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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Vasilopoulou Christina;

    Στους τομείς της Συστημικής Βιολογίας και της Βιοπληροφορικής, μία από τα μεγαλύτερες σύγχρονες προκλήσεις στην ανάπτυξη υπολογιστικών εξατομικευμένων προσεγγίσεων σύνθετων ασθενειών είναι ο τρόπος με τον οποίο μπορούν να εξαχθούν, να συνδυαστούν και να αναπαρασταθούν με τον βέλτιστο τρόπο σύνολα multi- omics και κλινικών δεδομένων από δείγματα ασθενών. Απώτερος σκοπός της παρούσας εργασίας ήταν η ανάδειξη της βελτιωμέ- νης απόδοσης που μπορεί να προσφέρουν σε ένα μοντέλο πρόγνωσης αναπα- ραστάσεις υψηλότερης πολυπλοκότητας όπως είναι η τοπολογία ενός ολο- κληρωμένου/ διασυνδεδεμένου δικτύου το οποίο συνδυάζει -omics δεδομένα πολλαπλών κυτταρικών επιπέδων σε σύγκριση με χαμηλότερης πολυπλοκό- τητας αναπαραστάσεις όπως είναι το διάνυσμα χαρακτηριστικών. Συγκεκριμένα, στην παρούσα εργασία πραγματοποιήθηκε εξόρυξη -omics δεδομένων από την τράπεζα δεδομένων Genomic Data Commons από 412 ασθενείς που πάσχουν από μυοδιηθητικό ουροθηλιακό καρκίνωμα της ουρο- δόχου κύστης. Τα δεδομένα που συγκεντρώθηκαν αφορούν επίπεδα έκφρασης γονιδίων και miRNA (microRNA) καθώς και εκτίμηση επιπέδων μεθυλίωσης περιοχών του γονιδιώματος από δείγματα τα οποία έχουν ληφθεί τόσο από τον πρωτογενή όγκο όσο και από φυσιολογικό ιστό του ιδίου οργάνου. Σε αυτό το πλαίσιο, καταφέραμε να κατασκευάσουμε ένα βελ- τιστοποιημένο αλγόριθμο εξόρυξης και επεξεργασίας της επιθυμητής βιολο- γικής πληροφορίας από 1250 αρχεία και 435 δείγματα, με απώτερο σκοπό τη πολλαπλή αναπαράσταση των δεδομένων με τη μορφή διανύσματος χαρα- κτηριστικών, δικτύου και τοπολογικού διανύσματος χαρακτηριστικών. Για την εκτίμηση της βελτίωσης της απόδοσης που μπορεί να προσφέρει κάθε ανα- παράσταση πραγματοποιήθηκαν διερευνητικές αναλύσεις και πειράματα in silico που πραγματεύονται προβλήματα ταξινόμησης. Επιλέγοντας τα πιο δια- φοροποιημένα χαρακτηριστικά πραγματοποιήθηκε κατασκευή του ολο- κληρωμένου δικτύου για το μυοδιηθητικό καρκίνωμα της ουροδόχου κύστης. Στη συνέχεια, σχεδιάσαμε και κατασκευάσαμε την εξατομικευμένη αναπαρά- σταση ενός ασθενούς αυτή τη φορά με τη μορφή δικτύου. Συμπερασματικά, η διανυσματική αναπαράσταση προσφέρει βέλτιστα αποτελέσματα στο πρό- βλημα ταξινόμησης κατηγορίας δειγμάτων, χρησιμοποιώντας τον αλγόριθμο του k- κοντινότερου γείτονα και των δέντρων απόφασης. Η τοπολογική ανα- παράσταση προσφέρει καλύτερη απόδοση στο μοντέλο ταξινόμησης των στα- δίων καρκίνου (tumor stage) σε σχέση με την διανυσματική, χρησιμοποιώντας τον αλγόριθμο του k- κοντινότερου γείτονα. One of the greatest modern challenges in the fields of Systemic Biology and Bioinformatics is the development of personalized computational approaches in complex diseases that are able to extract, combine and represent multi-omics and clinical data from samples of patients, in an optimal way. The ultimate goal of this work was to highlight the potentially improved performance of a prediction model that employ higher complexity representations such as the topology vector of a multi-omics integrated network, compared to lower complexity representations such as a feature vector. Specifically, in the present study, we performed data mining from the Genomic Data Commons database of 412 patients that suffer from muscle-invasive bladder urothelial carcinoma. The final dataset comprises of gene and miRNA (microRNA) expression data as well as DNA methylation data, extracted from both primary tumor and normal tissue samples. In this context, we succeeded in constructing an optimized algorithm for mining and processing the desired biological information from a total of 1250 files and 435 samples, as well as constructing multiple representations of the data in the form of a feature vector, network and topology vector. In addition, the reconstruction of the integrated network for the bladder urothelial carcinoma was carried out, selecting only the most differentiated features of all patients. We then designed and built a personalized network representation of each patient. Exploratory analyses and in silico experiments dealing with classification problems have been performed in order to investigate the performance of each representation. In conclusion, the feature vector representation provides optimal results for the sample class classification problem, using both the k-nearest neighbor and the decision tree algorithms. Topological representation provides better performance than the feature vector in the tumor stage classification task using the k-nearest neighbor algorithm.

