Texture classification of proteins using support vector machines and bio-inspired metaheuristics

6th International Joint Conference, BIOSTEC 2013, Barcelona, Spain, February 11-14, 2013[Abstract] In this paper, a novel classification method of two-dimensional polyacrylamide gel electrophoresis images is presented. Such a method uses textural features obtained by means of a feature selection process for whose implementation we compare Genetic Algorithms and Particle Swarm Optimization. Then, the selected features, among which the most decisive and representative ones appear to be those related to the second order co-occurrence matrix, are used as inputs for a Support Vector Machine. The accuracy of the proposed method is around 94 %, a statistically better performance than the classification based on the entire feature set. This classification step can be very useful for discarding over-segmented areas after a protein segmentation or identification process

Texture analysis in gel electrophoresis images using an integrative kernel-based approach

[Abstract] Texture information could be used in proteomics to improve the quality of the image analysis of proteins separated on a gel. In order to evaluate the best technique to identify relevant textures, we use several different kernel-based machine learning techniques to classify proteins in 2-DE images into spot and noise. We evaluate the classification accuracy of each of these techniques with proteins extracted from ten 2-DE images of different types of tissues and different experimental conditions. We found that the best classification model was FSMKL, a data integration method using multiple kernel learning, which achieved AUROC values above 95% while using a reduced number of features. This technique allows us to increment the interpretability of the complex combinations of textures and to weight the importance of each particular feature in the final model. In particular the Inverse Difference Moment exhibited the highest discriminating power. A higher value can be associated with an homogeneous structure as this feature describes the homogeneity; the larger the value, the more symmetric. The final model is performed by the combination of different groups of textural features. Here we demonstrated the feasibility of combining different groups of textures in 2-DE image analysis for spot detection.Instituto de Salud Carlos III; PI13/00280United Kingdom. Medical Research Council; G10000427, MC_UU_12013/8Galicia. Consellería de Economía e Industria; 10SIN105004P

Hybrid Model Based on Genetic Algorithms and SVM Applied to Variable Selection Within Fruit Juice Classification

Research article[Abstract] Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM). Therefore, a hybrid model that combines genetic algorithms and support vector machines is suggested in such a way that, when using SVM as a fitness function of the Genetic Algorithm (GA), the most representative variables for a specific classification problem can be selected

Técnicas basadas en kernel para el análisis de texturas en imagen biomédica

[Resumen] En problemas del mundo real es relevante el estudio de la importancia de todas las variables obtenidas de manera que sea posible la eliminación de ruido, es en este punto donde surgen las técnicas de selección de variables. El objetivo de estas técnicas es pues encontrar el subconjunto de variables que describan de la mejor manera posible la información útil contenida en los datos permitiendo mejorar el rendimiento. En espacios de alta dimensionalidad son especialmente interesantes las técnicas basadas en kernel, donde han demostrado una alta eficiencia debido a su capacidad para generalizar en dichos espacios. En este trabajo se realiza una nueva propuesta para el análisis de texturas en imagen biomédica mediante la integración, utilizando técnicas basadas en kernel, de diferentes tipos de datos de textura para la selección de las variables más representativas con el objetivo de mejorar los resultados obtenidos en clasificación y en interpretabilidad de las variables obtenidas. Para validar esta propuesta se ha formalizado un diseño experimental con cuatro fases diferenciadas: extracción y preprocesado de los datos, aprendizaje y selección del mejor modelo asegurando la reproducibilidad de los resultados a la vez que una comparación en condiciones de igualdad.[Resumo] En problemas do mundo real é relevante o estudo da importancia de todas as variables obtidas de maneira que sexa posible a eliminación de ruído, é neste punto onde xorden as técnicas de selección de variables. O obxectivo destas técnicas é pois encontrar o subconxunto de variables que describan do mellor xeito posible a información útil contida nos datos permitindo mellorar o rendemento. En espazos de alta dimensionalidade son especialmente interesantes as técnicas baseadas en kernel, onde demostraron unha alta eficiencia debido á súa capacidade para xeneralizar nos devanditos espazos. Neste traballo realízase unha nova proposta para a análise de texturas en imaxe biomédica mediante a integración, utilizando técnicas baseadas en kernel, de diferentes tipos de datos de textura para a selección das variables máis representativas co obxectivo de mellorar os resultados obtidos en clasificación e en interpretabilidade das variables obtidas. Para validar esta proposta formalizouse un deseño experimental con catro fases diferenciadas: extracción e preprocesar dos datos, aprendizaxe e selección do mellor modelo asegurando a reproducibilidade dos resultados á vez que unha comparación en condicións de igualdade. Utilizáronse imaxes de xeles de electroforese bidimensional.[Abstract] In real-world problems it is of relevance to study the importance of all the variables obtained, so that denoising could be possible, because it is at this point when the variable selection techniques arise. Therefore, these techniques are aimed at finding the subset of variables that describe' in the best possible way the useful information contained in the data, allowing improved performance. In high-dimensional spaces, the kernel-based techniques are of special relevance, as they have demonstrated a high efficiency due to their ability to generalize in these spaces. In this work, a new approach for texture analysis in biomedical imaging is performed by means of integration. For this procedure, kernel-based techniques were used with different types of texture data for the selection of the most representative variables in order to improve the results obtained in classification and interpretability of the obtained variables. To validate this proposal, an experimental design has been concluded, consisting of four different phases: 1) Data extraction; 2) Data pre-processing; 3) Learning and 4) Selection of the best model to ensure the reproducibility of results while making a comparison under conditions of equality. In this regard, two-dimensional electrophoresis gel images have been used

