| 51. |
- Rivas-Carrillo, Salvador Daniel, et al.
(författare)
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MindReader : unsupervised electroencephalographic reader
- 2023
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Background: Electroencephalogram (EEG) interpretation plays a critical role in the clinical assessment of neurological conditions, including epilepsy. Manual analysis requires highly specialized and heavily trained personnel. Moreover, the rate of capturing abnormal events makes interpretation time-consuming, resource-hungry, and, overall, an expensive process.Automatic detection offers the potential to improve the quality of patient care by shortening the time to diagnosis, managing big data, and optimizing the allocation of human resources.Findings: We present MindReader, an unsupervised method for EEG signals. First, MindReader processes the signal through an autoencoder in order to detect EEG abnormalities. Next, patterns are hypothesized by a Hidden Markov Model. Our algorithm automatically generates labels for non-pathological phases, thus reducing the search space for trained personnel.Conclusions: MindReader is effective in detecting EEG abnormalities in focal and generalized epilepsy.
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| 52. |
- Rivas-Carrillo, Salvador Daniel, et al.
(författare)
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MindReader : Unsupervised Classification of Electroencephalographic Data
- 2023
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Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 23:6, s. 2971-
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Tidskriftsartikel (refereegranskat)abstract
- Electroencephalogram (EEG) interpretation plays a critical role in the clinical assessment of neurological conditions, most notably epilepsy. However, EEG recordings are typically analyzed manually by highly specialized and heavily trained personnel. Moreover, the low rate of capturing abnormal events during the procedure makes interpretation time-consuming, resource-hungry, and overall an expensive process. Automatic detection offers the potential to improve the quality of patient care by shortening the time to diagnosis, managing big data and optimizing the allocation of human resources towards precision medicine. Here, we present MindReader, a novel unsupervised machine-learning method comprised of the interplay between an autoencoder network, a hidden Markov model (HMM), and a generative component: after dividing the signal into overlapping frames and performing a fast Fourier transform, MindReader trains an autoencoder neural network for dimensionality reduction and compact representation of different frequency patterns for each frame. Next, we processed the temporal patterns using a HMM, while a third and generative component hypothesized and characterized the different phases that were then fed back to the HMM. MindReader then automatically generates labels that the physician can interpret as pathological and non-pathological phases, thus effectively reducing the search space for trained personnel. We evaluated MindReader's predictive performance on 686 recordings, encompassing more than 980 h from the publicly available Physionet database. Compared to manual annotations, MindReader identified 197 of 198 epileptic events (99.45%), and is, as such, a highly sensitive method, which is a prerequisite for clinical use.
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| 53. |
- Rivas-Carrillo, Salvador Daniel
(författare)
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The revolutionary partnership of computation and biology
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The organization of living beings is complex. Science uses modeling in order to gain a deeper understanding, and to be able to manipulate the processes of living organisms. To this purpose, I used and developed computational tools to investigate and model different relevant biological phenomena. In paper I, I utilized whole-genome data from wild and domesticated European rabbit (Oryctolagus cuniculus sp.) populations to identify segregating insertions of endogenous retroviruses and compare their variation along the host phylogeny and domestication history. The results from this study highlight the importance of genomic modeling beyond reference organisms and reference individuals, and provide deep insights regarding strategies for variant analyses in host population comparative genomics. In paper IV, I studied the process of exaptation of foreign genetic elements at broad-scale by observing the presence and characteristics of retroviral env gene, syncytin, across vertebrates. I searched a library of more than 150 chromosome-length assemblies covering 17 taxonomical orders for syncytin homologs, where I identified and syntenically aligned over 300 loci insertions, including not previously known insertions. Additionally, three-dimensional structures of the recovered sequences were predicted using AlphaFold2. Phylogenomics analyses suggest a complex dynamic of multiple retroviral insertions at different time points with sequence conservation specific to clades that share a similar histo-physiological placental type.In paper II, I expanded the scope to encompass translational medicine by developing an unsupervised machine learning methodology for detecting anomalies in biomedical signals, MindReader, which I applied primarily to electroencephalogram. In paper III, I developed a hidden Markov model implementation that includes a hypothesis generator for stream time-domain signals, which is used as a dependency for paper II. The work in this thesis substantiates that a combination of biological knowledge, cutting-edge technology, and robust algorithmic design constitute the primordial factors for scientific advancement.
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| 54. |
- Schneider, Andreas N., et al.
