Journal articles: 'BIOLOGICAL LANGUAGE MODEL'

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Price, Carolyn. "Review: Language: A Biological Model." Mind 116, no. 463 (July 1, 2007): 766–69. http://dx.doi.org/10.1093/mind/fzm766.

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Duhau, Laura. "Ruth Garrett Millikan, Language: A Biological Model." Crítica (México D. F. En línea) 40, no. 118 (January 8, 2008): 109–15. http://dx.doi.org/10.22201/iifs.18704905e.2008.1026.

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Collins, John. "Language: a Biological Model ? Ruth Garrett Millikan." Philosophical Quarterly 57, no. 226 (January 2007): 142–45. http://dx.doi.org/10.1111/j.1467-9213.2007.476_5.x.

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Cameron, William. "Ruth Garrett Millikan, Language: A Biological Model." Minds and Machines 18, no. 1 (January 17, 2008): 127–31. http://dx.doi.org/10.1007/s11023-008-9088-4.

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woodfield, andrew. "Language: A Biological Model - by Ruth Garrett Millikan." Philosophical Books 48, no. 3 (July 2007): 279–81. http://dx.doi.org/10.1111/j.1468-0149.2007.00449_8.x.

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Wang, Yanbin, Zhu-Hong You, Shan Yang, Xiao Li, Tong-Hai Jiang, and Xi Zhou. "A High Efficient Biological Language Model for Predicting Protein–Protein Interactions." Cells 8, no. 2 (February 3, 2019): 122. http://dx.doi.org/10.3390/cells8020122.

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Many life activities and key functions in organisms are maintained by different types of protein–protein interactions (PPIs). In order to accelerate the discovery of PPIs for different species, many computational methods have been developed. Unfortunately, even though computational methods are constantly evolving, efficient methods for predicting PPIs from protein sequence information have not been found for many years due to limiting factors including both methodology and technology. Inspired by the similarity of biological sequences and languages, developing a biological language processing technology may provide a brand new theoretical perspective and feasible method for the study of biological sequences. In this paper, a pure biological language processing model is proposed for predicting protein–protein interactions only using a protein sequence. The model was constructed based on a feature representation method for biological sequences called bio-to-vector (Bio2Vec) and a convolution neural network (CNN). The Bio2Vec obtains protein sequence features by using a “bio-word” segmentation system and a word representation model used for learning the distributed representation for each “bio-word”. The Bio2Vec supplies a frame that allows researchers to consider the context information and implicit semantic information of a bio sequence. A remarkable improvement in PPIs prediction performance has been observed by using the proposed model compared with state-of-the-art methods. The presentation of this approach marks the start of “bio language processing technology,” which could cause a technological revolution and could be applied to improve the quality of predictions in other problems.

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Fitch, W. Tecumseh. "Unity and diversity in human language." Philosophical Transactions of the Royal Society B: Biological Sciences 366, no. 1563 (February 12, 2011): 376–88. http://dx.doi.org/10.1098/rstb.2010.0223.

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Human language is both highly diverse—different languages have different ways of achieving the same functional goals—and easily learnable. Any language allows its users to express virtually any thought they can conceptualize. These traits render human language unique in the biological world. Understanding the biological basis of language is thus both extremely challenging and fundamentally interesting. I review the literature on linguistic diversity and language universals, suggesting that an adequate notion of ‘formal universals’ provides a promising way to understand the facts of language acquisition, offering order in the face of the diversity of human languages. Formal universals are cross-linguistic generalizations, often of an abstract or implicational nature. They derive from cognitive capacities to perceive and process particular types of structures and biological constraints upon integration of the multiple systems involved in language. Such formal universals can be understood on the model of a general solution to a set of differential equations; each language is one particular solution. An explicit formal conception of human language that embraces both considerable diversity and underlying biological unity is possible, and fully compatible with modern evolutionary theory.

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Dickinson, Sandra. "Recursion in Development: Support for a Biological Model of Language." Language and Speech 30, no. 3 (July 1987): 239–49. http://dx.doi.org/10.1177/002383098703000304.

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Cuellar, Autumn A., Catherine M. Lloyd, Poul F. Nielsen, David P. Bullivant, David P. Nickerson, and Peter J. Hunter. "An Overview of CellML 1.1, a Biological Model Description Language." SIMULATION 79, no. 12 (December 2003): 740–47. http://dx.doi.org/10.1177/0037549703040939.

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Cardelli, Luca, Marta Kwiatkowska, and Luca Laurenti. "A Language for Modeling and Optimizing Experimental Biological Protocols." Computation 9, no. 10 (October 16, 2021): 107. http://dx.doi.org/10.3390/computation9100107.

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Automation is becoming ubiquitous in all laboratory activities, moving towards precisely defined and codified laboratory protocols. However, the integration between laboratory protocols and mathematical models is still lacking. Models describe physical processes, while protocols define the steps carried out during an experiment: neither cover the domain of the other, although they both attempt to characterize the same phenomena. We should ideally start from an integrated description of both the model and the steps carried out to test it, to concurrently analyze uncertainties in model parameters, equipment tolerances, and data collection. To this end, we present a language to model and optimize experimental biochemical protocols that facilitates such an integrated description, and that can be combined with experimental data. We provide probabilistic semantics for our language in terms of Gaussian processes (GPs) based on the linear noise approximation (LNA) that formally characterizes the uncertainties in the data collection, the underlying model, and the protocol operations. In a set of case studies, we illustrate how the resulting framework allows for automated analysis and optimization of experimental protocols, including Gibson assembly protocols.

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Song, Bosheng, Zimeng Li, Xuan Lin, Jianmin Wang, Tian Wang, and Xiangzheng Fu. "Pretraining model for biological sequence data." Briefings in Functional Genomics 20, no. 3 (May 2021): 181–95. http://dx.doi.org/10.1093/bfgp/elab025.

