| 1. |
- Robinson, Jonathan, 1986, et al.
(författare)
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An atlas of human metabolism
- 2020
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Ingår i: Science Signaling. - : American Association for the Advancement of Science (AAAS). - 1945-0877 .- 1937-9145. ; 13:624
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Tidskriftsartikel (refereegranskat)abstract
- Genome-scale metabolic models (GEMs) are valuable tools to study metabolism and provide a scaffold for the integrative analysis of omics data. Researchers have developed increasingly comprehensive human GEMs, but the disconnect among different model sources and versions impedes further progress. We therefore integrated and extensively curated the most recent human metabolic models to construct a consensus GEM, Human1. We demonstrated the versatility of Human1 through the generation and analysis of cell- and tissue-specific models using transcriptomic, proteomic, and kinetic data. We also present an accompanying web portal, Metabolic Atlas (https://www.metabolicatlas.org/), which facilitates further exploration and visualization of Human1 content. Human1 was created using a version-controlled, open-source model development framework to enable community-driven curation and refinement. This framework allows Human1 to be an evolving shared resource for future studies of human health and disease.
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| 2. |
- Strannegård, Claes, 1962, et al.
(författare)
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Ecosystem Models Based on Artificial Intelligence
- 2022
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Ingår i: 34th Workshop of the Swedish Artificial Intelligence Society, SAIS 2022. - : IEEE.
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Konferensbidrag (refereegranskat)abstract
- Ecosystem models can be used for understanding general phenomena of evolution, ecology, and ethology. They can also be used for analyzing and predicting the ecological consequences of human activities on specific ecosystems, e.g., the effects of agriculture, forestry, construction, hunting, and fishing. We argue that powerful ecosystem models need to include reasonable models of the physical environment and of animal behavior. We also argue that several well-known ecosystem models are unsatisfactory in this regard. Then we present the open-source ecosystem simulator Ecotwin, which is built on top of the game engine Unity. To model a specific ecosystem in Ecotwin, we first generate a 3D Unity model of the physical environment, based on topographic or bathymetric data. Then we insert digital 3D models of the organisms of interest into the environment model. Each organism is equipped with a genome and capable of sexual or asexual reproduction. An organism dies if it runs out of some vital resource or reaches its maximum age. The animal models are equipped with behavioral models that include sensors, actions, reward signals, and mechanisms of learning and decision-making. Finally, we illustrate how Ecotwin works by building and running one terrestrial and one marine ecosystem model.
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| 3. |
- Daoud, Adel, 1981, et al.
(författare)
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Using Satellite Images and Deep Learning to Measure Health and Living Standards in India
- 2023
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Ingår i: Social Indicators Research. - : SPRINGER. - 0303-8300 .- 1573-0921. ; 167:1-3, s. 475-505
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Tidskriftsartikel (refereegranskat)abstract
- Using deep learning with satellite images enhances our understanding of human development at a granular spatial and temporal level. Most studies have focused on Africa and on a narrow set of asset-based indicators. This article leverages georeferenced village-level census data from across 40% of the population of India to train deep models that predicts 16 indicators of human well-being from Landsat 7 imagery. Based on the principles of transfer learning, the census-based model is used as a feature extractor to train another model that predicts an even larger set of developmental variables—over 90 variables—included in two rounds of the National Family Health Survey (NFHS). The census-based-feature-extractor model outperforms the current standard in the literature for most of these NFHS variables. Overall, the results show that combining satellite data with Indian Census data unlocks rich information for training deep models that track human development at an unprecedented geographical and temporal resolution.
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| 4. |
- Zanne, Amy E, et al.
(författare)
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Fungal functional ecology: bringing a trait-based approach to plant-associated fungi.
- 2020
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Ingår i: Biological reviews of the Cambridge Philosophical Society. - : Wiley. - 1469-185X .- 1464-7931. ; 95:2, s. 409-433
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Tidskriftsartikel (refereegranskat)abstract
- Fungi play many essential roles in ecosystems. They facilitate plant access to nutrients and water, serve as decay agents that cycle carbon and nutrients through the soil, water and atmosphere, and are major regulators of macro-organismal populations. Although technological advances are improving the detection and identification of fungi, there still exist key gaps in our ecological knowledge of this kingdom, especially related to function. Trait-based approaches have been instrumental in strengthening our understanding of plant functional ecology and, as such, provide excellent models for deepening our understanding of fungal functional ecology in ways that complement insights gained from traditional and -omics-based techniques. In this review, we synthesize current knowledge of fungal functional ecology, taxonomy and systematics and introduce a novel database of fungal functional traits (FunFun ). FunFun is built to interface with other databases to explore and predict how fungal functional diversity varies by taxonomy, guild, and other evolutionary or ecological grouping variables. To highlight how a quantitative trait-based approach can provide new insights, we describe multiple targeted examples and end by suggesting next steps in the rapidly growing field of fungal functional ecology.