Fuzzy systems and unsupervised computing: exploration of applications in biology

In this thesis we will explore the use of fuzzy systems theory for applications in bioinformatics. The theory of fuzzy systems is concerned with formulating decision problems in data sets that are ill-defined. It supports the transfer from a subjective human classification to a numerical scale. In this manner it affords the testing of hypothesis and separation of the classes in the data. We first formulate problems in terms of a fuzzy system and then develop and test algorithms in terms of their performance with data from the domain of the life-sciences. From the results and the performance, we will learn about the usefulness of fuzzy systems for the field, as well as the applicability to the kind of problems and practicality for the computation itself. Computer Systems, Imagery and Medi

Biometrics

Biometrics uses methods for unique recognition of humans based upon one or more intrinsic physical or behavioral traits. In computer science, particularly, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. The book consists of 13 chapters, each focusing on a certain aspect of the problem. The book chapters are divided into three sections: physical biometrics, behavioral biometrics and medical biometrics. The key objective of the book is to provide comprehensive reference and text on human authentication and people identity verification from both physiological, behavioural and other points of view. It aims to publish new insights into current innovations in computer systems and technology for biometrics development and its applications. The book was reviewed by the editor Dr. Jucheng Yang, and many of the guest editors, such as Dr. Girija Chetty, Dr. Norman Poh, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park, Dr. Sook Yoon and so on, who also made a significant contribution to the book

New Statistical Algorithms for the Analysis of Mass Spectrometry Time-Of-Flight Mass Data with Applications in Clinical Diagnostics

Mass spectrometry (MS) based techniques have emerged as a standard forlarge-scale protein analysis. The ongoing progress in terms of more sensitive machines and improved data analysis algorithms led to a constant expansion of its fields of applications. Recently, MS was introduced into clinical proteomics with the prospect of early disease detection using proteomic pattern matching. Analyzing biological samples (e.g. blood) by mass spectrometry generates mass spectra that represent the components (molecules) contained in a sample as masses and their respective relative concentrations. In this work, we are interested in those components that are constant within a group of individuals but differ much between individuals of two distinct groups. These distinguishing components that dependent on a particular medical condition are generally called biomarkers. Since not all biomarkers found by the algorithms are of equal (discriminating) quality we are only interested in a small biomarker subset that - as a combination - can be used as a fingerprint for a disease. Once a fingerprint for a particular disease (or medical condition) is identified, it can be used in clinical diagnostics to classify unknown spectra. In this thesis we have developed new algorithms for automatic extraction of disease specific fingerprints from mass spectrometry data. Special emphasis has been put on designing highly sensitive methods with respect to signal detection. Thanks to our statistically based approach our methods are able to detect signals even below the noise level inherent in data acquired by common MS machines, such as hormones. To provide access to these new classes of algorithms to collaborating groups we have created a web-based analysis platform that provides all necessary interfaces for data transfer, data analysis and result inspection. To prove the platform's practical relevance it has been utilized in several clinical studies two of which are presented in this thesis. In these studies it could be shown that our platform is superior to commercial systems with respect to fingerprint identification. As an outcome of these studies several fingerprints for different cancer types (bladder, kidney, testicle, pancreas, colon and thyroid) have been detected and validated. The clinical partners in fact emphasize that these results would be impossible with a less sensitive analysis tool (such as the currently available systems). In addition to the issue of reliably finding and handling signals in noise we faced the problem to handle very large amounts of data, since an average dataset of an individual is about 2.5 Gigabytes in size and we have data of hundreds to thousands of persons. To cope with these large datasets, we developed a new framework for a heterogeneous (quasi) ad-hoc Grid - an infrastructure that allows to integrate thousands of computing resources (e.g. Desktop Computers, Computing Clusters or specialized hardware, such as IBM's Cell Processor in a Playstation 3)

Using proteomic technologies to understand the impact of stress and nutritional factors on fish metabolism, welfare and quality