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Abstract With the development of high-throughput sequencing technology, biological sequence data reflecting life information becomes increasingly accessible. Particularly on the background of the COVID-19 pandemic, biological sequence data play an important role in detecting diseases, analyzing the mechanism and discovering specific drugs. In recent years, pretraining models that have emerged in natural language processing have attracted widespread attention in many research fields not only to decrease training cost but also to improve performance on downstream tasks. Pretraining models are used for embedding biological sequence and extracting feature from large biological sequence corpus to comprehensively understand the biological sequence data. In this survey, we provide a broad review on pretraining models for biological sequence data. Moreover, we first introduce biological sequences and corresponding datasets, including brief description and accessible link. Subsequently, we systematically summarize popular pretraining models for biological sequences based on four categories: CNN, word2vec, LSTM and Transformer. Then, we present some applications with proposed pretraining models on downstream tasks to explain the role of pretraining models. Next, we provide a novel pretraining scheme for protein sequences and a multitask benchmark for protein pretraining models. Finally, we discuss the challenges and future directions in pretraining models for biological sequences.

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Kull, Kalevi. "Ladder, tree, web: The ages of biological understanding." Sign Systems Studies 31, no. 2 (December 31, 2003): 589–603. http://dx.doi.org/10.12697/sss.2003.31.2.15.

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Fundamental turns in biological understanding can be interpreted as replacements of deep models that organise the biological knowledge. Three deep models distinguished here are a holistic ladder model that sees all levels of nature being complete (from Aristotle to the 18th century), a modernist tree model that emphasises progress and evolution (from Enlightenment to the recent times), and a web model that evaluates diversity (since the 20th century). The turn from the tree model to the web model in biology includes (1) a transfer from modern to postmodern approaches, (2) a shift of semiotic threshold to the border of life, and (3) building the semiotic models of living systems, i.e., the rise of biosemiotics.

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Bartley, Bryan, Jacob Beal, Kevin Clancy, Goksel Misirli, Nicholas Roehner, Ernst Oberortner, Matthew Pocock, et al. "Synthetic Biology Open Language (SBOL) Version 2.0.0." Journal of Integrative Bioinformatics 12, no. 2 (June 1, 2015): 902–91. http://dx.doi.org/10.1515/jib-2015-272.

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Summary Synthetic biology builds upon the techniques and successes of genetics, molecular biology, and metabolic engineering by applying engineering principles to the design of biological systems. The field still faces substantial challenges, including long development times, high rates of failure, and poor reproducibility. One method to ameliorate these problems would be to improve the exchange of information about designed systems between laboratories. The Synthetic Biology Open Language (SBOL) has been developed as a standard to support the specification and exchange of biological design information in synthetic biology, filling a need not satisfied by other pre-existing standards. This document details version 2.0 of SBOL, introducing a standardized format for the electronic exchange of information on the structural and functional aspects of biological designs. The standard has been designed to support the explicit and unambiguous description of biological designs by means of a well defined data model. The standard also includes rules and best practices on how to use this data model and populate it with relevant design details. The publication of this specification is intended to make these capabilities more widely accessible to potential developers and users in the synthetic biology community and beyond.

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Agarwal, Pankaj. "The Cell Programming Language." Artificial Life 2, no. 1 (October 1994): 37–77. http://dx.doi.org/10.1162/artl.1994.2.1.37.

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In developmental biology, modeling and simulation play an important role in understanding cellular behavior. This article includes a review of the general models of development that are based on cellular interaction. Inspired by these models, we suggest a simple language, the Cell Programming Language (CPL), to write computer programs to describe this developmental behavior. These programs are an estimate of the minimal information needed to model realistically such developmental systems. Using these programs, it is possible to simulate and visualize cell behavior. We have employed CPL to model the following: aggregation in cellular slime mold in response to a chemotactic agent, cellular segregation and engulfment due to differential adhesion, and precartilage formation in vertebrate limbs. We believe CPL is a useful tool for developing, understanding, and checking biological models.

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Kandler, Anne, Roman Unger, and James Steele. "Language shift, bilingualism and the future of Britain's Celtic languages." Philosophical Transactions of the Royal Society B: Biological Sciences 365, no. 1559 (December 12, 2010): 3855–64. http://dx.doi.org/10.1098/rstb.2010.0051.

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‘Language shift’ is the process whereby members of a community in which more than one language is spoken abandon their original vernacular language in favour of another. The historical shifts to English by Celtic language speakers of Britain and Ireland are particularly well-studied examples for which good census data exist for the most recent 100–120 years in many areas where Celtic languages were once the prevailing vernaculars. We model the dynamics of language shift as a competition process in which the numbers of speakers of each language (both monolingual and bilingual) vary as a function both of internal recruitment (as the net outcome of birth, death, immigration and emigration rates of native speakers), and of gains and losses owing to language shift. We examine two models: a basic model in which bilingualism is simply the transitional state for households moving between alternative monolingual states, and a diglossia model in which there is an additional demand for the endangered language as the preferred medium of communication in some restricted sociolinguistic domain, superimposed on the basic shift dynamics. Fitting our models to census data, we successfully reproduce the demographic trajectories of both languages over the past century. We estimate the rates of recruitment of new Scottish Gaelic speakers that would be required each year (for instance, through school education) to counteract the ‘natural wastage’ as households with one or more Gaelic speakers fail to transmit the language to the next generation informally, for different rates of loss during informal intergenerational transmission.

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Owsianková, Hana, Dan Faltýnek, and Ondřej Kučera. "Genetic analysis of cabbages and related cultivated plants using the bag-of-words model." Linguistic Frontiers 1, no. 2 (December 1, 2018): 122–32. http://dx.doi.org/10.2478/lf-2018-0011.