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| 5. |
- Gerken, Jan, 1991, et al.
(författare)
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Equivariance versus augmentation for spherical images
- 2022
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Ingår i: Proceedings of Machine Learning Resaerch. ; , s. 7404-7421
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Konferensbidrag (refereegranskat)abstract
- We analyze the role of rotational equivariance in convolutional neural networks (CNNs) applied to spherical images. We compare the performance of the group equivariant networks known as S2CNNs and standard non-equivariant CNNs trained with an increasing amount of data augmentation. The chosen architectures can be considered baseline references for the respective design paradigms. Our models are trained and evaluated on single or multiple items from the MNIST- or FashionMNIST dataset projected onto the sphere. For the task of image classification, which is inherently rotationally invariant, we find that by considerably increasing the amount of data augmentation and the size of the networks, it is possible for the standard CNNs to reach at least the same performance as the equivariant network. In contrast, for the inherently equivariant task of semantic segmentation, the non-equivariant networks are consistently outperformed by the equivariant networks with significantly fewer parameters. We also analyze and compare the inference latency and training times of the different networks, enabling detailed tradeoff considerations between equivariant architectures and data augmentation for practical problems.
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| 6. |
- Uhen, Mark D., et al.
(författare)
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The EarthLife Consortium API: an extensible, open-source service foraccessing fossil data and taxonomies from multiple communitypaleodata resources
- 2021
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Ingår i: Frontiers of Biogeography. - : International Biogeography Society. ; 13:2
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Tidskriftsartikel (refereegranskat)abstract
- Paleobiologists and paleoecologists interested in studying biodiversity dynamics over broadspatial and temporal scales have built multiple community-curated data resources, eachemphasizing a particular spatial domain, timescale, or taxonomic group(s). This multiplicity ofdata resources is understandable, given the enormous diversity of life across Earth's history,but creates a barrier to achieving a truly global understanding of the diversity and distributionof life across time. Here we present the Earth Life Consortium Application ProgrammingInterface (ELC API), a lightweight data service designed to search and retrieve fossil occurrenceand taxonomic information from across multiple paleobiological resources. Key endpointsinclude Occurrences (returns spatiotemporal locations of fossils for selected taxa), Locales(returns information about sites with fossil data), References (returns bibliographicinformation), and Taxonomy (returns names of subtaxa associated with selected taxa). Dataobjects are returned as JSON or CSV format. The ELC API supports tectonic-driven shifts ingeographic position back to 580 Ma using services from Macrostrat and GPlates. The ELC APIhas been implemented first for the Paleobiology Database and Neotoma PaleoecologyDatabase, with a test extension to the Strategic Environmental Archaeology Database. The ELCAPI is designed to be readily extensible to other paleobiological data resources, with allendpoints fully documented and following open-source standards (e.g., Swagger, OGC). Thebroader goal is to help build an interlinked and federated ecosystem of paleobiological andpaleoenvironmental data resources, which together provide paleobiologists, macroecologists,biogeographers, and other interested scientists with full coverage of the diversity anddistribution of life across time.
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| 7. |
- Younes, Sara
(författare)
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Uncovering biomarkers and molecular heterogeneity of complex diseases : Utilizing the power of Data Science
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Uncovering causal drivers of complex diseases is yet a difficult challenge. Unlike single-gene disorders complex diseases are heterogeneous and are caused by a combination of genetic, environmental, and lifestyle factors which complicates the identification of patient subgroups and the disease causal drivers. In order to study the dimensions of complex diseases analyzing different omics data is a necessity.The main goal of this thesis is to provide computational approaches for analyzing omics data of two complex diseases; mainly, Acute Myeloid Leukaemia (AML) and Systemic Lupus Erythematosus (SLE). Additionally, we aim at providing a method that would deal with integration issues that usually arise when combining complex diseases omics (specifically metabolomics) data from multiple data sources. AML is a cancer of the myeloid blood cells that is known for its heterogeneity. Patients usually respond to treatment and achieve a complete remission state. However, a majority of patients relapse or develop treatment resistance. In paper I, we focus on investigating recurrent genomic alterations in adult and pediatric relapsed and primary resistant AML that may explain disease progression. In paper II, we characterize changes in the transcriptome of AML over the course of the disease, incorporating machine learning analysis.SLE is a heterogeneous autoimmune disease characterized by unpredictable periods of flares. The flares are presented as different SLE disease activities (DA). Studies on the combinatorial effects of genes towards the manifestation of SLE DAs in patients’ subgroups have been limited. In paper III, we analyze gene expression data of pediatric SLE using interpretable machine learning. The aim was to study the co-predictive transcriptomic factors driving disease progression, discover the disease subtypes, and explore the relationship between transcriptomics factors and the phenotypes associated with the discovered subtypes.Recently, Metabolomics has been a crucial dimension in major multi-omics complex disease studies. Small-compound databases contain a large amount of information for metabolites. However, the existing redundancy of information in the databases leads to major standardization issues. In paper IV, we aim at resolving the inconsistencies that exist when linking and combining metabolomics data from several databases by introducing the new R package MetaFetcheR.
