscape scale variability in surface fluxes will yield highly uncertain predictions. A disaggregation approach suggested by Beven (1995) requires that the land surface be represented by a linear sum of a number of representative parameterizations or functional types. This study explores the nature of the parameter space in terms of a simple definition of functional behaviour. Parameter interactions producing similar predicted behaviours are investigated through application of Principal Component Analyses. These reveal the lack of a dominant global interaction indicating the presence of highly complex parameter interactions throughout the feasible parameter space.

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27

Herjanto, Halimin, and Muslim Amin. "Repurchase intention: the effect of similarity and client knowledge." International Journal of Bank Marketing 38, no. 6 (July 13, 2020): 1351–71. http://dx.doi.org/10.1108/ijbm-03-2020-0108.

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PurposeThe objective of this study was to investigate the effect of appearance, lifestyle and status similarity on interaction intensity, satisfaction with a banker and repurchase intention. Also examined was the moderating effect of client knowledge in the enhancement of customer satisfaction with a banker.Design/methodology/approachA total of 800 questionnaires using the snowball sampling technique were performed to distribute the questionnaires to bank customers at different ethnic community centers in New Zealand. A total of 377 useable questionnaires were collected for further analysis.FindingsThe findings indicated that the three types of similarity affect interaction intensity differently. Lifestyle similarity was found to positively influence interaction intensity. The similarity constructs of appearance and status were found to have an insignificant relationship with interaction intensity. The findings show that appearance similarity and interaction intensity are able to enhance customer satisfaction with a banker. Customer satisfaction with a banker has a significant relationship with repurchase intention. Client knowledge influences the degree of interaction intensity and satisfaction with a banker.Practical implicationsThe findings of this study help bankers to understand the importance of their similarities with a customer and to design recruitment strategies and training sections to improve customer satisfaction.Originality/valueThis study contributes to the body of knowledge by incorporating interaction intensity, similarity and satisfaction with a bank into the repurchase intention model.

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28

Wegner, J. L., L. Jiang, and J. B. Haddow. "On the Interaction and Reflection of Shocks in Hyperelastic Strings." Journal of Applied Mechanics 58, no. 2 (June 1, 1991): 554–58. http://dx.doi.org/10.1115/1.2897219.

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Governing equations for finite amplitude wave propagation in stretched hyperelastic strings are given in recent papers, (Beatty and Haddow, 1985), along with similarity solutions for symmetrically plucked and impacted strings. The similarity solutions are valid until the first reflections at the fixed ends and in this paper we consider symmetrically plucked Mooney-Rivlin strings and investigate the response after reflections. The method of characteristics is applied to extend the results of the similarity solutions and to obtain solutions for the interaction of a reflected longitudinal shock and incident transverse shock and the reflection of an incident transverse shock. A deformed shape, which is not intuitively obvious, is predicted by the solution of the interaction problem and is confirmed by an experimental study. A finite difference scheme is used to obtain numerical solutions, which are valid after multiple wave interactions and reflections occur. Solutions obtained by the method of characteristics are used as a partial check on the numerical results.

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29

Jamali, Ali Akbar, Anthony Kusalik, and Fang-Xiang Wu. "MDIPA: a microRNA–drug interaction prediction approach based on non-negative matrix factorization." Bioinformatics 36, no. 20 (June 17, 2020): 5061–67. http://dx.doi.org/10.1093/bioinformatics/btaa577.

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Abstract Motivation Evidence has shown that microRNAs, one type of small biomolecule, regulate the expression level of genes and play an important role in the development or treatment of diseases. Drugs, as important chemical compounds, can interact with microRNAs and change their functions. The experimental identification of microRNA–drug interactions is time-consuming and expensive. Therefore, it is appealing to develop effective computational approaches for predicting microRNA–drug interactions. Results In this study, a matrix factorization-based method, called the microRNA–drug interaction prediction approach (MDIPA), is proposed for predicting unknown interactions among microRNAs and drugs. Specifically, MDIPA utilizes experimentally validated interactions between drugs and microRNAs, drug similarity and microRNA similarity to predict undiscovered interactions. A path-based microRNA similarity matrix is constructed, while the structural information of drugs is used to establish a drug similarity matrix. To evaluate its performance, our MDIPA is compared with four state-of-the-art prediction methods with an independent dataset and cross-validation. The results of both evaluation methods confirm the superior performance of MDIPA over other methods. Finally, the results of molecular docking in a case study with breast cancer confirm the efficacy of our approach. In conclusion, MDIPA can be effective in predicting potential microRNA–drug interactions. Availability and implementation All code and data are freely available from https://github.com/AliJam82/MDIPA. Supplementary information Supplementary data are available at Bioinformatics online.