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AbstractIn this study, we aim to introduce the analytical method bag-of-words, which is mainly used as a tool for the analysis (document classification, authorship attribution and so on; e.g. [1, 2]) of natural languages. Quantitative linguistic methods similar to bag-of-words (e.g. Damerau–Levenshtein distance in the paper by Serva and Petroni [3]) have been used for the mapping of language evolution within the field of glottochronology. We attempt to apply this method in the field of biological taxonomy – on the Brassicaceae (Cruciferae) family. The subjects of our interest are well-known cultivated crops, which at first sight are morphologically very different and culturally perceived as objects of different interests (e.g. oil from oilseed rape, turnip as animal feed and cabbage as a side dish). Despite the phenotypic divergence of these crops, they are very closely related, which is not morphologically obvious at first sight. For this reason, we think that Brassicaceae crops are appropriate illustrative examples for introducing the method. For the analysis, we use genetic markers (internal transcribed spacer [ITS] and maturase K [matK]). Until now, the bag-of-words model has not been used for biological taxonomisation purposes; therefore, the results of the bagof-words analysis are compared with the existing very well-developed Brassica taxonomy. Our goal is to present a method that is suitable for language development reconstruction as well as possibly being usable for biological taxonomy purposes.

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Catania, A. Charles. "Language evolution: Two tracks are not enough." Behavioral and Brain Sciences 32, no. 5 (October 2009): 451–52. http://dx.doi.org/10.1017/s0140525x0999063x.

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AbstractThis commentary argues that Evans & Levinson (E&L) should expand their two-track model to a three-track model in which biological and cultural evolution interact with the evolution of an individual's language repertories in ontogeny. It also comments on the relevance of the argument from the poverty of the stimulus and offers a caveat, based on analogous issues in biology, on the metaphor of language as a container, whether of meanings or of other content.

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Francesco-Alessio Ursini. "Which Model of Biological Plausibility for Language?: The Case of “What Darwin Got Wrong”." Language & Information Society 19, no. ll (May 2013): 103–40. http://dx.doi.org/10.29211/soli.2013.19..004.

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GANAPATHIRAJU, MADHAVI K., ASIA D. MITCHELL, MOHAMED THAHIR, KAMIYA MOTWANI, and SESHAN ANANTHASUBRAMANIAN. "SUITE OF TOOLS FOR STATISTICAL N-GRAM LANGUAGE MODELING FOR PATTERN MINING IN WHOLE GENOME SEQUENCES." Journal of Bioinformatics and Computational Biology 10, no. 06 (October 18, 2012): 1250016. http://dx.doi.org/10.1142/s0219720012500163.

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Genome sequences contain a number of patterns that have biomedical significance. Repetitive sequences of various kinds are a primary component of most of the genomic sequence patterns. We extended the suffix-array based Biological Language Modeling Toolkit to compute n-gram frequencies as well as n-gram language-model based perplexity in windows over the whole genome sequence to find biologically relevant patterns. We present the suite of tools and their application for analysis on whole human genome sequence.

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Iranmanesh, Farzaneh, Mehry Haddad Narafshan, and Mohammad Golshan. "A brain-based model of language instruction: from theory to practice." Research and Development in Medical Education 10, no. 1 (September 7, 2021): 17. http://dx.doi.org/10.34172/rdme.2021.017.

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Background: A recent trend in second language acquisition and learning has been oriented towards brain-based studies and its association with brain development and plasticity. There are currently unprecedented opportunities for contemporary understanding of the neurological basis of second language (L2) learning owing to recent advances in cognitive neuroscience. Brain functional and structural investigations have contributed remarkably to biological explanations of language acquisition in addition to behavioral explorations. Methods: This study used a meta-analysis of previous findings of functional neuroimaging studies to elucidate the neuroanatomy of language learning from a functional perspective. By synthesizing existing literature, brain activation areas associated with different language learning skills and their convergence and overlap with other areas of activation for other cognitive and motor skills are extracted to reveal consistent functional areas of the brain. The current study attempts to link psycholinguistic research and cognitive neuroscience in the mediation of L2 learning and teaching. This review paper begins with a theoretical view of brain structure and function and concludes with a practical model of brain-based language instruction, resulting in a deeper understanding of the field. Results: Organized, conjoining cognitive neuroscience findings and L2 acquisition and learning approaches provide an opportunity for collaboration in cross-disciplinary studies. They provide new insights into how our brain represents languages. This article reviews recent advancements in our understanding of the brain; structural and functional organization of the brain; the role the brain plays in emotion, cognition, and development; and its consequent implication in language instruction. In effect, taking neurocognitive findings into account may have potential in developing brain-based tasks for the benefit of second language instruction in educational settings. Based on the revealed structural and functional areas of the brain and their networks of connection and interaction, manipulating areas of demanded activity may be as efficient as doing physical exercise to strengthen muscles. Conclusion: Developing a systematic model of second language instruction compatible with brain functions and patterns can benefit the rate and proficiency of language learners, thus improving language teaching and learning outcomes. This paper will aid the quest for utilizing general information of brain functions and related methods in developing practical, efficient language instruction as well as enhancing interdisciplinary research studies in both language and cognitive neuroscience.

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Vanhooren, Henk, Jurgen Meirlaen, Youri Amerlinck, Filip Claeys, Hans Vangheluwe, and Peter A. Vanrolleghem. "WEST: modelling biological wastewater treatment." Journal of Hydroinformatics 5, no. 1 (January 1, 2003): 27–50. http://dx.doi.org/10.2166/hydro.2003.0003.

