Preparation and characterization of magnetite (Fe3O4) nanoparticles By Sol-Gel method

The magnetite (Fe3O4) nanoparticles were successfully synthesized and annealed under vacuum at different temperature. The Fe3O4 nanoparticles prepared via sol-gel assisted method and annealed at 200-400ºC were characterized by Fourier Transformation Infrared Spectroscopy (FTIR), X-ray Diffraction spectra (XRD), Field Emission Scanning Electron Microscope (FESEM) and Atomic Force Microscopy (AFM). The XRD result indicate the presence of Fe3O4 nanoparticles, and the Scherer`s Formula calculated the mean particles size in range of 2-25 nm. The FESEM result shows that the morphologies of the particles annealed at 400ºC are more spherical and partially agglomerated, while the EDS result indicates the presence of Fe3O4 by showing Fe-O group of elements. AFM analyzed the 3D and roughness of the sample; the Fe3O4 nanoparticles have a minimum diameter of 79.04 nm, which is in agreement with FESEM result. In many cases, the synthesis of Fe3O4 nanoparticles using FeCl3 and FeCl2 has not been achieved, according to some literatures, but this research was able to obtained Fe3O4 nanoparticles base on the characterization results

Classification of microarray gene expression cancer data by using artificial intelligence methods

Günümüzde bilgisayar teknolojilerinin gelişmesi ile birçok alanda yapılan çalışmaları etkilemiştir. Moleküler biyoloji ve bilgisayar teknolojilerinde meydana gelen gelişmeler biyoinformatik adlı bilimi ortaya çıkarmıştır. Biyoinformatik alanında meydana gelen hızlı gelişmeler, bu alanda çözülmeyi bekleyen birçok probleme çözüm olma yolunda büyük katkılar sağlamıştır. DNA mikroarray gen ekspresyonlarının sınıflandırılması da bu problemlerden birisidir. DNA mikroarray çalışmaları, biyoinformatik alanında kullanılan bir teknolojidir. DNA mikroarray veri analizi, kanser gibi genlerle alakalı hastalıkların teşhisinde çok etkin bir rol oynamaktadır. Hastalık türüne bağlı gen ifadeleri belirlenerek, herhangi bir bireyin hastalıklı gene sahip olup olmadığı büyük bir başarı oranı ile tespit edilebilir. Bireyin sağlıklı olup olmadığının tespiti için, mikroarray gen ekspresyonları üzerinde yüksek performanslı sınıflandırma tekniklerinin kullanılması büyük öneme sahiptir. DNA mikroarray’lerini sınıflandırmak için birçok yöntem bulunmaktadır. Destek Vektör Makinaları, Naive Bayes, k-En yakın Komşu, Karar Ağaçları gibi birçok istatistiksel yöntemler yaygın olarak kullanlmaktadır. Fakat bu yöntemler tek başına kullanıldığında, mikroarray verilerini sınıflandırmada her zaman yüksek başarı oranları vermemektedir. Bu yüzden mikroarray verilerini sınıflandırmada yüksek başarı oranları elde etmek için yapay zekâ tabanlı yöntemlerin de kullanılması yapılan çalışmalarda görülmektedir. Bu çalışmada, bu istatistiksel yöntemlere ek olarak yapay zekâ tabanlı ANFIS gibi bir yöntemi kullanarak daha yüksek başarı oranları elde etmek amaçlanmıştır. İstatistiksel sınıflandırma yöntemleri olarak K-En Yakın Komşuluk, Naive Bayes ve Destek Vektör Makineleri kullanılmıştır. Burada Göğüs ve Merkezi Sinir Sistemi kanseri olmak üzere iki farklı kanser veri seti üzerinde çalışmalar yapılmıştır. Sonuçlardan elde edilen bilgilere göre, genel olarak yapay zekâ tabanlı ANFIS tekniğinin, istatistiksel yöntemlere göre daha başarılı olduğu tespit edilmiştir

Bayesian statistical analysis of bacterial diversity

Bacteria play an important role in many ecological systems. The molecular characterization of bacteria using either cultivation-dependent or cultivation-independent methods reveals the large scale of bacterial diversity in natural communities, and the vastness of subpopulations within a species or genus. Understanding how bacterial diversity varies across different environments and also within populations should provide insights into many important questions of bacterial evolution and population dynamics. This thesis presents novel statistical methods for analyzing bacterial diversity using widely employed molecular fingerprinting techniques. The first objective of this thesis was to develop Bayesian clustering models to identify bacterial population structures. Bacterial isolates were identified using multilous sequence typing (MLST), and Bayesian clustering models were used to explore the evolutionary relationships among isolates. Our method involves the inference of genetic population structures via an unsupervised clustering framework where the dependence between loci is represented using graphical models. The population dynamics that generate such a population stratification were investigated using a stochastic model, in which homologous recombination between subpopulations can be quantified within a gene flow network. The second part of the thesis focuses on cluster analysis of community compositional data produced by two different cultivation-independent analyses: terminal restriction fragment length polymorphism (T-RFLP) analysis, and fatty acid methyl ester (FAME) analysis. The cluster analysis aims to group bacterial communities that are similar in composition, which is an important step for understanding the overall influences of environmental and ecological perturbations on bacterial diversity. A common feature of T-RFLP and FAME data is zero-inflation, which indicates that the observation of a zero value is much more frequent than would be expected, for example, from a Poisson distribution in the discrete case, or a Gaussian distribution in the continuous case. We provided two strategies for modeling zero-inflation in the clustering framework, which were validated by both synthetic and empirical complex data sets. We show in the thesis that our model that takes into account dependencies between loci in MLST data can produce better clustering results than those methods which assume independent loci. Furthermore, computer algorithms that are efficient in analyzing large scale data were adopted for meeting the increasing computational need. Our method that detects homologous recombination in subpopulations may provide a theoretical criterion for defining bacterial species. The clustering of bacterial community data include T-RFLP and FAME provides an initial effort for discovering the evolutionary dynamics that structure and maintain bacterial diversity in the natural environment

Reverse-engineering transcriptional modules from gene expression data

"Module networks" are a framework to learn gene regulatory networks from expression data using a probabilistic model in which coregulated genes share the same parameters and conditional distributions. We present a method to infer ensembles of such networks and an averaging procedure to extract the statistically most significant modules and their regulators. We show that the inferred probabilistic models extend beyond the data set used to learn the models.Comment: 5 pages REVTeX, 4 figure

Bioinformatics: a knowledge engineering approach

The paper introduces the knowledge engineering (KE) approach for the modeling and the discovery of new knowledge in bioinformatics. This approach extends the machine learning approach with various rule extraction and other knowledge representation procedures. Examples of the KE approach, and especially of one of the recently developed techniques - evolving connectionist systems (ECOS), to challenging problems in bioinformatics are given, that include: DNA sequence analysis, microarray gene expression profiling, protein structure prediction, finding gene regulatory networks, medical prognostic systems, computational neurogenetic modeling