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30

Montes-Berges, Beatriz, and Miguel Moya. "Attitude Similarity and Stereotypicality in Leader Evaluation." Spanish journal of psychology 12, no. 1 (May 2009): 258–66. http://dx.doi.org/10.1017/s1138741600001669.

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Stereotypicality and attitudinal similarity are variables broadly studied in the research about leader's acceptance and evaluation. However, the interaction between these variables has not been deeply studied. An experimental research in which we analyze the influence of both variables and their interaction on leaders' evaluation is presented. A3 × 3 (attitudinal similarity [none, moderate, high] × leaders' stereotypicality [none, moderately and very stereotypical]) design was used. Participants were 215 Psychology students. Results show that both variables influenced leaders' evaluation, although the influence of stereotypicality was stronger than that of attitude similarity. The significant interaction between both variables indicates that, when a very stereotypical leader is not at all similar or moderately similar to the perceiver, his or her evaluation diminishes.

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31

Zhang, Wen, Weiran Lin, Ding Zhang, Siman Wang, Jingwen Shi, and Yanqing Niu. "Recent Advances in the Machine Learning-Based Drug-Target Interaction Prediction." Current Drug Metabolism 20, no. 3 (May 22, 2019): 194–202. http://dx.doi.org/10.2174/1389200219666180821094047.

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Background:The identification of drug-target interactions is a crucial issue in drug discovery. In recent years, researchers have made great efforts on the drug-target interaction predictions, and developed databases, software and computational methods.Results:In the paper, we review the recent advances in machine learning-based drug-target interaction prediction. First, we briefly introduce the datasets and data, and summarize features for drugs and targets which can be extracted from different data. Since drug-drug similarity and target-target similarity are important for many machine learning prediction models, we introduce how to calculate similarities based on data or features. Different machine learningbased drug-target interaction prediction methods can be proposed by using different features or information. Thus, we summarize, analyze and compare different machine learning-based prediction methods.Conclusion:This study provides the guide to the development of computational methods for the drug-target interaction prediction.

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32

Gillies, Christopher E., Xiaoli Gao, Nilesh V. Patel, Mohammad-Reza Siadat, and George D. Wilson. "Improved Feature Selection by Incorporating Gene Similarity into the LASSO." International Journal of Knowledge Discovery in Bioinformatics 3, no. 1 (January 2012): 1–22. http://dx.doi.org/10.4018/jkdb.2012010101.

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Personalized medicine is customizing treatments to a patient’s genetic profile and has the potential to revolutionize medical practice. An important process used in personalized medicine is gene expression profiling. Analyzing gene expression profiles is difficult, because there are usually few patients and thousands of genes, leading to the curse of dimensionality. To combat this problem, researchers suggest using prior knowledge to enhance feature selection for supervised learning algorithms. The authors propose an enhancement to the LASSO, a shrinkage and selection technique that induces parameter sparsity by penalizing a model’s objective function. Their enhancement gives preference to the selection of genes that are involved in similar biological processes. The authors’ modified LASSO selects similar genes by penalizing interaction terms between genes. They devise a coordinate descent algorithm to minimize the corresponding objective function. To evaluate their method, the authors created simulation data where they compared their model to the standard LASSO model and an interaction LASSO model. The authors’ model outperformed both the standard and interaction LASSO models in terms of detecting important genes and gene interactions for a reasonable number of training samples. They also demonstrated the performance of their method on a real gene expression data set from lung cancer cell lines.

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Zhu, Lin, Su-Ping Deng, Zhu-Hong You, and De-Shuang Huang. "Identifying Spurious Interactions in the Protein-Protein Interaction Networks Using Local Similarity Preserving Embedding." IEEE/ACM Transactions on Computational Biology and Bioinformatics 14, no. 2 (March 1, 2017): 345–52. http://dx.doi.org/10.1109/tcbb.2015.2407393.

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Deshpande, Raamesh, Benjamin VanderSluis, and Chad L. Myers. "Comparison of Profile Similarity Measures for Genetic Interaction Networks." PLoS ONE 8, no. 7 (July 10, 2013): e68664. http://dx.doi.org/10.1371/journal.pone.0068664.