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Modelling is considered to be an inherent part of the design and operation of a wastewater treatment system. The models used in practice range from conceptual models and physical design models (laboratory-scale or pilot-scale reactors) to empirical or mechanistic mathematical models. These mathematical models can be used during the design, operation and optimisation of a wastewater treatment system. To do so, a good software tool is indispensable. WEST is a general modelling and simulation environment and can, together with a model base, be used for this task. The model base presented here is specific for biological wastewater treatment and is written in MSL-USER. In this high-level object-oriented language, the dynamics of systems can be represented along with symbolic information. In WEST's graphical modelling environment, the physical layout of the plant can be rebuilt, and each building block can be linked to a specific model from the model base. The graphical information is then combined with the information in the model base to produce MSL-EXEC code, which can be compiled with a C++ compiler. In the experimentation environment, the user can design different experiments, such as simulations and optimisations of, for instance, designs, controllers and model fits to data (calibration).

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Meyerhöffer, Nina, and Daniel C. Dreesmann. "Using English as the Language of Science." American Biology Teacher 83, no. 3 (March 1, 2021): 154–60. http://dx.doi.org/10.1525/abt.2021.83.3.154.

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This article presents the development and testing of a content-based video exchange model as a motivating means to introduce lower secondary English learners to English as the language of science. The central goal was that students reach the required curricular content knowledge despite learning some of the content in a foreign language. The model was tested in German seventh-grade classes (n = 133), in which the students communicated with U.S. eighth-graders on the topic of ecology. Following field trips to a forest and a desert ecosystem, students presented and compared biotic and abiotic data in videos. The German students’ content knowledge and their motivation were assessed in a pretest/posttest design. They met the curricular outcome requirements, and their motivation was remarkably high at both test times. We discuss implications for further application of the exchange model.

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Hucka, Michael, Frank T. Bergmann, Stefan Hoops, Sarah M. Keating, Sven Sahle, James C. Schaff, Lucian P. Smith, and Darren J. Wilkinson. "The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 1 Core." Journal of Integrative Bioinformatics 12, no. 2 (June 1, 2015): 382–549. http://dx.doi.org/10.1515/jib-2015-266.

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Summary Computational models can help researchers to interpret data, understand biological function, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that can be exchanged between different software systems. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 1 of SBML Level 3 Core. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project web site, http://sbml.org/.

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SCHWÄMMLE, V. "SIMULATION FOR COMPETITION OF LANGUAGES WITH AN AGING SEXUAL POPULATION." International Journal of Modern Physics C 16, no. 10 (October 2005): 1519–26. http://dx.doi.org/10.1142/s0129183105008084.

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Recently, individual-based models originally used for biological purposes revealed interesting insights into processes of the competition of languages. Within this new field of population dynamics a model considering sexual populations with aging is presented. The agents are situated on a lattice and each one speaks one of two languages or both. The stability and quantitative structure of an interface between two regions, initially speaking different languages, is studied. We find that individuals speaking both languages do not prefer any of these regions and have a different age structure than individuals speaking only one language.

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Formanowicz, Dorota, Adam Kozak, and Piotr Formanowicz. "A Petri net based model of oxidative stress in atherosclerosis." Foundations of Computing and Decision Sciences 37, no. 2 (October 1, 2012): 59–78. http://dx.doi.org/10.2478/v10209-011-0005-x.

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Abstract. In this paper a Petri net based model of the process of oxidative stress in atherosclerosis is presented and analyzed. Model expressed in the language of Petri net theory have, on one hand, an intuitive graphical representation, and on the other hand their formal properties can be analyzed using rigorous mathematical methods. Moreover, the behavior of a net can be simulated what supports the process of model development and an interpretation of the results of the analysis. Both the analysis and the simulation can be supported by many freely available software tools. In the case of biological systems an analysis the t-invariants is especially important since they correspond to some elementary biological subprocesses. In this paper the results of such an analysis are presented. In particular, minimal t-invariants, MCT-sets and t-clusters are calculated, their biological meaning is determined and some biological conclusions are drawn.

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Yang, Charles D. "Internal and external forces in language change." Language Variation and Change 12, no. 3 (October 2000): 231–50. http://dx.doi.org/10.1017/s0954394500123014.

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If every productive form of linguistic expression can be described by some idealized human grammar, an individuals's variable linguistic behavior (Weinreich, Labov, & Herzog, 1968) can be modeled as a statistical distribution of multiple idealized grammars. The distribution of grammars is determined by the interaction between the biological constraints on human grammar and the properties of linguistic data in the environment during the course of language acquisition. Such interaction can be formalized precisely and quantitatively in a mathematical model of language learning. Consequently, we model language change as the change in grammar distribution over time, which can be related to the statistical properties of historical linguistic data. As an empirical test, we apply the proposed model to explain the loss of the verb-second phenomenon in Old French and Old English based on corpus studies of historical texts.

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Rapp, Alexander, Mira Hensler, Mathias Bartels, Dorothee Mutschler, Ralf Saur, and Katja Markert. "METONYMY RESOLUTION IN SCHIZOPHRENIA: A MODEL FOR COMPLEX SEMANTIC LANGUAGE COMPREHENSION." Schizophrenia Research 102, no. 1-3 (June 2008): 144. http://dx.doi.org/10.1016/s0920-9964(08)70437-7.

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Faiqoh, Avita Elok, and Ashadi Ashadi. "EFL students’ language attitudes toward virtual learning environment: A technology acceptance model." Englisia: Journal of Language, Education, and Humanities 10, no. 2 (May 2, 2023): 20. http://dx.doi.org/10.22373/ej.v10i2.15178.

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It is widely believed that L2 learners who have positive attitudes towards the target culture and its people are likely to learn the target language more effectively than those who do not possess such attitudes. As technology continues to be increasingly integrated into language learning, this article aims to explore EFL students' attitudes towards technology acceptance via Virtual Learning Environment (VLE), as well as the potential advantages of VLE in the EFL classroom. A quantitative approach was employed in this study, which involved 30 students from English language education departments at a private university in Indonesia. The students' computer attitudes were assessed using a Likert scale questionnaire with four factors, including affective, perceived usefulness, perceived control, and behavioural intention. The Technology Acceptance Model (TAM) developed by Davis (1989) was utilized as the framework to further examine these factors. The results indicated that the students had a positive attitude towards the Computer Attitude Scale (CAS) factors, which were categorized into affective, perceived usefulness, perceived control, and behavioural intention. The implications of each of these categories in the framework are discussed in relation to behaviourism theory.

