Prediction of peptides binding to MHC class I alleles by partial periodic pattern mining

MHC (Major Histocompatibility Complex) is a key player in the immune response of an organism. It is important to be able to predict which antigenic peptides will bind to a specific MHC allele and which will not, creating possibilities for controlling immune response and for the applications of immunotherapy. However, a problem for MHC class I is the presence of bulges and loops in the peptides, changing the total length. Most machine learning methods in use today require the sequences to be of same length to successfully mine the binding motifs. We propose the use of time-based data mining methods in motif mining to be able to mine motifs position-independently. Also, the information for both binding and non-binding peptides is used on the contrary to the other methods which only rely on binding peptides. The prediction results are between 60-95% for the tested alleles

Prediction of peptides binding to MHC class I alleles by partial periodic pattern mining

MHC (Major Histocompatibility Complex) is a key player in the immune response of an organism. It is important to be able to predict which antigenic peptides will bind to a spe-cific MHC allele and which will not, creating possibilities for controlling immune response and for the applications of immunotherapy. However a problem encountered in the computational binding prediction methods for MHC class I is the presence of bulges and loops in the peptides, changing the total length. Most machine learning methods in use to-day require the sequences to be of same length to success-fully mine the binding motifs. We propose the use of time-based data mining methods in motif mining to be able to mine motifs position-independently. Also, the information for both binding and non-binding peptides are used on the contrary to the other methods which only rely on binding peptides. The prediction results are between 70-80% for the tested alleles

Discovering discriminative and class-specific sequence and structural motifs in proteins

Finding recurring motifs is an important problem in bioinformatics. Such motifs can be used for any number of problems including sequence classi cation, label prediction, knowledge discovery and biological engineering of proteins t for a speci c purpose. Our motivation is to create a better foundation for the research and development of novel motif mining and machine learning methods that can extract class-speci c and discriminative motifs using both sequence and structural features. We propose the building blocks of a general machine learning framework to act on a biological input. This thesis present a combination of elements that are aimed to be applicable to a variety of biological problems. Ideally, the learner should only require a number of biological data instances as input that are classi- ed into a number of di erent classes as de ned by the researchers. The output should be the factors and motifs that discriminate between those classes (for reasonable, non-random class de nitions). This ideal work ow requires two main steps. First step is the representation of the biological input with features that contain the signi cant information the researcher is looking for. Due to the complexity of the macromolecules, abstract representations are required to convert the real world representation into quanti able descriptors that are suitable for motif mining and machine learning. The second step of the proposed work ow is the motif mining and knowledge discovery step. Using these informative representations, an algorithm should be able to nd discriminative, class-speci c motifs that are over-represented in one class and under-represented in the other. This thesis presents novel procedures for representation of the proteins to be used in a variety of machine learning algorithms, and two separate motif mining algorithms, one based on temporal motif mining, and the other on deep learning, that can work with the given biological data. The descriptors and the learners are applied to a wide range of computational problems encountered in life sciences

Management skills training needs analysis of company and battalion commanders in the Turkish army

Cataloged from PDF version of article.The Turkish Army like other organizations tries to keep up with the change in all areas and uses some methods of change. One of the areas is management and the method of change used by The Army is training and development of its officers at managerial positions. The army needs to know whether its officers need training or not. A needs analysis should be done for this. This study tries to determine whether the officers at two managerial positions, company and battalion commanders, need training in nine topics of management skills. The topics are: problem solving, stress management, organizing/coordinating, conflict management, motivating, coaching and counseling, team building, communication, empowering/delegating. A questionnaire is developed and used to collect the data. Determining the needs is the first step of designing training programs.Meydan, Cem HarunM.S

Prediction of peptides binding to MHC class I and II alleles by temporal motif mining