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| 8. |
- Munappy, Aiswarya Raj, 1990, et al.
(författare)
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On the Trade-off Between Robustness and Complexity in Data Pipelines
- 2021
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Ingår i: Quality of Information and Communications Technology. - Cham : Springer. - 9783030853464 - 9783030853471 ; 1439 CCIS, s. 401-415
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Konferensbidrag (refereegranskat)abstract
- Data pipelines play an important role throughout the data management process whether these are used for data analytics or machine learning. Data-driven organizations can make use of data pipelines for producing good quality data applications. Moreover, data pipelines ensure end-to-end velocity by automating the processes involved in extracting, transforming, combining, validating, and loading data for further analysis and visualization. However, the robustness of data pipelines is equally important since unhealthy data pipelines can add more noise to the input data. This paper identifies the essential elements for a robust data pipeline and analyses the trade-off between data pipeline robustness and complexity.
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| 9. |
- de Dios, Eddie, et al.
(författare)
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Introduction to Deep Learning in Clinical Neuroscience
- 2022
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Ingår i: Acta Neurochirurgica, Supplement. - Cham : Springer International Publishing. - 2197-8395 .- 0065-1419. ; 134, s. 79-89
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- The use of deep learning (DL) is rapidly increasing in clinical neuroscience. The term denotes models with multiple sequential layers of learning algorithms, architecturally similar to neural networks of the brain. We provide examples of DL in analyzing MRI data and discuss potential applications and methodological caveats. Important aspects are data pre-processing, volumetric segmentation, and specific task-performing DL methods, such as CNNs and AEs. Additionally, GAN-expansion and domain mapping are useful DL techniques for generating artificial data and combining several smaller datasets. We present results of DL-based segmentation and accuracy in predicting glioma subtypes based on MRI features. Dice scores range from 0.77 to 0.89. In mixed glioma cohorts, IDH mutation can be predicted with a sensitivity of 0.98 and specificity of 0.97. Results in test cohorts have shown improvements of 5–7% in accuracy, following GAN-expansion of data and domain mapping of smaller datasets. The provided DL examples are promising, although not yet in clinical practice. DL has demonstrated usefulness in data augmentation and for overcoming data variability. DL methods should be further studied, developed, and validated for broader clinical use. Ultimately, DL models can serve as effective decision support systems, and are especially well-suited for time-consuming, detail-focused, and data-ample tasks.
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| 10. |
- Lidstrom, D, et al.
(författare)
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Agent based match racing simulations : Starting practice
- 2022
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Ingår i: SNAME 24th Chesapeake Sailing Yacht Symposium, CSYS 2022. - : Society of Naval Architects and Marine Engineers.
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Konferensbidrag (refereegranskat)abstract
- Match racing starts in sailing are strategically complex and of great importance for the outcome of a race. With the return of the America's Cup to upwind starts and the World Match Racing Tour attracting young and development sailors, the tactical skills necessary to master the starts could be trained and learned by means of computer simulations to assess a large range of approaches to the starting box. This project used game theory to model the start of a match race, intending to develop and study strategies using Monte-Carlo tree search to estimate the utility of a player's potential moves throughout a race. Strategies that utilised the utility estimated in different ways were defined and tested against each other through means of simulation and with an expert advice on match racing start strategy from a sailor's perspective. The results show that the strategies that put greater emphasis on what the opponent might do, perform better than those that did not. It is concluded that Monte-Carlo tree search can provide a basis for decision making in match races and that it has potential for further use.
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