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    Pergamos
    2019
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      Pergamos
      2019
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    Подано базовий теоретичний матеріал з нечіткої логіки, алгоритмів прийняття рішень та синтезу нечітких моделей. Наведено приклади розв’язання типових задач та запропоновано завдання для виконання робіт з нечіткої логіки за навчальною дисципліною «Інтелектуальний аналіз даних для задач енергетики». Крім методичних вказівок до виконання комп’ютерного практикуму, навчальний посібник містить загальні відомості про пакет Matlab.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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    Экз. деф.: повреждены с. 3-4

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  • Authors: Sugianela, Y. (Yuna); Sutino, Q. L. (Qonita); Herumurti, D. (Darlis);

    EEG (electroencephalogram) can detect epileptic seizures by neurophysiologists in clinical practice with visually scan long recordings. Epilepsy seizure is a condition of brain disorder with chronic noncommunicable that affects people of all ages. The challenge of study is how to develop a method for signal processing that extract the subtle information of EEG and use it for automating the detection of epileptic with high accuration, so we can use it for monitoring and treatment the epileptic patient. In this study we developed a method to classify the EEG signal based on Wavelet Packet Decomposition that decompose the EEG signal and Random Forest for seizure detetion. The result of study shows that Random Forest classification has the best performance than KNN, ANN, and SVM. The best combination of statisctical features is standard deviation, maximum and minimum value, and bandpower. WPD is has best decomposition in 5th level.

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  • Authors: Zarmiyeni, Z. (Zarmiyeni);

    Oyster mushroom (Pleurotus ostreatus) is one type of wood mushroom that is used as a vegetable and processed food. In the cultivation of oyster mushrooms (Pleurotus ostreatus), seeds are a determining factor. This study aims to obtain the best media composition in the growth of oyster mushroom F2 seedlings (Pleurotus ostreatus). The research conducted at the Amuntai STIPER Laboratory from April to June 2013, using a Completely Randomized Design (CRD), with 5 treatments and 4 replications. As a treatment, the composition of media (K) is (k1) 80% corn: 18% sawdust: 1% CaCO3: 1% CaSO4, (k2) 70% corn: 28% sawdust: 1% CaCO3: 1% CaSO4, ( k3) 60% corn: 38% sawdust: 1% CaCO3: 1% CaSO4, (k4) 50% corn: 48% sawdust: 1% CaCO3: 1% CaSO4, (k5) 40% corn: 58% sawdust : 1% CaCO3: 1% CaSO4. Observation data were analyzed by variance and DMRT follow-up at 5% level. The parameters observed were the growth of mycelium horizontally and vertically. The results showed that media composition (k2) 70% corn: 28% sawdust: 1% CaCO3: 1% CaSO4 gave the best results for F2 seedling growth

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  • Authors: Rakhmadi, Arwin Juli;

    Astronomy is special scientific discipline in Islam as it relates to daily Muslim worship practices. In the Mamalik era (1250-1517), especially in Cairo and Damascus regions, Islamic astronomy has developed significantly as can be seen in the new discoveries of astronomical instruments. However, Islamic astronomy in the era of Mamalik conceals a very large amount of scientific knowledge that needs further discussion. As mentioned by King, there were a dozen of Islamic schbolars in astronomy whose treatises and ideas are unrevealed. Therefore contemporary astronomical studies run in higher educational institutions, should also seriously be focused on examining classical knowledge in astronomy. This article discusses the history and develop- ment of Islamic astronomy in the era of Mamalik and their contribution to the body of scientific knowledge in as- tronomy in that period

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    Трехкрасочная печать, бумага

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    Authors: Herbarium, Bolus;

    Kingdom: PlantaeDivision: MagnoliophytaClass: EudicotsOrder: AsteralesFamily: AsteraceaeScientific name: Helichrysum felinum Less.Specimen barcode: 159961