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Schmidt, Thomas Sebastian Benedikt, João Frederico Matias Rodrigues, and Christian von Mering. "A family of interaction-adjusted indices of community similarity." ISME Journal 11, no. 3 (December 9, 2016): 791–807. http://dx.doi.org/10.1038/ismej.2016.139.

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36

Bonikowski, Bart. "Cross-national interaction and cultural similarity: A relational analysis." International Journal of Comparative Sociology 51, no. 5 (September 27, 2010): 315–48. http://dx.doi.org/10.1177/0020715210376854.

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Capobianchi, Maria Rosaria, Elena Uleri, Claudia Caglioti, and Antonina Dolei. "Type I IFN family members: Similarity, differences and interaction." Cytokine & Growth Factor Reviews 26, no. 2 (April 2015): 103–11. http://dx.doi.org/10.1016/j.cytogfr.2014.10.011.

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Wyman, Douglas R., Michael S. Patterson, and Brian C. Wilson. "Similarity relations for the interaction parameters in radiation transport." Applied Optics 28, no. 24 (December 15, 1989): 5243. http://dx.doi.org/10.1364/ao.28.005243.

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Li, Chunhong, DaPeng Xu, Rob Law, and Xudong Liu. "Cultural similarity and guest-host interaction for virtual tourism." Journal of Hospitality and Tourism Management 58 (March 2024): 11–15. http://dx.doi.org/10.1016/j.jhtm.2023.11.007.

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Warta, Samantha F., Katelynn A. Kapalo, Andrew Best, and Stephen M. Fiore. "Similarity, Complementarity, and Agency in HRI." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 60, no. 1 (September 2016): 1230–34. http://dx.doi.org/10.1177/1541931213601287.

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Robotic teammates are becoming prevalent in increasingly complex and dynamic operational and social settings. For this reason, the perception of robots operating in such environments has transitioned from the perception of robots as tools, extending human capabilities, to the perception of robots as teammates, collaborating with humans and displaying complex social cognitive processes. The goal of this paper is to introduce a discussion on an integrated set of robotic design elements, as well as provide support for the idea that human-robot interaction requires a clearer understanding of social cognitive constructs to optimize human-robot collaboration. We develop a set of research questions addressing these constructs with the goal of improving the engineering of artificial cognitive systems reliant on natural human-robot interaction.

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Luo, Haiqiong, Wei Lan, Qingfeng Chen, Zhiqiang Wang, Zhixian Liu, Xiaofeng Yue, and Lingzhi Zhu. "Inferring microRNA-Environmental Factor Interactions Based on Multiple Biological Information Fusion." Molecules 23, no. 10 (September 24, 2018): 2439. http://dx.doi.org/10.3390/molecules23102439.

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Accumulated studies have shown that environmental factors (EFs) can regulate the expression of microRNA (miRNA) which is closely associated with several diseases. Therefore, identifying miRNA-EF associations can facilitate the study of diseases. Recently, several computational methods have been proposed to explore miRNA-EF interactions. In this paper, a novel computational method, MEI-BRWMLL, is proposed to uncover the relationship between miRNA and EF. The similarities of miRNA-miRNA are calculated by using miRNA sequence, miRNA-EF interaction, and the similarities of EF-EF are calculated based on the anatomical therapeutic chemical information, chemical structure and miRNA-EF interaction. The similarity network fusion is used to fuse the similarity between miRNA and the similarity between EF, respectively. Further, the multiple-label learning and bi-random walk are employed to identify the association between miRNA and EF. The experimental results show that our method outperforms the state-of-the-art algorithms.

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Morales-Bayuelo, Alejandro, and Ricardo Vivas-Reyes. "Theoretical Calculations and Modeling for the Molecular Polarization of Furan and Thiophene under the Action of an Electric Field Using Quantum Similarity." Journal of Quantum Chemistry 2014 (March 17, 2014): 1–10. http://dx.doi.org/10.1155/2014/585394.

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A theoretical study on the molecular polarization of thiophene and furan under the action of an electric field using Local Quantum Similarity Indexes (LQSI) was performed. This model is based on Hirshfeld partitioning of electron density within the framework of Density Functional Theory (DFT). Six local similarity indexes were used: overlap, overlap-interaction, coulomb, coulomb-interaction, Euclidian distances of overlap, and Euclidean distances of coulomb. In addition Topo-Geometrical Superposition Algorithm (TGSA) was used as a method of alignment. This method provides a straightforward procedure to solve the problem of molecular relative orientation. It provides a tool to evaluate molecular quantum similarity, enabling the study of structural systems, which differ in only one atom such as thiophene and furan (point group C2v) and cyclopentadienyl molecule (point group D5h). Additionally, this model can contribute to the interpretation of chemical bonds, and molecular interactions in the framework of the solvent effect theory.