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Pulvermüller, Friedemann, Bettina Mohr, and Hubert Preissl. "Biology of language: Principle predictions and evidence." Behavioral and Brain Sciences 19, no. 4 (December 1996): 643–45. http://dx.doi.org/10.1017/s0140525x00043442.

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AbstractMüller's target article aims to summarize approaches to the question of how language elements (phonemes, morphemes, etc.) and rules are laid down in the brain. However, it suffers from being too vague about basic assumptions and empirical predictions of neurobiological models, and the empirical evidence available to test the models is not appropriately evaluated. (1) In a neuroscientific model of language, different cortical localizations of words can only be based on biological principles. These need to be made explicit. (2) Evidence for and against word class differences could be evaluated more rigorously. (3) All (and only) humans are able to learn languages with complex syntactic structures; it is, therefore, not appropriate to deny innateness and universality of syntactic principles. The real question appears to be the following: Which neurobiological principles are the linguistic principles based on?

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Żychowska-Skiba, Dorota. "PERSON-FIRST LANGUAGE OR IDENTITY-FIRST LANGUAGE IN RELATION TO PEOPLE WITH DISABILITIES IN PUBLIC DISCOURSE." Polityka Społeczna 587, no. 2 (April 28, 2023): 28–34. http://dx.doi.org/10.5604/01.3001.0053.4169.

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Polands ratification of the UN Convention on the Rights of Persons with Disabilities in 2012 has changed the ways in which disability is defined in the country. There has been a move away from a perception of disability based on medical terms towards one that views disability as an effect of barriers inherent in society. The adoption of such a social model has led to a shift in perspective in terms of how people with disabilities are identified. In public debate there has been a noticeable discarding of the terms disabled, blind, cripple, invalid, etc., in favour of such expressions as a person with a disability, and a person with special needs. This indicates a tendency to refrain from offering any definition of disability or type of disability and focussing instead on putting the person first and replacing the concept of disability with other less stigmatizing terms. The main goal of the study is a discourse analysis (a quantitative content analysis) of the content of the websites of ministries and government institutions. This approach will enable us to highlight changes in the language of disability determination at government level, where social policy is shaped. Thanks to this, it will also be possible to recreate different ways of defining people with disabilities and show the dominant model of disability perception in contemporary Poland.

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Chen, Huajun, Xi Chen, Peiqin Gu, Zhaohui Wu, and Tong Yu. "OWL Reasoning Framework over Big Biological Knowledge Network." BioMed Research International 2014 (2014): 1–16. http://dx.doi.org/10.1155/2014/272915.

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Recently, huge amounts of data are generated in the domain of biology. Embedded with domain knowledge from different disciplines, the isolated biological resources are implicitly connected. Thus it has shaped a big network of versatile biological knowledge. Faced with such massive, disparate, and interlinked biological data, providing an efficient way to model, integrate, and analyze the big biological network becomes a challenge. In this paper, we present a general OWL (web ontology language) reasoning framework to study the implicit relationships among biological entities. A comprehensive biological ontology across traditional Chinese medicine (TCM) and western medicine (WM) is used to create a conceptual model for the biological network. Then corresponding biological data is integrated into a biological knowledge network as the data model. Based on the conceptual model and data model, a scalable OWL reasoning method is utilized to infer the potential associations between biological entities from the biological network. In our experiment, we focus on the association discovery between TCM and WM. The derived associations are quite useful for biologists to promote the development of novel drugs and TCM modernization. The experimental results show that the system achieves high efficiency, accuracy, scalability, and effectivity.

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Smith, Kenny, and Simon Kirby. "Cultural evolution: implications for understanding the human language faculty and its evolution." Philosophical Transactions of the Royal Society B: Biological Sciences 363, no. 1509 (September 19, 2008): 3591–603. http://dx.doi.org/10.1098/rstb.2008.0145.

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Human language is unique among the communication systems of the natural world: it is socially learned and, as a consequence of its recursively compositional structure, offers open-ended communicative potential. The structure of this communication system can be explained as a consequence of the evolution of the human biological capacity for language or the cultural evolution of language itself. We argue, supported by a formal model, that an explanatory account that involves some role for cultural evolution has profound implications for our understanding of the biological evolution of the language faculty: under a number of reasonable scenarios, cultural evolution can shield the language faculty from selection, such that strongly constraining language-specific learning biases are unlikely to evolve. We therefore argue that language is best seen as a consequence of cultural evolution in populations with a weak and/or domain-general language faculty.

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Hucka, Michael, Frank T. Bergmann, Andreas Dräger, Stefan Hoops, Sarah M. Keating, Nicolas Le Novère, Chris J. Myers, et al. "Systems Biology Markup Language (SBML) Level 2 Version 5: Structures and Facilities for Model Definitions." Journal of Integrative Bioinformatics 12, no. 2 (June 1, 2015): 731–901. http://dx.doi.org/10.1515/jib-2015-271.

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Summary Computational models can help researchers to interpret data, understand biological function, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that can be exchanged between different software systems. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 5 of SBML Level 2. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project web site, http://sbml.org/.

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Pu, Jiaozi, and Zongxin Liu. "Cloud Improvement Model of Niche Width and Its Application in Tram Evaluation." Wireless Communications and Mobile Computing 2022 (September 7, 2022): 1–9. http://dx.doi.org/10.1155/2022/1599664.