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    ZivaHub - Open Data UCT
    Other ORP type . 2023
    License: CC BY
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      ZivaHub - Open Data UCT
      Other ORP type . 2023
      License: CC BY
7,799,945 Research products
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    Authors: Vasilopoulou Christina;

    Στους τομείς της Συστημικής Βιολογίας και της Βιοπληροφορικής, μία από τα μεγαλύτερες σύγχρονες προκλήσεις στην ανάπτυξη υπολογιστικών εξατομικευμένων προσεγγίσεων σύνθετων ασθενειών είναι ο τρόπος με τον οποίο μπορούν να εξαχθούν, να συνδυαστούν και να αναπαρασταθούν με τον βέλτιστο τρόπο σύνολα multi- omics και κλινικών δεδομένων από δείγματα ασθενών. Απώτερος σκοπός της παρούσας εργασίας ήταν η ανάδειξη της βελτιωμέ- νης απόδοσης που μπορεί να προσφέρουν σε ένα μοντέλο πρόγνωσης αναπα- ραστάσεις υψηλότερης πολυπλοκότητας όπως είναι η τοπολογία ενός ολο- κληρωμένου/ διασυνδεδεμένου δικτύου το οποίο συνδυάζει -omics δεδομένα πολλαπλών κυτταρικών επιπέδων σε σύγκριση με χαμηλότερης πολυπλοκό- τητας αναπαραστάσεις όπως είναι το διάνυσμα χαρακτηριστικών. Συγκεκριμένα, στην παρούσα εργασία πραγματοποιήθηκε εξόρυξη -omics δεδομένων από την τράπεζα δεδομένων Genomic Data Commons από 412 ασθενείς που πάσχουν από μυοδιηθητικό ουροθηλιακό καρκίνωμα της ουρο- δόχου κύστης. Τα δεδομένα που συγκεντρώθηκαν αφορούν επίπεδα έκφρασης γονιδίων και miRNA (microRNA) καθώς και εκτίμηση επιπέδων μεθυλίωσης περιοχών του γονιδιώματος από δείγματα τα οποία έχουν ληφθεί τόσο από τον πρωτογενή όγκο όσο και από φυσιολογικό ιστό του ιδίου οργάνου. Σε αυτό το πλαίσιο, καταφέραμε να κατασκευάσουμε ένα βελ- τιστοποιημένο αλγόριθμο εξόρυξης και επεξεργασίας της επιθυμητής βιολο- γικής πληροφορίας από 1250 αρχεία και 435 δείγματα, με απώτερο σκοπό τη πολλαπλή αναπαράσταση των δεδομένων με τη μορφή διανύσματος χαρα- κτηριστικών, δικτύου και τοπολογικού διανύσματος χαρακτηριστικών. Για την εκτίμηση της βελτίωσης της απόδοσης που μπορεί να προσφέρει κάθε ανα- παράσταση πραγματοποιήθηκαν διερευνητικές αναλύσεις και πειράματα in silico που πραγματεύονται προβλήματα ταξινόμησης. Επιλέγοντας τα πιο δια- φοροποιημένα χαρακτηριστικά πραγματοποιήθηκε κατασκευή του ολο- κληρωμένου δικτύου για το μυοδιηθητικό καρκίνωμα της ουροδόχου κύστης. Στη συνέχεια, σχεδιάσαμε και κατασκευάσαμε την εξατομικευμένη αναπαρά- σταση ενός ασθενούς αυτή τη φορά με τη μορφή δικτύου. Συμπερασματικά, η διανυσματική αναπαράσταση προσφέρει βέλτιστα αποτελέσματα στο πρό- βλημα ταξινόμησης κατηγορίας δειγμάτων, χρησιμοποιώντας τον αλγόριθμο του k- κοντινότερου γείτονα και των δέντρων απόφασης. Η τοπολογική ανα- παράσταση προσφέρει καλύτερη απόδοση στο μοντέλο ταξινόμησης των στα- δίων καρκίνου (tumor stage) σε σχέση με την διανυσματική, χρησιμοποιώντας τον αλγόριθμο του k- κοντινότερου γείτονα. One of the greatest modern challenges in the fields of Systemic Biology and Bioinformatics is the development of personalized computational approaches in complex diseases that are able to extract, combine and represent multi-omics and clinical data from samples of patients, in an optimal way. The ultimate goal of this work was to highlight the potentially improved performance of a prediction model that employ higher complexity representations such as the topology vector of a multi-omics integrated network, compared to lower complexity representations such as a feature vector. Specifically, in the present study, we performed data mining from the Genomic Data Commons database of 412 patients that suffer from muscle-invasive bladder urothelial carcinoma. The final dataset comprises of gene and miRNA (microRNA) expression data as well as DNA methylation data, extracted from both primary tumor and normal tissue samples. In this context, we succeeded in constructing an optimized algorithm for mining and processing the desired biological information from a total of 1250 files and 435 samples, as well as constructing multiple representations of the data in the form of a feature vector, network and topology vector. In addition, the reconstruction of the integrated network for the bladder urothelial carcinoma was carried out, selecting only the most differentiated features of all patients. We then designed and built a personalized network representation of each patient. Exploratory analyses and in silico experiments dealing with classification problems have been performed in order to investigate the performance of each representation. In conclusion, the feature vector representation provides optimal results for the sample class classification problem, using both the k-nearest neighbor and the decision tree algorithms. Topological representation provides better performance than the feature vector in the tumor stage classification task using the k-nearest neighbor algorithm.