Background: MHC (Major Histocompatibility Complex) is a key player in the immune response of most vertebrates. The computational prediction of whether a given antigenic peptide will bind to a specific MHC allele is important in the development of vaccines for emerging pathogens, the creation of possibilities for controlling immune response, and for the applications of immunotherapy. One of the problems that make this computational prediction difficult is the detection of the binding core region in peptides, coupled with the presence of bulges and loops causing variations in the total sequence length. Most machine learning methods require the sequences to be of the same length to successfully discover the binding motifs, ignoring the length variance in both motif mining and prediction steps. In order to overcome this limitation, we propose the use of time-based motif mining methods that work position-independently. Results: The prediction method was tested on a benchmark set of 28 different alleles for MHC class I and 27 different alleles for MHC class II. The obtained results are comparable to the state of the art methods for both MHC classes, surpassing the published results for some alleles. The average prediction AUC values are 0.897 for class I, and 0.858 for class II. Conclusions: Temporal motif mining using partial periodic patterns can capture information about the sequences well enough to predict the binding of the peptides and is comparable to state of the art methods in the literature. Unlike neural networks or matrix based predictors, our proposed method does not depend on peptide length and can work with both short and long fragments. This advantage allows better use of the available training data and the prediction of peptides of uncommon lengths

Prediction of peptides binding to MHC class I and II alleles by temporal motif mining

Background: MHC (Major Histocompatibility Complex) is a key player in the immune response of most vertebrates. The computational prediction of whether a given antigenic peptide will bind to a specific MHC allele is important in the development of vaccines for emerging pathogens, the creation of possibilities for controlling immune response, and for the applications of immunotherapy. One of the problems that make this computational prediction difficult is the detection of the binding core region in peptides, coupled with the presence of bulges and loops causing variations in the total sequence length. Most machine learning methods require the sequences to be of the same length to successfully discover the binding motifs, ignoring the length variance in both motif mining and prediction steps. In order to overcome this limitation, we propose the use of time-based motif mining methods that work position-independently. Results: The prediction method was tested on a benchmark set of 28 different alleles for MHC class I and 27 different alleles for MHC class II. The obtained results are comparable to the state of the art methods for both MHC classes, surpassing the published results for some alleles. The average prediction AUC values are 0.897 for class I, and 0.858 for class II. Conclusions: Temporal motif mining using partial periodic patterns can capture information about the sequences well enough to predict the binding of the peptides and is comparable to state of the art methods in the literature. Unlike neural networks or matrix based predictors, our proposed method does not depend on peptide length and can work with both short and long fragments. This advantage allows better use of the available training data and the prediction of peptides of uncommon lengths

Bir Örgüt Formu Olarak Okul ile Özdeşleşmede Akademik Başarı ve Özdisiplinin Rolü

