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Analiza signalov membranske kapacitivnosti živih celic
ID MISLEJ, JERNEJA (Author), ID Kononenko, Igor (Mentor) More about this mentor... This link opens in a new window, ID Kreft, Marko (Co-mentor)

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PID: 20.500.12556/rul/d60fce9c-61c0-42ba-bb07-8bcd45f733f1

Abstract
Namen diplomskega dela je (pol)avtomatska detekcija skokovitih sprememb v signalu membranske kapacitivnosti celic, in sicer sprememb, ki so posledice eksocitoze ali endocitoze. Ustrezne spremembe v signalu so v preteklosti že detektirali avtomatsko, in sicer s pomočjo matematične analize signala, vendar pa je matematična analiza možna le, če je razmerje med signalom in šumom dovolj veliko. Pri nekaterih tipih celic so amplitude iskanih sprememb tako majhne, da se skoraj popolnoma skrijejo v šumu in preprosta matematična analiza ni več možna. Posledično detekcija ustreznih skokovitih sprememb trenutno poteka ročno. Označenih je bilo že kar nekaj signalov in tako se ponuja možnost olajšanja ročnega pregledovanja s pomočjo strojnega učenja, kar je delo te diplomske naloge. Podatki se na začetku predobdelajo, pri čemer se iz meritve odstranijo kalibracijski pulzi, obsežno šumni predeli meritve ter popravi bazna linija signala, ki predstavlja meritev. Sledi priprava množice podatkov, ki zajema ekstrakcijo učnih primerov, izračun atributov učnih primerov in prevzorčenje učne množice. Zatem se na podlagi učne in testne množice ter različnih metod strojnega učenja predstavijo in primerjajo rezultati klasifikacij novih primerov.

Language:Slovenian
Keywords:Eksocitoza, endocitoza, klasifikacija, membranska kapacitivnost celice, signal, skokovita sprememba, strojno učenje, šum
Work type:Bachelor thesis/paper (mb11)
Organization:FRI - Faculty of computer and information science
Year:2015
PID:20.500.12556/RUL-30654 This link opens in a new window
Publication date in RUL:19.03.2015
Views:1640
Downloads:389
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Secondary language

Language:English
Title:Living Cells Membrane Capacitance Signal analysis
Abstract:
The purpose of the thesis is a (semi)automatic detection of rapid changes in the cell membrane capacitance signal, namely changes that are a consequence of exocytosis and endocytosis. Corresponding changes in the signal were previously detected automatically, using mathematical signal analysis. However, mathematical analysis is only possible when the signal-to-noise ratio is sufficiently large. In some cell types the amplitude of the change is so small that it is almost completely hidden in the noise, therefore simple mathematical analysis is no longer possible. Consequently, the detection of the corresponding rapid changes is currently done manually. A number of signals have been labeled and so offers a possibility of facilitating the manual inspection by applying machine learning, which is the work of this thesis. The data is initially pre-processed, where calibration pulses and areas with extensive noise are removed from the measurements and the baseline of the signal representing the measurement is corrected. This is followed by a preparation of the dataset, which includes the extraction of instances, calculation of the attributes of instances and resampling of the training dataset. Then, based on the training set, test set and a variety of machine learning methods, results of the classifications of new cases are presented and compared.

Keywords:Exocytosis, endocytosis, cell membrane capacitance, classification, signal, rapid change, machine learning, noise

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