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Niche theory is one of the most important theories and tools in ecology. Niche width is a concept used to measure the position and role of an individual or population in a biological community. However, in the measurement of niche width, due to the unique characteristics of ecosystems, evaluators often give information based on subjective judgment to describe the status of ecological factors. Natural language is used to describe the state of ecological factors, and all the observed values come from the subjective judgment of the language value of the evaluator and have fuzziness and randomness at the same time. To achieve scientific and reasonable decision evaluation, it is necessary to comprehensively consider the fuzziness and randomness of complex systems. The cloud model is used to improve the niche width model, aimed at solving ecological factors under natural language information assignment and niche width evaluation. It is applied to measure tram niche width, which is the typical ecological factor value assignment using natural language thinking.

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Bauer, Mark. "Normative Characterization in Empirical Explanation." THEORIA. An International Journal for Theory, History and Foundations of Science 30, no. 2 (June 20, 2015): 271. http://dx.doi.org/10.1387/theoria.11957.

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Normative characterization is a commonplace feature of biological and cognitive explanation. Such language seems to commit the biological and cognitive sciences to the existence of natural norms, but it is also difficult to understand how such normativity fits into a natural world of physical causes and forces. Existing models for how such language can have a legitimate causal-explanatory role in the sciences are, I think, unsatisfactory. I suggest an alternative model in which normativity is mapped onto systems stabilized by counteractive constraints. Such a mapping, I propose, explains normativity’s causal-explanatory role in biological and cognitive inquiry.

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Brandes, Nadav, Dan Ofer, Yam Peleg, Nadav Rappoport, and Michal Linial. "ProteinBERT: a universal deep-learning model of protein sequence and function." Bioinformatics 38, no. 8 (February 10, 2022): 2102–10. http://dx.doi.org/10.1093/bioinformatics/btac020.

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Abstract Summary Self-supervised deep language modeling has shown unprecedented success across natural language tasks, and has recently been repurposed to biological sequences. However, existing models and pretraining methods are designed and optimized for text analysis. We introduce ProteinBERT, a deep language model specifically designed for proteins. Our pretraining scheme combines language modeling with a novel task of Gene Ontology (GO) annotation prediction. We introduce novel architectural elements that make the model highly efficient and flexible to long sequences. The architecture of ProteinBERT consists of both local and global representations, allowing end-to-end processing of these types of inputs and outputs. ProteinBERT obtains near state-of-the-art performance, and sometimes exceeds it, on multiple benchmarks covering diverse protein properties (including protein structure, post-translational modifications and biophysical attributes), despite using a far smaller and faster model than competing deep-learning methods. Overall, ProteinBERT provides an efficient framework for rapidly training protein predictors, even with limited labeled data. Availability and implementation Code and pretrained model weights are available at https://github.com/nadavbra/protein_bert. Supplementary information Supplementary data are available at Bioinformatics online.

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Johnson, David, Anthony J. Connor, Steve Mckeever, Zhihui Wang, Thomas S. Deisboeck, Tom Quaiser, and Eliezer Shochat. "Semantically Linking in Silico Cancer Models." Cancer Informatics 13s1 (January 2014): CIN.S13895. http://dx.doi.org/10.4137/cin.s13895.

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Multiscale models are commonplace in cancer modeling, where individual models acting on different biological scales are combined within a single, cohesive modeling framework. However, model composition gives rise to challenges in understanding interfaces and interactions between them. Based on specific domain expertise, typically these computational models are developed by separate research groups using different methodologies, programming languages, and parameters. This paper introduces a graph-based model for semantically linking computational cancer models via domain graphs that can help us better understand and explore combinations of models spanning multiple biological scales. We take the data model encoded by TumorML, an XML-based markup language for storing cancer models in online repositories, and transpose its model description elements into a graph-based representation. By taking such an approach, we can link domain models, such as controlled vocabularies, taxonomic schemes, and ontologies, with cancer model descriptions to better understand and explore relationships between models. The union of these graphs creates a connected property graph that links cancer models by categorizations, by computational compatibility, and by semantic interoperability, yielding a framework in which opportunities for exploration and discovery of combinations of models become possible.

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Smith, Kenny, Amy Perfors, Olga Fehér, Anna Samara, Kate Swoboda, and Elizabeth Wonnacott. "Language learning, language use and the evolution of linguistic variation." Philosophical Transactions of the Royal Society B: Biological Sciences 372, no. 1711 (January 5, 2017): 20160051. http://dx.doi.org/10.1098/rstb.2016.0051.

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Linguistic universals arise from the interaction between the processes of language learning and language use. A test case for the relationship between these factors is linguistic variation, which tends to be conditioned on linguistic or sociolinguistic criteria. How can we explain the scarcity of unpredictable variation in natural language, and to what extent is this property of language a straightforward reflection of biases in statistical learning? We review three strands of experimental work exploring these questions, and introduce a Bayesian model of the learning and transmission of linguistic variation along with a closely matched artificial language learning experiment with adult participants. Our results show that while the biases of language learners can potentially play a role in shaping linguistic systems, the relationship between biases of learners and the structure of languages is not straightforward. Weak biases can have strong effects on language structure as they accumulate over repeated transmission. But the opposite can also be true: strong biases can have weak or no effects. Furthermore, the use of language during interaction can reshape linguistic systems. Combining data and insights from studies of learning, transmission and use is therefore essential if we are to understand how biases in statistical learning interact with language transmission and language use to shape the structural properties of language. This article is part of the themed issue ‘New frontiers for statistical learning in the cognitive sciences’.

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Clark, Kevin B. "Natural chunk-and-pass language processing: Just another joint source-channel coding model?" Communicative & Integrative Biology 11, no. 2 (March 4, 2018): 1–2. http://dx.doi.org/10.1080/19420889.2018.1445899.

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杨, 伦宇. "Optimization and Development of Family Foreign Language Education Model in the New Era." Advances in Social Sciences 12, no. 03 (2023): 993–98. http://dx.doi.org/10.12677/ass.2023.123137.

