Support Vector Machine optimization with fractional gradient descent for data classification

Data classification has several problems one of which is a large amount of data that will reduce computing time. SVM is a reliable linear classifier for linear or non-linear data, for large-scale data, there are computational time constraints. The Fractional gradient descent method is an unconstrained optimization algorithm to train classifiers with support vector machines that have convex problems. Compared to the classic integer-order model, a model built with fractional calculus has a significant advantage to accelerate computing time. In this research, it is to conduct investigate the current state of this new optimization method fractional derivatives that can be implemented in the classifier algorithm. The results of the SVM Classifier with fractional gradient descent optimization, it reaches a convergence point of approximately 50 iterations smaller than SVM-SGD. The process of updating or fixing the model is smaller in fractional because the multiplier value is less than 1 or in the form of fractions. The SVM-Fractional SGD algorithm is proven to be an effective method for rainfall forecast decisions

Geeniekspressiooni andmete integreerimine teiste ‘oomika’ andmetega kirjeldamaks endomeetriumi retseptiivsuse bioloogilisi mehhanisme

Väitekirja elektrooniline versioon ei sisalda publikatsiooneMaailma Terviseorganisatsiooni statistika väidab, et umbes 10% püsisuhetes olevatest naistest on ühel või teisel põhjusel viljatud. Naise viljakust mõjutab välja palju erinevaid faktoreid ning setõttu on viljatuse põhjuste leidmine tihti väga keeruline. Viljatust põhjustavateks faktoriteks võivad olla üldine terviseseisund, erinevad haigused, geneetiline taust, väliskeskkonna ja eluviisiga seotud tegurid. Ühe näitena võib tuua embrüo pesastumist (implantatsioon) emaka limaskesta (endomeetriumi), mis võib toimuda ainult kindla lühikese perioodi vältel (implantatsiooni aken), kui endomeetrium on embrüo suhtes kõige vastuvõtlikum. Implantatsiooni akna periood on aga iga naise jaoks erinev, ning on määratud erinevate bioloogiliste protsesside poolt. Kunstliku viljastamise (IVF) läbiviimise jaoks on kriitiline teada täpset implantatsiooni akna aega, sellega seotud mehhanisme ja nende vastastikust mõju. Selleks, et uurida mehhanismide omavahelisi seoseid, panime paariviisiliselt kokku erinevaid geneetilise regulatsiooni andmekihte, milleks olid RNA, mikroRNA ja DNA metülatsiooni admed, ja mida koos nimetatakse ‘oomika’ andmekihtideks. Kokkuvõtvalt näitavad antud töö tulemused, et, võrreldes ühe ‘oomika’ andmekihi uurimisega, ‘oomika’ andmekihtide kombineerimine aitab paremini mõista endomeetriumi retseptiivsusega seotud bioloogilisi protsesse ning vältida valepositiivseid tulemusi. Antud tööga me rõhutame süsteemibioloogia ning paljude andmekihtide samaaegse kasutamise olulisust naise reproduktiivsuse bioloogiliste mehhanismide uurimisel.According to the World Health Organization, over 10% of females in a stable relationship are suffering from involuntary infertility/subfertility worldwide. Untangling the reasons for this is difficult because female reproduction is a sophisticated matter and can be affected by many factors such as health, accompanying diseases, genetic background, environment, and lifestyle. As a specific example, embryo implantation – its attachment to the uterine lining (endometrium) – occurs only during a relatively short period of time, called the window of implantation (WOI), when the endometrium is most receptive to an embryo. This is critical for a commonly used fertility treatment of in vitro fertilizaton (IVF) – and to make matters more complex, the WOI is not the same for everyone, but adjusted by an interlocking system of biological regulation mechanisms. Thus, to provide successful IVF, it is important to know these exact regulation mechanisms – and, since they interact with one another, to understand how they work together, not just individually. We used pairwise integration of data from different layers of genetic regulation, such as RNA, microRNA, and DNA methylation, called together the ‘omics’ layers, and showed the advantage of the data integration approach over the usage of just a single ‘omics’ layer. As a result, we obtained the lists of novel potential biomarkers that could regulate WOI, validated some previously known receptivity biomarkers, and showed that integration of different ‘omics’ layers helps to avoid false-positive results. With our work, we encourage other researchers in the female reproduction field to integrate several data layers for further studieshttps://www.ester.ee/record=b535138

The Shared Genetic Architecture of Modifiable Risk for Dementia and its Influence on Brain Health

Targeting modifiable risk factors for dementia may prevent or delay dementia. However, the mechanisms by which risk factors influence dementia remain unclear and current research often ignores commonality between risk factors. Therefore, my thesis aimed to model the shared genetic architecture of modifiable risk for dementia and explored how these shared pathways may influence dementia and brain health. I used linkage disequilibrium score regression and genomic structural equation modelling (SEM) to create a multivariate model of the shared genetics between Alzheimer’s disease (AD) and its modifiable risk factors. Although AD was genetically distinct, there was widespread genetic overlap between most of its risk factors. This genetic overlap formed an overarching Common Factor of general modifiable dementia risk, in addition to 3 subclusters of distinct sets of risk factors. Next, I performed two multivariate genome-wide association studies (GWASs) to identify the risk variants that underpinned the Common Factor and the 3 subclusters of risk factors. Together, these uncovered 590 genome-wide significant loci for the four latent factors, 34 of which were novel findings. Using post-GWAS analyses I found evidence that the shared genetics between risk factors influence a range of neuronal functions, which were highly expressed in brain regions that degenerate in dementia. Pathway analysis indicated that shared genetics between risk factors may impact dementia pathogenesis directly at specific loci. Finally, I used Mendelian randomisation to test whether the shared genetic pathways between modifiable dementia risk factors were causal for AD. I found evidence of a causal effect of the Common Factor on AD risk. Taken together, my thesis provides new insights into how modifiable risk factors for dementia interrelate on a genetic level. Although the shared genetics between modifiable risk factors for dementia seem to be distinct from dementia pathways on a genome-wide level, I provide evidence that they influence general brain health, and so they may increase dementia risk indirectly by altering cognitive reserve. However, I also found that shared genetics risk between risk factors in certain genomic regions may directly influence dementia pathogenesis, which should be explored in future work to determine whether these regions represent targets to prevent dementia

Grand Celebration: 10th Anniversary of the Human Genome Project

In 1990, scientists began working together on one of the largest biological research projects ever proposed. The project proposed to sequence the three billion nucleotides in the human genome. The Human Genome Project took 13 years and was completed in April 2003, at a cost of approximately three billion dollars. It was a major scientific achievement that forever changed the understanding of our own nature. The sequencing of the human genome was in many ways a triumph for technology as much as it was for science. From the Human Genome Project, powerful technologies have been developed (e.g., microarrays and next generation sequencing) and new branches of science have emerged (e.g., functional genomics and pharmacogenomics), paving new ways for advancing genomic research and medical applications of genomics in the 21st century. The investigations have provided new tests and drug targets, as well as insights into the basis of human development and diagnosis/treatment of cancer and several mysterious humans diseases. This genomic revolution is prompting a new era in medicine, which brings both challenges and opportunities. Parallel to the promising advances over the last decade, the study of the human genome has also revealed how complicated human biology is, and how much remains to be understood. The legacy of the understanding of our genome has just begun. To celebrate the 10th anniversary of the essential completion of the Human Genome Project, in April 2013 Genes launched this Special Issue, which highlights the recent scientific breakthroughs in human genomics, with a collection of papers written by authors who are leading experts in the field