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نتیجه جستجو - Model interpretation

تعداد مقالات یافته شده: 3
ردیف عنوان نوع
1 Using traits to build and explain an ecosystem model: Ecopath with Ecosim modelling of the North Aegean Sea (Eastern Mediterranean)
استفاده از صفات برای ساخت و توضیح یک مدل اکوسیستم: Ecopath با مدل سازی Ecosim از شمال به دریای اژه (مدیترانه شرقی)-2020
The mass-balance-trophic-model Ecopath and its extension Ecopath with Ecosim is very popular for modelling marine ecosystems. In this work, a model of the marine ecosystem of the North Aegean Sea (eastern Mediter- ranean) was created, incorporating the use of biological traits in both building and explaining the model. For the former, data regarding a total of 19 biological traits concerning the biology, distribution, ecology and behavior were used for the definition of fish functional groups. Regarding model interpretation, the results were not only analyzed in the functional groups level but also regarding the composition of the resulting biological traits. In total, 41 functional groups were created for this model. Landings data and discards estimations of the year 1993 were used for the Ecopath component. Six different fisheries management scenaria, one retaining a business-as-usual (maintenance of the status quo) approach and the others investigating changes in fishing effort for the period 2018–2033 were simulated with Ecosim. The simulation results indicated a reduction of pelagic species as well as of biological characteristics associated with them. Concurrently, an increase in the biomass of deep-livingspecies and of relevant biological traits was observed. In general, no strong differences were documented among the various simulation scenarios with the exception of the species targeted by the individual fisheries whose fishing effort changed in each scenario and the associated traits. Significant findings pertain to the decrease of thermophilic traits like high optimal temperature and summer spawning in all fisheries management scenaria simulated. These could mitigate the opposite trend expected to be favored by climate change and the decrease of characteristics associated with the r-life strategy, possibly resulting in less resilient future fish communities. In addition, the negative trends of the biological traits that are anyway rare (e.g. low trophic level and small lifespan) may impact ecosystem functioning. The modelling approach used on this work could be adopted to model other marine communities (possibly also using traits for other ecosystem components) to provide interesting insights regarding anthropogenic effects on marine ecosystem functioning.
Keywords: Ecosystem modeling | Traits-based approaches | Simulations | Ecosystem approach to fisheries
مقاله انگلیسی
2 Designing Eukaryotic Gene Expression Regulation Using Machine Learning
طراحی تنظیم بیان ژن یوکاریوتی با استفاده از یادگیری ماشین-2019
Controlling the expression of genes is one of the key challenges of synthetic biology. Until recently fine-tuned control has been out of reach, particularly in eukaryotes owing to their complexity of gene regulation. With advances in machine learning (ML) and in particular with increasing dataset sizes, models predicting gene expression levels from regulatory sequences can now be successfully constructed. Such models form the cornerstone of algorithms that allow users to design regulatory regions to achieve a specific gene expression level. In this review we discuss strategies for data collection, data encoding, ML practices, design algorithm choices, and finally model interpretation. Ultimately, these developments will provide synthetic biologists with highly specific genetic building blocks to rationally engineer complex pathways and circuits.
مقاله انگلیسی
3 A deep learning interpretable classifier for diabetic retinopathy disease grading
طبقه بندی تفسیر آمیز عمیق برای درجه بندی بیماری رتینوپاتی دیابتی-2019
In this paper we present a diabetic retinopathy deep learning interpretable classifier. On one hand, it classifies retina images into different levels of severity with good performance. On the other hand, this classifier is able of explaining the classification results by assigning a score for each point in the hidden and input spaces. These scores indicate the pixel contribution to the final classification. To obtain these scores, we propose a new pixel-wise score propagation model that for every neuron, divides the observed output score into two components. With this method, the generated visual maps can be easily interpreted by an ophthalmologist in order to find the underlying statistical regularities that help to the diagnosis of this eye disease.
Keywords: Deep learning | Classification | Explanations | Diabetic retinopathy | Model interpretation
مقاله انگلیسی
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