As the scale of aquacultural activities increases, we increasingly face challenges, not only in terms of sustainability, but also regarding issues like animal welfare and product quality. Within these research fields, proteomics (along with other -omics) is establishing itself as an invaluable tool for an untargeted assessment of the impact of exogenous stimuli on fish metabolism. This dissertation describes work developed in this area, where proteomic technologies were used to understand the impact of stress and nutritional factors on fish metabolism, welfare and quality. Gilthead seabream was chosen as the main biological model, due to its high importance in the Portuguese aquaculture sector, with Senegalese sole as secondary model. These studies were focused on both skeletal muscle and liver tissue. Results demonstrated that a reproducible proteomic analysis of the sarcoplasmic fraction of gilthead seabream muscle is feasible, with pre-slaughter stress inducing a clear hastening of the transition between the pre-rigor and post-rigor profiles. Comparatively, glycerol supplementation (as a tool to modulate muscle glycogen reserves) was shown to have a more subtle impact on the sarcoplasmic proteome of gilthead seabream, with results generally suggesting adaptive effects associated with this dietary factor. Hepatic proteome analyses revealed a high sensitivity towards both stress and dietary factors, with stress factors again displaying a broader impact on protein expression. In these experiments, the proteomic responses to sources of stress displayed specificities that depend on both the biological model and the type/duration of stressor, despite some degree of overlap in terms of the affected pathways. Concluding, despite the apparent resilience of gilthead seabream quality attributes in regards to nutritional and/or stress factors, proteome analysis revealed that these factors have an impact on both muscle and liver metabolism, being likely to affect post-mortem muscle degradation dynamics. The suggestion of specific candidates for further targeted studies underlines the usefulness of proteomics in this context.A aquacultura, apesar de se tratar de uma atividade humana que já conta com milénios de existência, entrou numa fase de crescimento surpreendente a partir da década de 60 do século passado, sendo que atualmente ultrapassa já o sector pesqueiro como principal fornecedor de produtos alimentares derivados de organismos aquáticos. Por outro lado, embora este crescimento prodigioso tenha acarretado mais-valias no que respeita ao custo e acessibilidade destes produtos alimentares (de elevada qualidade nutricional) às populações em geral, cada vez mais nos deparamos com diversos desafios, não só ao nível da sustentabilidade a longo-prazo destas atividades, como ao nível de questões relacionadas com o bem-estar animal e com a qualidade (nutricional e organolética) do produto final. Neste contexto, a proteómica (assim como a genómica, transcriptómica e metabolómica) tem-se estabelecido nos últimos anos como uma valiosa ferramenta na avaliação holística do impacto de fatores extrínsecos sobre os processos celulares dos peixes. O trabalho descrito nesta dissertação foca-se precisamente na aplicação de ferramentas proteómicas para o estudo do impacto do stress (a curto e a longo-prazo) no metabolismo dos peixes e processos de decomposição post-mortem (e, consequentemente, o seu efeito a nível do bem-estar dos peixes e da qualidade do produto final), assim como a interação de fatores nutricionais com estes fatores de stress. Para o efeito, procurou-se efetuar análises proteómicas ao nível de dois tecidos: por um lado, o músculo esquelético (i.e. o filete), dado que constitui a principal parte comestível do peixe; por outro, o fígado, que é o principal órgão responsável pelo controlo central dos processos metabólicos. Estes estudos foram realizados utilizando a dourada (Sparus aurata) como principal modelo animal, dada a sua importância no sector nacional de aquacultura. Para efeitos de comparação, foi ainda efetuado um estudo em linguado (Solea senegalensis), visto se tratar também de uma espécie relevante na aquacultura nacional e apresentar particular sensibilidade a fatores de stress, quando em cativeiro. No decorrer deste trabalho, foram realizados vários ensaios focados no tecido muscular da dourada, particularmente no que respeita a fatores imediatamente pre-mortem e o seu impacto nos processos de decomposição e, consequentemente, nas propriedades organoléticas do produto final. Os resultados obtidos confirmam a possibilidade de isolar a fração sarcoplasmática do proteoma muscular da dourada de forma reprodutível, dado que a sua aplicação aparentemente não aumenta a quantidade de ruído técnico inerente à análise do proteoma (Capítulo 2). A aplicação desta metodologia no estudo do stress pré-abate demonstrou um forte efeito deste tipo de stress pré-mortem no proteoma sarcoplasmático, claramente acelerando a transição entre um perfil pré-rigor e um perfil pós-rigor (Capítulo 3). Para além disso, apesar do impacto de fatores de stress sobre o proteoma muscular da dourada ser muito maior e mais abrangente do que o de fatores nutricionais, também se tornou claro que estes últimos (particularmente, o caso da suplementação alimentar com glicerol) podem induzir um efeito positivo ao nível das reservas de energia pré-mortem, podendo portanto constituir um fator mitigante no impacto dos efeitos de fatores de stress (Capítulo 4). Em relação aos ensaios focados na resposta proteómica do fígado face a fatores nutricionais e de stress, os resultados geralmente demonstram uma maior sensibilidade (comparado ao tecido muscular) face a estímulos externos, com os fatores de stress uma vez mais induzindo um maior impacto no proteoma comparativamente aos fatores nutricionais. Comparando os dois modelos animais utilizados (dourada e linguado), verificou-se que, apesar das semelhanças entre os dois modelos quando sujeitos a fatores de stress crónico em termos de vias metabólicas afetadas, existe uma fraca sobreposição em termos de proteínas especificamente afetadas, sugerindo a existência de idiossincrasias inerentes a cada espécie no que toca à sua resposta a fatores de stress (Capítulos 5 e 6). De facto, mesmo a aplicação de diferentes fatores de stress no mesmo modelo (dourada), em situações experimentais idênticas, demonstra a existência de uma dependência entre o tipo/duração do fator de stress aplicado e a resposta proteómica induzida, a nível hepático (Capítulo 5). Finalmente, conseguiu-se demonstrar que os dados obtidos por espectroscopia de infravermelho transmissiva do fígado de dourada fornecem importante informação que é tanto consistente como complementar à informação fornecida por metodologias proteómicas, providenciando um contributo significativo para o estudo do impacto do stress térmico sazonal no metabolismo da dourada, assim como para a formulação de dietas especiais fortificadas que providenciem um efeito positivo demonstrável no sentido de mitigar o impacto deste fator de stress (Capítulo 7). Concluindo, este trabalho demonstrou que tanto o stress crónico como o stress pré-abate induzem efeitos significativos a nível do proteoma muscular e hepático de peixes como a dourada e o linguado. É importante notar também que, em algumas das experiências, se demonstrou o potencial de fatores nutricionais na mitigação de alguns dos efeitos do stress induzido (tanto a nível hepático como a nível muscular). Para além disso, apesar da observação de claros efeitos induzidos por fatores nutricionais e de stress sobre o proteoma muscular, poucos efeitos foram notados em termos de impacto nas propriedades organoléticas do produto final (nomeadamente, textura, aroma e aspeto visual) que, apesar de positivo (dado ilustrar a robustez dos traços fenotípicos e propriedades organoléticas da dourada, face a fatores extrínsecos), torna difícil a utilização da dourada como modelo ótimo no estudo das relações entre o bem-estar animal e possíveis efeitos indesejados a nível da qualidade do produto final. Apesar disto, é inegável que estes fatores apresentam um impacto não-negligenciável sobre o bem-estar e o metabolismo dos peixes, facto atestado pelos resultados descritos nesta dissertação. Exemplos específicos de candidatos de estudo interessantes em futuros trabalhos nesta área incluem a proteína DJ-1, a proteína inibidora da Raf cinase (RKIP), a fosfohistidina fosfatase (PHP), entre outras proteínas regulatórias cuja expressão se demonstrou ser afetada por fatores nutricionais e de stress. Tudo isto demonstra a utilidade da proteómica no contexto da aquacultura, particularmente na área do bem-estar animal, como uma ferramenta sensível na deteção de sinais de stress fisiológico/celular e desvios metabólicos, mesmo quando estes não são aparentes mediante o recurso a técnicas clássicas.Universidade do Algarve, Faculdade de Ciências e Tecnologi