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Kotchoubey, Boris. "Pragmatics, prosody, and evolution: Language is more than a symbolic system." Behavioral and Brain Sciences 28, no. 2 (April 2005): 136–37. http://dx.doi.org/10.1017/s0140525x05340039.

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The model presented in the target article is biased towards a cognitive-symbolic understanding of language, thus ignoring its other important aspects. Possible relationships of this cognitive-symbolic subsystem to pragmatics and prosody of language are discussed in the first part of the commentary. In the second part, the issue of a purely social versus biological mechanisms for transition from protolanguage to properly language is considered.

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Calude, Andreea S., and Mark Pagel. "How do we use language? Shared patterns in the frequency of word use across 17 world languages." Philosophical Transactions of the Royal Society B: Biological Sciences 366, no. 1567 (April 12, 2011): 1101–7. http://dx.doi.org/10.1098/rstb.2010.0315.

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We present data from 17 languages on the frequency with which a common set of words is used in everyday language. The languages are drawn from six language families representing 65 per cent of the world's 7000 languages. Our data were collected from linguistic corpora that record frequencies of use for the 200 meanings in the widely used Swadesh fundamental vocabulary. Our interest is to assess evidence for shared patterns of language use around the world, and for the relationship of language use to rates of lexical replacement, defined as the replacement of a word by a new unrelated or non-cognate word. Frequencies of use for words in the Swadesh list range from just a few per million words of speech to 191 000 or more. The average inter-correlation among languages in the frequency of use across the 200 words is 0.73 ( p < 0.0001). The first principal component of these data accounts for 70 per cent of the variance in frequency of use. Elsewhere, we have shown that frequently used words in the Indo-European languages tend to be more conserved, and that this relationship holds separately for different parts of speech. A regression model combining the principal factor loadings derived from the worldwide sample along with their part of speech predicts 46 per cent of the variance in the rates of lexical replacement in the Indo-European languages. This suggests that Indo-European lexical replacement rates might be broadly representative of worldwide rates of change. Evidence for this speculation comes from using the same factor loadings and part-of-speech categories to predict a word's position in a list of 110 words ranked from slowest to most rapidly evolving among 14 of the world's language families. This regression model accounts for 30 per cent of the variance. Our results point to a remarkable regularity in the way that human speakers use language, and hint that the words for a shared set of meanings have been slowly evolving and others more rapidly evolving throughout human history.

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Sterelny, Kim. "Language, gesture, skill: the co-evolutionary foundations of language." Philosophical Transactions of the Royal Society B: Biological Sciences 367, no. 1599 (August 5, 2012): 2141–51. http://dx.doi.org/10.1098/rstb.2012.0116.

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This paper defends a gestural origins hypothesis about the evolution of enhanced communication and language in the hominin lineage. The paper shows that we can develop an incremental model of language evolution on that hypothesis, but not if we suppose that language originated in an expansion of great ape vocalization. On the basis of the gestural origins hypothesis, the paper then advances solutions to four classic problems about the evolution of language: (i) why did language evolve only in the hominin lineage? (ii) why is language use an evolutionarily stable form of informational cooperation, despite the fact that hominins have diverging evolutionary interests? (iii) how did stimulus independent symbols emerge? (iv) what were the origins of complex, syntactically organized symbols? The paper concludes by confronting two challenges: those of testability and of explaining the gesture-to-speech transition; crucial issues for any gestural origins hypothesis

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Panaitof, S. Carmen. "A Songbird Animal Model for Dissecting the Genetic Bases of Autism Spectrum Disorder." Disease Markers 33, no. 5 (2012): 241–49. http://dx.doi.org/10.1155/2012/727058.

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The neural and genetic bases of human language development and associated neurodevelopmental disorders, including autism spectrum disorder (ASD), in which language impairment represents a core deficit, are poorly understood. Given that no single animal model can fully capture the behavioral and genetic complexity of ASD, work in songbird, an experimentally tractable animal model of vocal learning, can complement the valuable tool of rodent genetic models and contribute important insights to our understanding of the communication deficits observed in ASD. Like humans, but unlike traditional laboratory animals such as rodents or non-human primates, songbirds exhibit the capacity of vocal learning, a key subcomponent of language. Human speech and birdsong reveal important parallels, highlighting similar developmental critical periods, a homologous cortico-basal ganglia-thalamic circuitry, and a critical role for social influences in the learning of vocalizations. Here I highlight recent advances in using the songbird model to probe the cellular and molecular mechanisms underlying the formation and function of neural circuitry for birdsong and, by analogy, human language, with the ultimate goal of identifying any shared or human unique biological pathways underscoring language development and its disruption in ASD.

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Westermann, Gert, and Denis Mareschal. "From perceptual to language-mediated categorization." Philosophical Transactions of the Royal Society B: Biological Sciences 369, no. 1634 (January 19, 2014): 20120391. http://dx.doi.org/10.1098/rstb.2012.0391.

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From at least two months onwards, infants can form perceptual categories. During the first year of life, object knowledge develops from the ability to represent individual object features to representing correlations between attributes and to integrate information from different sources. At the end of the first year, these representations are shaped by labels, opening the way to conceptual knowledge. Here, we review the development of object knowledge and object categorization over the first year of life. We then present an artificial neural network model that models the transition from early perceptual categorization to categories mediated by labels. The model informs a current debate on the role of labels in object categorization by suggesting that although labels do not act as object features they nevertheless affect perceived similarity of perceptually distinct objects sharing the same label. The model presents the first step of an integrated account from early perceptual categorization to language-based concept learning.

