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Artykuły w czasopismach na temat „Interaction similarity”

Autor: Grafiati

Data publikacji: 16 listopada 2024

Data aktualizacji: 31 lipca 2025

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1

Hong-Yi, Zhang, and Zhou Yan. "GILPI: Graphlet Interaction - based lncRNA-Protein Interaction Prediction." International Journal of Engineering Research & Science (IJOER) 10, no. 7 (2024): 01–14. https://doi.org/10.5281/zenodo.13133077.

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Identification of lncRNA-protein interactions is important for understanding the biological functions and molecular mechanisms of lncRNAs. In this study, we proposed a computational model for predicting lncRNA-protein interactions based on Graphlet interactions to find potential LPIs (GILPI). First, five LPI datasets were collected. Second, vector features of lncRNAs and proteins were extracted from the sequence data by pyfeat and BioTriangle, respectively. Third, these features were subjected to Pearson's correlation coefficient to calculate the similarity between lncRNAs and the similarity b

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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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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 (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 s

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4

Islam, Sumaiya, and Robert J. Pantazes. "Developing similarity matrices for antibody-protein binding interactions." PLOS ONE 18, no. 10 (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

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Bursalıoğlu, Ertugrul, Şaban Kalay, and Sefa Metin. "Evaluation of gene interaction and similarity in 17 different cancer pathways." Journal of Research in Pharmacy 29, no. 2 (2025): 742–50. https://doi.org/10.12991/jrespharm.1664926.

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This study investigates similarities and gene interactions in 17 different cancer types using Kyoto University's KEGG cancer pathways. Using Python software and the Google Colab platform, gene similarities and interactions within cancer pathways were calculated through Jaccard similarity indices and interaction analyses. The results reveal important genes and pathways shared between cancer types, providing insights into common molecular mechanisms underlying cancer development and progression. These findings may contribute to the identification of potential therapeutic targets by understanding

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6

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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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 (2011): 671–73. http://dx.doi.org/10.1080/00224545.2010.522627.

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8

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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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

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10

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

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11

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

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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 (2007): 1596–606. http://dx.doi.org/10.1016/j.jmb.2006.11.020.

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13

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 (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

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14

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 (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 inf

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15

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 (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 predi

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16

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 (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 p

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17

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 (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

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18

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 (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 hom

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19

Melkus, Gatis, Peteris Rucevskis, Edgars Celms, et al. "Network motif-based analysis of regulatory patterns in paralogous gene pairs." Journal of Bioinformatics and Computational Biology 18, no. 03 (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

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20

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 (2012): 1066–74. http://dx.doi.org/10.1136/amiajnl-2012-000935.

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21

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

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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 (2008): 3801–10. http://dx.doi.org/10.1016/j.physa.2008.02.043.

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23

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

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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 des

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25

Herjanto, Halimin, and Muslim Amin. "Repurchase intention: the effect of similarity and client knowledge." International Journal of Bank Marketing 38, no. 6 (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.Fi

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Wang, Mengran, Johanna B. Withers, Piero Ricchiuto, et al. "A systems-based method to repurpose marketed therapeutics for antiviral use: a SARS-CoV-2 case study." Life Science Alliance 4, no. 5 (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–sim

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27

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 a

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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 analysi

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Beven, K. J., and S. W. Franks. "Functional similarity in landscape scale SVAT modelling." Hydrology and Earth System Sciences 3, no. 1 (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 di

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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 (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 learni

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31

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 (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

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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 (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 a

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Montes-Berges, Beatriz, and Miguel Moya. "Attitude Similarity and Stereotypicality in Leader Evaluation." Spanish journal of psychology 12, no. 1 (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, alth

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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 (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 pa

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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 (2017): 345–52. http://dx.doi.org/10.1109/tcbb.2015.2407393.

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Isnan, Mulia, Ananta Kusuma Wisnu, and Mochamad Afendi Farit. "Algorithm for Predicting Compound Protein Interaction Using Tanimoto Similarity and Klekota-roth Fingerprint." TELKOMNIKA Telecommunication, Computing, Electronics and Control 16, no. 4 (2018): 1785–92. https://doi.org/10.12928/TELKOMNIKA.v16i4.5916.

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This research aimed to develop a method for predicting interaction between chemical compounds contained in herbs and proteins related to particular disease. The algorithm of this method is based on binary local models algorithm, with protein similarity section is omitted. Klekota-Roth fingerprint is used for the compound's representation. In the development process of the method, three similarity functions are compared: Tanimoto, Cosine, and Dice. Youden’s index is used to evaluate optimum threshold value. The result showed that Tanimoto similarity function yielded higher similarity

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Luo, Haiqiong, Wei Lan, Qingfeng Chen, et al. "Inferring microRNA-Environmental Factor Interactions Based on Multiple Biological Information Fusion." Molecules 23, no. 10 (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 bas

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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 pro

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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 (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 socia

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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 (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 (2016): 791–807. http://dx.doi.org/10.1038/ismej.2016.139.

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Bonikowski, Bart. "Cross-national interaction and cultural similarity: A relational analysis." International Journal of Comparative Sociology 51, no. 5 (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 (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 (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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Kang, Yan, Hao Peng, Yue Peng, Jing Guo, and Ying Lin. "Tri-level interaction fusion network for graph similarity learning." Knowledge-Based Systems 326 (September 2025): 113969. https://doi.org/10.1016/j.knosys.2025.113969.

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Guney, Emre. "Revisiting Cross-Validation of Drug Similarity Based Classifiers Using Paired Data." Genomics and Computational Biology 4, no. 1 (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

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Lin, Pei. "HandDiffuse: Generative Controllers for Two-Hand Interactions via Diffusion Models." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 5 (2025): 5280–88. https://doi.org/10.1609/aaai.v39i5.32561.

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Existing hands datasets are largely short-range and the interaction is weak due to the self-occlusion and self-similarity of hands, which can not yet fit the need for interacting hands motion generation. To rescue the data scarcity, we propose HandDiffuse12.5M, a novel and real dataset that consists of temporal sequences with strong two-hand interactions. HandDiffuse12.5M has the largest scale and richest interactions among the existing two-hand datasets. We further present a strong baseline method HandDiffuse for the controllable motion generation of interacting hands using various controller

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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

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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 (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 comp

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