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43

Guney, Emre. "Revisiting Cross-Validation of Drug Similarity Based Classifiers Using Paired Data." Genomics and Computational Biology 4, no. 1 (December 6, 2017): 100047. http://dx.doi.org/10.18547/gcb.2018.vol4.iss1.e100047.

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Following the recent availability of high-throughput data for drug discovery, computational methods, especially machine learning based approaches, have gained remarkable attention. A number of studies use chemical, target and side effect similarity between drugs to build knowledge-based models that predict drug indications and drug-drug interactions. In light of previous works demonstrating the perils of cross-validation using paired data, in this study, we employ a disjoint cross validation approach for similarity-based drug-drug interaction (DDI) prediction and we investigate the prediction accuracy of classifier under various settings. Our results point to the dependence on the cross validation strategy used to evaluate prediction accuracy of drug similarity-based classifiers operating on paired data such as pharmacokinetic interactions between drugs.

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Kazemi-Pour, Ali, Bahram Goliaei, and Hamid Pezeshk. "Protein Complex Discovery by Interaction Filtering from Protein Interaction Networks Using Mutual Rank Coexpression and Sequence Similarity." BioMed Research International 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/165186.

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The evaluation of the biological networks is considered the essential key to understanding the complex biological systems. Meanwhile, the graph clustering algorithms are mostly used in the protein-protein interaction (PPI) network analysis. The complexes introduced by the clustering algorithms include noise proteins. The error rate of the noise proteins in the PPI network researches is about 40–90%. However, only 30–40% of the existing interactions in the PPI databases depend on the specific biological function. It is essential to eliminate the noise proteins and the interactions from the complexes created via clustering methods. We have introduced new methods of weighting interactions in protein clusters and the splicing of noise interactions and proteins-based interactions on their weights. The coexpression and the sequence similarity of each pair of proteins are considered the edge weight of the proteins in the network. The results showed that the edge filtering based on the amount of coexpression acts similar to the node filtering via graph-based characteristics. Regarding the removal of the noise edges, the edge filtering has a significant advantage over the graph-based method. The edge filtering based on the amount of sequence similarity has the ability to remove the noise proteins and the noise interactions.

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45

Albacete, E., J. Calle, E. Castro, and D. Cuadra. "Semantic Similarity Measures Applied to an Ontology for Human-Like Interaction." Journal of Artificial Intelligence Research 44 (July 1, 2012): 397–421. http://dx.doi.org/10.1613/jair.3612.

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The focus of this paper is the calculation of similarity between two concepts from an ontology for a Human-Like Interaction system. In order to facilitate this calculation, a similarity function is proposed based on five dimensions (sort, compositional, essential, restrictive and descriptive) constituting the structure of ontological knowledge. The paper includes a proposal for computing a similarity function for each dimension of knowledge. Later on, the similarity values obtained are weighted and aggregated to obtain a global similarity measure. In order to calculate those weights associated to each dimension, four training methods have been proposed. The training methods differ in the element to fit: the user, concepts or pairs of concepts, and a hybrid approach. For evaluating the proposal, the knowledge base was fed from WordNet and extended by using a knowledge editing toolkit (Cognos). The evaluation of the proposal is carried out through the comparison of system responses with those given by human test subjects, both providing a measure of the soundness of the procedure and revealing ways in which the proposal may be improved.

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Wang, Shenglong, Jing Yang, Xiaoyu Ding, and Meng Zhao. "Detecting local communities in complex network via the optimization of interaction relationship between node and community." PeerJ Computer Science 9 (May 15, 2023): e1386. http://dx.doi.org/10.7717/peerj-cs.1386.

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The goal of local community detection algorithms is to explore the optimal community with a reference to a given node. Such algorithms typically include two primary processes: seed selection and community expansion. This study develops and tests a novel local community detection algorithm called OIRLCD that is based on the optimization of interaction relationships between nodes and the community. First, we introduce an improved seed selection method to solve the seed deviation problem. Second, this study uses a series of similarity indices to measure the interaction relationship between nodes and community. Third, this study uses a series of algorithms based on different similarity indices, and designs experiments to reveal the role of the similarity index in algorithms based on relationship optimization. The proposed algorithm was compared with five existing local community algorithms in both real-world networks and artificial networks. Experimental results show that the optimization of interaction relationship algorithms based on node similarity can detect communities accurately and efficiently. In addition, a good similarity index can highlight the advantages of the proposed algorithm based on interaction optimization.