This study aims to identify the relationship between students’ organizational identification with their personality and academic success. Conscientiousness, one of the most important five personality traits, has been taken as a personality trait involving submissiveness, orderliness, discipline, responsibility, and achievement orientation. Furthermore, it is a result of learning and socialization while functioning as an evaluator. Considering schools, students’ academic success is an important factor since students who have a high level of academic success try to achieve their personal goals that contribute to schools’ overall goals. Organizational identification as the other variable of the study is defined as a cognitive linking between the definition of the organization and the definition of the self. Considering the students who have high level of academic success and have self-discipline, they would identify themselves with their school. Taking this as the hypothesis of the study, data have been gathered from 506 collage students. Research results showed that individuals’ time spent at school and their conscientiousness were effective while academic success was not effective on their organizational identificationBu çalışma, öğrencilerin okulları ile özdeşleşmeleri ile akademik başarı ve kişilikleri arasındaki ilişkileri ortaya koymayı hedeflemektedir. Kişilik değişkeni olarak Beş Faktör Kişilik özelliklerinden özdisiplin ele alınmıştır. Özdisiplin kişilik özelliği, itaatkârlık, düzenlilik, disiplin, sorumluluk, başarı yönelimlilik gibi özellikleri bünyesinde barındırmakta, bu özelliklerin yanında öğrenme ve sosyalleşmenin sonucu olmakta ve değerlendirici bir yönü de bulunmaktadır. Araştırmanın diğer değişkeni olan özdeşleşme, bireyin kendi benliğiyle örgütünü tanımlaması arasındaki bilişsel bağ olarak ifade edilmektedir. Okullar değerlendirildiğinde öğrencilerin akademik başarılarının okullar için önemi ön plana çıkmaktadır. Bu bağlamda, akademik başarısı yüksek ve bireysel hedeflerini gerçekleştirmeye çalışan öğrenciler aynı zamanda okulun hedeflerine de katkıda bulunmaktadır. Akademik başarı seviyesi yüksek ve özdisiplinli öğrencilerin kendilerini okullarıyla tanımlama düzeylerinin yüksek olacağı değerlendirilerek hipotezler geliştirilmiş ve geliştirilen hipotezler 506 üniversite öğrencisinden toplanan veri ile analiz yapılarak test edilmiştir. Veriler anket yöntemi ile toplanmış, bu maksatla özdeşleşme ve özdisiplin ölçekleri kullanılmıştır. Araştırma sonuçları, öğrencilerin okulda bulundukları süre ile özdisiplinli olmalarının okulları ile özdeşleşmede etkili faktörler olduğuna, ancak akademik başarının etkili bir faktör olmadığına işaret etmektedir

Optimization of morphological data in numerical taxonomy analysis using genetic algorithms feature selection method

Studies in Numerical Taxonomy are carried out by measuring characters as much as possible. The workload over scientists and labor to perform measurements will increase proportionally with the number of variables (or characters) to be used in the study. However, some part of the data may be irrelevant or sometimes meaningless. Here in this study, we introduce an algorithm to obtain a subset of data with minimum characters that can represent original data. Morphological characters were used in optimization of data by Genetic Algorithms Feature Selection method. The analyses were performed on an 18 character*11 taxa data matrix with standardized continuous characters. The analyses resulted in a minimum set of 2 characters, which means the original tree based on the complete data can also be constructed by those two characters

Kişilerarası Çatışma Çözme Yaklaşımlarında Kontrol Odağının Rolü

Bu çalışmada, iletişim süreçlerine odaklanan kişilerarasıçatışma çözme yaklaşımlarıile kişisel bir değişken olan kontrol odağının bu yaklaşımların benimsenmesinde herhangi bir rolünün olup olmadığının araştırılmasıamaçlanmıştır. Araştırmanın örneklemini Ankara’da öğrenim gören ve yaşları18 ile 26 arasında değişen 307 üniversite öğrencisi oluşmaktadır. Araştırmada anket yöntemiyle veri toplanmışve ölçüm araçlarıolarak; ulusal kültüre uyarlama çalışmalarıyapılmışolan KişilerarasıÇatışma Çözme YaklaşımlarıÖlçeği ve Kontrol OdağıÖlçeği kullanılmıştır. Elde edilen bulgular; özellikle iç kontrol odaklıkişilerin yapıcıve olumlu çözüm süreçleri açısından yüzleşmeye daha çok önem verdiklerini, genel davranışsergilediklerini, çatışmaya yaklaştıklarını, kendilerini daha çok açtıklarınıve duygularınıdaha fazla sergilediklerini göstermektedir. Bununla birlikte demografik değişkenlerden cinsiyetin de çözüm yaklaşımlarının benimsenmesinde önemli rol oynadığıve kadınların çatışma süreçlerinde daha fazla yüzleştikleri, kendilerini daha çok açtıklarıve daha fazla duygusal ifadeler sergileyerek çözüme yönelik daha aktif ve etkili davranışlarda bulunduklarıortaya çıkmıştır. Tüm bu bulgular, kişilerarasıçatışma çözme süreçlerinde kişilik ve cinsiyet değişkenlerinin önemli rol oynadığınıgöstermektedir