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Jia, Li-Na, Xin Yan, Zhu-Hong You, Xi Zhou, Li-Ping Li, Lei Wang, and Ke-Jian Song. "NLPEI: A Novel Self-Interacting Protein Prediction Model Based on Natural Language Processing and Evolutionary Information." Evolutionary Bioinformatics 16 (January 2020): 117693432098417. http://dx.doi.org/10.1177/1176934320984171.

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The study of protein self-interactions (SIPs) can not only reveal the function of proteins at the molecular level, but is also crucial to understand activities such as growth, development, differentiation, and apoptosis, providing an important theoretical basis for exploring the mechanism of major diseases. With the rapid advances in biotechnology, a large number of SIPs have been discovered. However, due to the long period and high cost inherent to biological experiments, the gap between the identification of SIPs and the accumulation of data is growing. Therefore, fast and accurate computational methods are needed to effectively predict SIPs. In this study, we designed a new method, NLPEI, for predicting SIPs based on natural language understanding theory and evolutionary information. Specifically, we first understand the protein sequence as natural language and use natural language processing algorithms to extract its features. Then, we use the Position-Specific Scoring Matrix (PSSM) to represent the evolutionary information of the protein and extract its features through the Stacked Auto-Encoder (SAE) algorithm of deep learning. Finally, we fuse the natural language features of proteins with evolutionary features and make accurate predictions by Extreme Learning Machine (ELM) classifier. In the SIPs gold standard data sets of human and yeast, NLPEI achieved 94.19% and 91.29% prediction accuracy. Compared with different classifier models, different feature models, and other existing methods, NLPEI obtained the best results. These experimental results indicated that NLPEI is an effective tool for predicting SIPs and can provide reliable candidates for biological experiments.

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Lister, Allyson L., Matthew Pocock, and Anil Wipat. "Integration of constraints documented in SBML, SBO, and the SBML Manual facilitates validation of biological models." Journal of Integrative Bioinformatics 4, no. 3 (December 1, 2007): 252–63. http://dx.doi.org/10.1515/jib-2007-80.

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Abstract The creation of quantitative, simulatable, Systems Biology Markup Language (SBML) models that accurately simulate the system under study is a time-intensive manual process that requires careful checking. Currently, the rules and constraints of model creation, curation, and annotation are distributed over at least three separate documents: the SBML schema document (XSD), the Systems Biology Ontology (SBO), and the “Structures and Facilities for Model Definition” document. The latter document contains the richest set of constraints on models, and yet it is not amenable to computational processing. We have developed a Web Ontology Language (OWL) knowledge base that integrates these three structure documents, and that contains a representative sample of the information contained within them. This Model Format OWL (MFO) performs both structural and constraint integration and can be reasoned over and validated. SBML Models are represented as individuals of OWL classes, resulting in a single computationally amenable resource for model checking. Knowledge that was only accessible to humans is now explicitly and directly available for computational approaches. The integration of all structural knowledge for SBML models into a single resource creates a new style of model development and checking.

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Costantini, Alessandro, Rosalinda Cassibba, Gabrielle Coppola, and Germana Castoro. "Attachment security and language development in an Italian sample." International Journal of Behavioral Development 36, no. 2 (December 15, 2011): 85–92. http://dx.doi.org/10.1177/0165025411426682.

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We investigated the influence of biological immaturity and attachment security on linguistic development and tested whether maternal language mediated the impact of security on the child’s linguistic abilities. Forty mother–child dyads were followed longitudinally, with the child’s attachment security assessed at 24 months of age through trained observers’ Attachment Q-Sorts, and linguistic abilities assessed at 24 and 30 months through observational measures and maternal reports. Both factors were found to contribute, though not independently, to the prediction of the child’s linguistic abilities, and the mediation model was confirmed.

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Zhang, Yikang, Xiaomin Chu, Yelu Jiang, Hongjie Wu, and Lijun Quan. "SemanticCAP: Chromatin Accessibility Prediction Enhanced by Features Learning from a Language Model." Genes 13, no. 4 (March 23, 2022): 568. http://dx.doi.org/10.3390/genes13040568.

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A large number of inorganic and organic compounds are able to bind DNA and form complexes, among which drug-related molecules are important. Chromatin accessibility changes not only directly affect drug–DNA interactions, but they can promote or inhibit the expression of the critical genes associated with drug resistance by affecting the DNA binding capacity of TFs and transcriptional regulators. However, the biological experimental techniques for measuring it are expensive and time-consuming. In recent years, several kinds of computational methods have been proposed to identify accessible regions of the genome. Existing computational models mostly ignore the contextual information provided by the bases in gene sequences. To address these issues, we proposed a new solution called SemanticCAP. It introduces a gene language model that models the context of gene sequences and is thus able to provide an effective representation of a certain site in a gene sequence. Basically, we merged the features provided by the gene language model into our chromatin accessibility model. During the process, we designed methods called SFA and SFC to make feature fusion smoother. Compared to DeepSEA, gkm-SVM, and k-mer using public benchmarks, our model proved to have better performance, showing a 1.25% maximum improvement in auROC and a 2.41% maximum improvement in auPRC.

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Yahlali, Mebarka. "A Survey on Bio-Inspired Method for Detection of Spamming." International Journal of Strategic Information Technology and Applications 8, no. 3 (July 2017): 1–19. http://dx.doi.org/10.4018/ijsita.2017070101.

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The objective of this work is to show the importance of bi-inspiration SPAM filtering. To achieve this goal, the author compared two methods: Social bees vs inspiration from the Human Renal. The inspiration is taken from a biological model. Messages are indexed and represented by the n-gram words and characters independent of languages (because message can be received in any language). The results are promising and provide an important way for the use of this model for solving other problems in data mining. The author starts this article with a short introduction where the readers will see the importance of IT security—especially today. The author then explains and experiments on a two original meta-heuristics and explains the natural model and then the artificial model.

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Journal articles on the topic 'BIOLOGICAL LANGUAGE MODEL'

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