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Chen, Mengqi, Jingyang Xia, Ruoyun Huang, and Weiguo Fang. "Case-Based Reasoning System for Aeroengine Fault Diagnosis Enhanced with Attitudinal Choquet Integral." Applied Sciences 12, no. 11 (June 3, 2022): 5696. http://dx.doi.org/10.3390/app12115696.

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As the core process of case-based reasoning (CBR), case retrieval is the foundation for CBR success, and the quality of case retrieval depends on the case similarity measure. We improved the CBR system for aeroengine fault diagnosis by embedding the attitudinal Choquet integral (ACI) and 2-order additive measure to consider attribute interactions and decision makers’ attitudes. The enhanced case retrieval method can not only integrate the local similarity, attribute importance, and interaction between attributes, but also incorporate the attitude of the decision maker, thus producing more comprehensive and reasonable global similarity and high-quality recommendations. An experimental study of aeroengine fault diagnosis and comparisons with other similarity aggregation methods were performed to demonstrate the effectiveness of the proposed method.

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Wu, Yangyang, Siying Wu, and Duansheng Chen. "Chinese-English Bilingual Word Semantic Similarity Based on Chinese WordNet." Journal of Software 10, no. 1 (January 2015): 20–31. http://dx.doi.org/10.17706/jsw.10.1.20-31.

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49

Chen, Shubai, Song Wu, and Li Wang. "Hierarchical semantic interaction-based deep hashing network for cross-modal retrieval." PeerJ Computer Science 7 (May 25, 2021): e552. http://dx.doi.org/10.7717/peerj-cs.552.

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Due to the high efficiency of hashing technology and the high abstraction of deep networks, deep hashing has achieved appealing effectiveness and efficiency for large-scale cross-modal retrieval. However, how to efficiently measure the similarity of fine-grained multi-labels for multi-modal data and thoroughly explore the intermediate layers specific information of networks are still two challenges for high-performance cross-modal hashing retrieval. Thus, in this paper, we propose a novel Hierarchical Semantic Interaction-based Deep Hashing Network (HSIDHN) for large-scale cross-modal retrieval. In the proposed HSIDHN, the multi-scale and fusion operations are first applied to each layer of the network. A Bidirectional Bi-linear Interaction (BBI) policy is then designed to achieve the hierarchical semantic interaction among different layers, such that the capability of hash representations can be enhanced. Moreover, a dual-similarity measurement (“hard” similarity and “soft” similarity) is designed to calculate the semantic similarity of different modality data, aiming to better preserve the semantic correlation of multi-labels. Extensive experiment results on two large-scale public datasets have shown that the performance of our HSIDHN is competitive to state-of-the-art deep cross-modal hashing methods.

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Glass, Kimberly, Edward Ott, Wolfgang Losert, and Michelle Girvan. "Implications of functional similarity for gene regulatory interactions." Journal of The Royal Society Interface 9, no. 72 (February 2012): 1625–36. http://dx.doi.org/10.1098/rsif.2011.0585.

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If one gene regulates another, those two genes are likely to be involved in many of the same biological functions. Conversely, shared biological function may be suggestive of the existence and nature of a regulatory interaction. With this in mind, we develop a measure of functional similarity between genes based on annotations made to the Gene Ontology in which the magnitude of their functional relationship is also indicative of a regulatory relationship. In contrast to other measures that have previously been used to quantify the functional similarity between genes, our measure scales the strength of any shared functional annotation by the frequency of that function's appearance across the entire set of annotations. We apply our method to both Escherichia coli and Saccharomyces cerevisiae gene annotations and find that the strength of our scaled similarity measure is more predictive of known regulatory interactions than previously published measures of functional similarity. In addition, we observe that the strength of the scaled similarity measure is correlated with the structural importance of links in the known regulatory network. By contrast, other measures of functional similarity are not indicative of any structural importance in the regulatory network. We therefore conclude that adequately adjusting for the frequency of shared biological functions is important in the construction of a functional similarity measure aimed at elucidating the existence and nature of regulatory interactions. We also compare the performance of the scaled similarity with a high-throughput method for determining regulatory interactions from gene expression data and observe that the ontology-based approach identifies a different subset of regulatory interactions compared with the gene expression approach. We show that combining predictions from the scaled similarity with those from the reconstruction algorithm leads to a significant improvement in the accuracy of the reconstructed network.

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