scholarly journals Vegetation cover analysis of the mountainous part of north-eastern Siberia by means of geoinformation modelling and machine learning (basic principles, approaches, technology and relation to geosystem science)

2021 ◽  
Vol 38 ◽  
pp. 00142
Author(s):  
Moisey Zakharov ◽  
Mikhail Cherosov ◽  
Elena Troeva ◽  
Sebastien Gadal
Keyword(s):  
Machine Learning ◽  
Vegetation Cover ◽  
Slope Aspect ◽  
Learning Approaches ◽  
Eastern Siberia ◽  
Basic Principles ◽  
Vegetation Map ◽  
Mountain Landscape ◽  
North Eastern ◽  
Vegetative Indices

For the first time, the geoinformation modelling and machine learning approaches have been used to study the vegetation cover of the mountainous part of North-Eastern Siberia – the Orulgan medium-altitude mountain landscape province. These technologies allowed us to distinguish a number of mapping units that were used for creation and analysis of 1:100 000 scale vegetation map of the interpreted key area. Based on the studies, we decided upon the basic principles, approaches and technologies that would serve as a methodology basis for the further studies of vegetation cover of the large region. Relief, slope aspect, genetic types of sediments, and moisture conditions were selected as supplementary factors to the vegetative indices for differentiation of both plant communities and vegetation map units.

scholarly journals Machine Learning Approaches for the Prioritization of Genomic Variants Impacting Pre-mRNA Splicing

2019 ◽  
Author(s):  
Charles F Rowlands ◽  
Diana Baralle ◽  
Jamie M Ellingford
Keyword(s):  
Machine Learning ◽  
In Silico Analysis ◽  
Mrna Splicing ◽  
Comparative Approach ◽  
Mendelian Disease ◽  
Learning Approaches ◽  
Basic Principles ◽  
Prediction Tools ◽  
Splicing Defects ◽  
Made In

Defects in pre-mRNA splicing are frequently a cause of Mendelian disease. Despite the advent of next-generation sequencing, allowing a deeper insight into a patient’s variant landscape, the ability to characterize variants causing splicing defects has not progressed with the same speed. To address this, recent years have seen a sharp spike in the number of splice prediction tools leveraging machine learning approaches, leaving clinical geneticists with a plethora of choices for in silico analysis. In this Review, some basic principles of machine learning are introduced in the context of genomics and splicing analysis. A critical comparative approach is then used to describe seven recent machine learning-based splice prediction tools, revealing highly diverse approaches and common caveats. We find that, although great progress has been made in producing specific and sensitive tools, there is still much scope for personalized approaches to prediction of variant impact on splicing. Such approaches may increase diagnostic yields and underpin improvements to patient care.

Machine learning approaches for elucidating the biological effects of natural products

2020 ◽  
Author(s):  
Ruihan Zhang ◽  
Xiaoli Li ◽  
Xingjie Zhang ◽  
Huayan Qin ◽  
Weilie Xiao
Keyword(s):  
Machine Learning ◽  
Natural Products ◽  
Biological Effects ◽  
Learning Approaches ◽  
Basic Principles

This review presents the basic principles, protocols and examples of using the machine learning approaches to investigate the bioactivity of natural products.

scholarly journals The latitudinal distribution of vegetation cover in Siberia

2019 ◽  
Vol 16 ◽  
pp. 00047
Author(s):  
Irina Safronova ◽  
Tatiana Yurkovsksya
Keyword(s):  
Vegetation Cover ◽  
Western Siberia ◽  
Steppe Zone ◽  
Coniferous Forests ◽  
Forest Steppe ◽  
Taiga Zone ◽  
Eastern Siberia ◽  
Central Siberia ◽  
Zonal Vegetation ◽  
North Eastern

The latitudinal changes of vegetation cover on the plains of Siberia are observed. In Western Siberia there are 4 zones (tundra and taiga, and forest-steppe and steppe only here), in Central and North-Eastern Siberia − only 2 zones (tundra and taiga).Tundra zone is represented by 4 subzones in Central Siberia; in Western and North-Eastern Siberia − by 3 subzones (there are no polar subzone). All 5 subzones of the taiga zone are distinguished both in Western Siberia and in the Central Siberia, but in the Central Siberia, forests are found in very high latitudes. The feature of the taiga zone of Western Siberia is high paludification. As a result, the vegetation of mires dominates over the zonal vegetation. Zonal West Siberian types are dark coniferous forests. Light coniferous forests predominate in the taiga zone of Central and North-Eastern Siberia. In the forest-steppe zone in Western Siberia forests are small-leaved − birch, aspen-birch (Betula pendula, Populus tremula). The abundance of mires is the feature of this zone, as well as in the taiga.