Avian muscle development and growth mechanisms: association with muscle myopathies and meat quality Volume II

open2siGiven the significant interest in Volume I, it was decided to launch Volume II of the Research Topic “Avian Muscle Development and Growth Mechanisms: Association With Muscle Myopathies and Meat Quality.” The broiler industry is still facing an unsustainable occurrence of growth-related muscular abnormalities that mainly affect fast-growing genotypes selected for high growth rate and breast yield. From their onset, research interest in these issues continues as proven by the temporal trend of published papers during the past decade (Figure 1). Even if meat affected by white striping, wooden breast, and spaghetti meat abnormalities is not harmful for human nutrition, these conditions impair quality traits of both raw and processed meat products causing severe economic losses in the poultry industry worldwide (Petracci et al., 2019; Velleman, 2019). Since the Research Topic of “Avian Muscle Development and Growth Mechanisms: Association With Muscle Myopathies and Meat Quality” is quite diverse, contributions in this second volume reflect the broad scope of areas of investigation related to muscle growth and development with 11 original research papers and one mini-review from prominent scientists in the sector. We hope that this collection will instigate novel questions in the minds of our readers and will be helpful in facilitating the development of the field.openMassimiliano Petracci; Sandra G. VellemanMassimiliano Petracci; Sandra G. Vellema