Gene set function enrichment analysis using biological networks

Molecular biologists and geneticists are, nowadays, mostly focused on studying and understanding the DNA transcription. The gathered experimental data is difficult to interpret. Classical methods compare the discovered groups of genes with predefined gene sets. Unfortunately, these methods do not perform well. A solution is proposed by the SANTA method, which evaluates the experimental results on discovered gene sets based on known and extensive gene networks. In this thesis, we have implemented the method in Orange Bioinformatics. Orange is a well-known data-analysis programme. The Bioinformatics add-on includes specialised functionality for processing genomic data. Furthermore, we have also enriched its widgets for access to BioGRID and STRING, which store information on functional sets of genes and interactions

Gene set function enrichment analysis using biological networks

Molecular biologists and geneticists are, nowadays, mostly focused on studying and understanding the DNA transcription. The gathered experimental data is difficult to interpret. Classical methods compare the discovered groups of genes with predefined gene sets. Unfortunately, these methods do not perform well. A solution is proposed by the SANTA method, which evaluates the experimental results on discovered gene sets based on known and extensive gene networks. In this thesis, we have implemented the method in Orange Bioinformatics. Orange is a well-known data-analysis programme. The Bioinformatics add-on includes specialised functionality for processing genomic data. Furthermore, we have also enriched its widgets for access to BioGRID and STRING, which store information on functional sets of genes and interactions

Gene set function enrichment analysis using biological networks

Molekularni biologi in genetiki se dandanes ukvarjajo s proučevanjem zapisa DNA. Pri eksperimentih tipično dobijo podatke, ki se zaradi svoje narave težko interpretirajo. Klasične metode primerjajo eksperimentalno dobljeno skupino genov z znanimi, funkcijsko povezanimi skupinami genov, a pogosto niso uspešne. Izboljšava pristopa je metoda SANTA, ki vrednoti eksperimentalne rezultate na podlagi omrežja genov. Metodo SANTA smo implementirali v sklopu diplome, v dodatku Orange Bioinformatics. Orange je že dobro uveljavljen program za obdelavo podatkov. Dodatek Bioinformatics zavzema specializirane funkcionalnosti za obdelavo genskih podatkov. Dodatek smo obogatili tudi z izboljšanim dostopom do zbirk BioGRID in STRING, ki hranita podatke o eksperimentalno določenih povezavah med geni.Molecular biologists and geneticists are, nowadays, mostly focused on studying and understanding the DNA transcription. The gathered experimental data is difficult to interpret. Classical methods compare the discovered groups of genes with predefined gene sets. Unfortunately, these methods do not perform well. A solution is proposed by the SANTA method, which evaluates the experimental results on discovered gene sets based on known and extensive gene networks. In this thesis, we have implemented the method in Orange Bioinformatics. Orange is a well-known data-analysis programme. The Bioinformatics add-on includes specialised functionality for processing genomic data. Furthermore, we have also enriched its widgets for access to BioGRID and STRING, which store information on functional sets of genes and interactions

Recommending items from inventory

Klasični priporočilni sistemi so zelo dobri pri napovedovanju preferenc uporabnikov, pri napovedovanju pa ne upoštevajo dejstva, da so zaloge nekaterih izdelkov omejene. V delu smo predstavili nekaj klasičnih pristopov modeliranja preferenc uporabnikov s pomočjo matričnega razcepa. Predstavili smo dinamičen in statičen priporočilni sistem, ki optimizirata porabo zaloge. Predlagali smo tudi hibridni model, ki lahko združi napovedi priporočilnega sistema, ki modelira preferenco, in priporočilnega sistema, ki optimizira porabo izdelkov. Poleg modela smo predstavili tudi način testiranja uspešnosti sistemov s pomočjo simulacije nakupovanja izdelkov. Priporočilne sisteme in simulacijo smo združili v knjižnico PyRec. Uspešnost implementiranih modelov smo testirali na podatkovni zbirki MovieLense 1M in zasebnih trgovskih podatkih. Pokažemo, da s predlaganim pristopom izboljšamo porabo zaloge klasičnih sistemov.Classic recommendation systems are very good at predicting user preferences but they do not take into account the fact that some products are limited by their inventory. In this thesis, we presented some classical approaches to modeling user preferences using matrix factorization. We presented a dynamic and a static recommendation system that optimizes inventory consumption. We also proposed a hybrid model that can combine the predictions of a recommendation system which models preference, and a recommendation system that optimizes product consumption. In addition to the model, we also presented a way to test the performance of systems using a product-shopping simulation. The recommended systems and simulation have been combined into the library PyRec. The performance of the implemented models was tested on the MovieLense 1M dataset and private commercial data. We show that with the proposed approach we can improve the stock consumption of classical systems

Assessment and evaluation of force–velocity variables in flywheel squats

Research into flywheel (FW) resistance training and force–velocity–power (F–v–P) profiling has recently gained attention. Ground reaction force (GRF) and velocity (v) during FW squats can be predicted from shaft rotational data. Our study aimed to compare the inter-set reliability of GRF, v, and F–v–P relationship output variables calculated from force plates and linear encoder (presumed gold-standard) and rotary encoder data. Fifty participants performed two sets of FW squats at four inertias. Peak and mean concentric and eccentric GRF, v, and F–v–P outcomes from mean variables during the concentric phase of the squat were calculated. Good to excellent reliability was found for GRF and v (ICC > 0.85), regardless of the measure and the variable type. The F–v–P outcomes showed moderate to good reliability (ICC > 0.74). Inter-measure bias (p < 0.05) was found in the majority of GRF and v variables, as well as for all the calculated F–v–P outcomes (trivial to large TEs) with very large to perfect correlations for v (r 0.797–0.948), GRF (r 0.712–0.959), and, finally, F–v–P outcomes (ICC 0.737–0.943). Rotary encoder overestimated the force plates and linear encoder variables, and the differences were dependent on the level of inertia. Despite high reliability, FW device users should be aware of the discrepancy between the measures