scholarly journals Machine Learning Approaches for the Prioritization of Genomic Variants Impacting Pre-mRNA Splicing

Cells ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1513 ◽  
Author(s):  
Charlie F Rowlands ◽  
Diana Baralle ◽  
Jamie M Ellingford
Keyword(s):  
Machine Learning ◽  
In Silico Analysis ◽  
Mrna Splicing ◽  
Comparative Approach ◽  
Mendelian Disease ◽  
Learning Approaches ◽  
Basic Principles ◽  
Prediction Tools ◽  
Splicing Defects ◽  
Made In

Defects in pre-mRNA splicing are frequently a cause of Mendelian disease. Despite the advent of next-generation sequencing, allowing a deeper insight into a patient’s variant landscape, the ability to characterize variants causing splicing defects has not progressed with the same speed. To address this, recent years have seen a sharp spike in the number of splice prediction tools leveraging machine learning approaches, leaving clinical geneticists with a plethora of choices for in silico analysis. In this review, some basic principles of machine learning are introduced in the context of genomics and splicing analysis. A critical comparative approach is then used to describe seven recent machine learning-based splice prediction tools, revealing highly diverse approaches and common caveats. We find that, although great progress has been made in producing specific and sensitive tools, there is still much scope for personalized approaches to prediction of variant impact on splicing. Such approaches may increase diagnostic yields and underpin improvements to patient care.

Geobotanical mapping ot the vegetation cover in the basins of the North-Eastern Cisbaikalia

2015 ◽  
pp. 62-77 ◽  
Author(s):  
A. P. Sofronov
Keyword(s):  
Vegetation Cover ◽  
Anthropogenic Influences ◽  
Structural Dynamic ◽  
Vegetation Map ◽  
Gis Technologies ◽  
The North ◽  
Multi Stage ◽  
North Eastern ◽  
Archive Data ◽  
Coenotic Diversity

This paper presents the results of study of the vegetation cover in the North Baikal and the Upper Angara basins. The vegetation mapping was carried out in a scale 1 : 200 000 using the field and archive data as well as GIS-technologies. The structure of the map legend was based on the principles of multi-stage vegetation classification developed by V.B. Sochava. The vegetation map shows the basic structural-coenotic diversity of the vegetation cover of the study area. Due to the high disturbance of forest vegetation special attention was paid to structural-dynamic analysis to identify potential plant communities. The rows of transformation were estimated as well. The map provides a possibility to make a prognosis for further vegetation successions under the natural and anthropogenic influences.

scholarly journals Landslide Susceptibility Zonation Using Statistical and Machine Learning Approaches in Northern Lecco, Italy

2021 ◽  
Author(s):  
Mohammad Mehrabi
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Learning Approaches ◽  
Visual Interpretation ◽  
Topographic Wetness Index ◽  
Inference System ◽  
Conditioning Factors ◽  
Susceptibility Maps

Abstract This study deals with landslide susceptibility mapping in the northern part of Lecco Province, Lombardy Region, Italy. In so doing, a valid landslide inventory map and thirteen conditioning factors (including elevation, slope aspect, slope degree, plan curvature, profile curvature, distance to waterway, distance to road, distance to fault, soil type, land use, lithology, stream power index, and topographic wetness index) form the spatial database within geographic information system (GIS). The used evaluative models comprise a bivariate statistical approach called frequency ratio (FR) and two machine learning tools, namely multi-layer perceptron neural network (MLPNN) and adaptive neuro-fuzzy inference system (ANFIS). These models first use landslide and non-landslide records for comprehending the relationship between the landslide occurrence and conditioning factors. Then landslide susceptibility values are predicted for the whole area. The accuracy of the produced susceptibility maps is measured using area under the curve (AUC) index, according to which, the MLPNN (AUC = 0.916) presented the most accurate map, followed by the FR (AUC = 0. 898) and ANFIS (AUC = 0.889). Visual interpretation of the susceptibility maps, FR-based correlation analysis, as well as the importance assessment of conditioning factors, all indicated the significant contribution of the road networks to the crucial susceptibility of landslide. Lastly, an explicit predictive formula is extracted from the implemented MLPNN model for a convenient approximation of landslide susceptibility value.

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