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    Подано базовий теоретичний матеріал з нечіткої логіки, алгоритмів прийняття рішень та синтезу нечітких моделей. Наведено приклади розв’язання типових задач та запропоновано завдання для виконання робіт з нечіткої логіки за навчальною дисципліною «Інтелектуальний аналіз даних для задач енергетики». Крім методичних вказівок до виконання комп’ютерного практикуму, навчальний посібник містить загальні відомості про пакет Matlab.

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    Экз. деф.: повреждены с. 3-4

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  • Authors: Sugianela, Y. (Yuna); Sutino, Q. L. (Qonita); Herumurti, D. (Darlis);

    EEG (electroencephalogram) can detect epileptic seizures by neurophysiologists in clinical practice with visually scan long recordings. Epilepsy seizure is a condition of brain disorder with chronic noncommunicable that affects people of all ages. The challenge of study is how to develop a method for signal processing that extract the subtle information of EEG and use it for automating the detection of epileptic with high accuration, so we can use it for monitoring and treatment the epileptic patient. In this study we developed a method to classify the EEG signal based on Wavelet Packet Decomposition that decompose the EEG signal and Random Forest for seizure detetion. The result of study shows that Random Forest classification has the best performance than KNN, ANN, and SVM. The best combination of statisctical features is standard deviation, maximum and minimum value, and bandpower. WPD is has best decomposition in 5th level.

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  • Authors: Zarmiyeni, Z. (Zarmiyeni);

    Oyster mushroom (Pleurotus ostreatus) is one type of wood mushroom that is used as a vegetable and processed food. In the cultivation of oyster mushrooms (Pleurotus ostreatus), seeds are a determining factor. This study aims to obtain the best media composition in the growth of oyster mushroom F2 seedlings (Pleurotus ostreatus). The research conducted at the Amuntai STIPER Laboratory from April to June 2013, using a Completely Randomized Design (CRD), with 5 treatments and 4 replications. As a treatment, the composition of media (K) is (k1) 80% corn: 18% sawdust: 1% CaCO3: 1% CaSO4, (k2) 70% corn: 28% sawdust: 1% CaCO3: 1% CaSO4, ( k3) 60% corn: 38% sawdust: 1% CaCO3: 1% CaSO4, (k4) 50% corn: 48% sawdust: 1% CaCO3: 1% CaSO4, (k5) 40% corn: 58% sawdust : 1% CaCO3: 1% CaSO4. Observation data were analyzed by variance and DMRT follow-up at 5% level. The parameters observed were the growth of mycelium horizontally and vertically. The results showed that media composition (k2) 70% corn: 28% sawdust: 1% CaCO3: 1% CaSO4 gave the best results for F2 seedling growth

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  • Authors: Rakhmadi, Arwin Juli;

    Astronomy is special scientific discipline in Islam as it relates to daily Muslim worship practices. In the Mamalik era (1250-1517), especially in Cairo and Damascus regions, Islamic astronomy has developed significantly as can be seen in the new discoveries of astronomical instruments. However, Islamic astronomy in the era of Mamalik conceals a very large amount of scientific knowledge that needs further discussion. As mentioned by King, there were a dozen of Islamic schbolars in astronomy whose treatises and ideas are unrevealed. Therefore contemporary astronomical studies run in higher educational institutions, should also seriously be focused on examining classical knowledge in astronomy. This article discusses the history and develop- ment of Islamic astronomy in the era of Mamalik and their contribution to the body of scientific knowledge in as- tronomy in that period

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    Трехкрасочная печать, бумага

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    Authors: Herbarium, Bolus;

    Kingdom: PlantaeDivision: MagnoliophytaClass: EudicotsOrder: AsteralesFamily: AsteraceaeScientific name: Helichrysum felinum Less.Specimen barcode: 159961

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    ZivaHub - Open Data UCT
    Other ORP type . 2023
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