(författare)
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Comparative fungal community analyses using metatranscriptomics and internal transcribed spacer amplicon sequencing from Norway spruce
- 2021
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Ingår i: mSystems. - : American Society for Microbiology. - 2379-5077. ; 6:1
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Tidskriftsartikel (refereegranskat)abstract
- The health, growth, and fitness of boreal forest trees are impacted and improved by their associated microbiomes. Microbial gene expression and functional activity can be assayed with RNA sequencing (RNA-Seq) data from host samples. In contrast, phylogenetic marker gene amplicon sequencing data are used to assess taxonomic composition and community structure of the microbiome. Few studies have considered how much of this structural and taxonomic information is included in transcriptomic data from matched samples. Here, we described fungal communities using both host-derived RNA-Seq and fungal ITS1 DNA amplicon sequencing to compare the outcomes between the methods. We used a panel of root and needle samples from the coniferous tree species Picea abies (Norway spruce) growing in untreated (nutrient-deficient) and nutrient-enriched plots at the Flakaliden forest research site in boreal northern Sweden. We show that the relationship between samples and alpha and beta diversity indicated by the fungal transcriptome is in agreement with that generated by the ITS data, while also identifying a lack of taxonomic overlap due to limitations imposed by current database coverage. Furthermore, we demonstrate how metatranscriptomics data additionally provide biologically informative functional insights. At the community level, there were changes in starch and sucrose metabolism, biosynthesis of amino acids, and pentose and glucuronate interconversions, while processing of organic macromolecules, including aromatic and heterocyclic compounds, was enriched in transcripts assigned to the genus Cortinarius. IMPORTANCE A deeper understanding of microbial communities associated with plants is revealing their importance for plant health and productivity. RNA extracted from plant field samples represents the host and other organisms present. Typically, gene expression studies focus on the plant component or, in a limited number of studies, expression in one or more associated organisms. However, metatranscriptomic data are rarely used for taxonomic profiling, which is currently performed using amplicon approaches. We created an assembly-based, reproducible, and hardware-agnostic workflow to taxonomically and functionally annotate fungal RNA-Seq data obtained from Norway spruce roots, which we compared to matching ITS amplicon sequencing data. While we identified some limitations and caveats, we show that functional, taxonomic, and compositional insights can all be obtained from RNA-Seq data. These findings highlight the potential of metatranscriptomics to advance our understanding of interaction, response, and effect between host plants and their associated microbial communities.
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| 55. |
- Schneider, Andreas N., et al.
(författare)
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Comparative Fungal Community Analyses Using Metatranscriptomics and Internal Transcribed Spacer Amplicon Sequencing from Norway Spruce
- 2021
-
Ingår i: mSystems. - : American Society for Microbiology. - 2379-5077. ; 6:1
-
Tidskriftsartikel (refereegranskat)abstract
- The health, growth, and fitness of boreal forest trees are impacted and improved by their associated microbiomes. Microbial gene expression and functional activity can be assayed with RNA sequencing (RNA-Seq) data from host samples. In contrast, phylogenetic marker gene amplicon sequencing data are used to assess taxonomic composition and community structure of the microbiome. Few studies have considered how much of this structural and taxonomic information is included in transcriptomic data from matched samples. Here, we described fungal communities using both host-derived RNA-Seq and fungal ITS1 DNA amplicon sequencing to compare the outcomes between the methods. We used a panel of root and needle samples from the coniferous tree species Picea abies (Norway spruce) growing in untreated (nutrient-deficient) and nutrient-enriched plots at the Flakaliden forest research site in boreal northern Sweden. We show that the relationship between samples and alpha and beta diversity indicated by the fungal transcriptome is in agreement with that generated by the ITS data, while also identifying a lack of taxonomic overlap due to limitations imposed by current database coverage. Furthermore, we demonstrate how metatranscriptomics data additionally provide biologically informative functional insights. At the community level, there were changes in starch and sucrose metabolism, biosynthesis of amino acids, and pentose and glucuronate interconversions, while processing of organic macromolecules, including aromatic and heterocyclic compounds, was enriched in transcripts assigned to the genus Cortinarius.IMPORTANCE A deeper understanding of microbial communities associated with plants is revealing their importance for plant health and productivity. RNA extracted from plant field samples represents the host and other organisms present. Typically, gene expression studies focus on the plant component or, in a limited number of studies, expression in one or more associated organisms. However, metatranscriptomic data are rarely used for taxonomic profiling, which is currently performed using amplicon approaches. We created an assembly-based, reproducible, and hardware-agnostic workflow to taxonomically and functionally annotate fungal RNA-Seq data obtained from Norway spruce roots, which we compared to matching ITS amplicon sequencing data. While we identified some limitations and caveats, we show that functional, taxonomic, and compositional insights can all be obtained from RNA-Seq data. These findings highlight the potential of metatranscriptomics to advance our understanding of interaction, response, and effect between host plants and their associated microbial communities.
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| 56. |
- Shirak, Andrey, et al.
(författare)
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Identification of Repetitive Elements in the Genome of Oreochromis niloticus : Tilapia Repeat Masker
- 2009
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Ingår i: Marine Biotechnology. - : Springer Science and Business Media LLC. - 1436-2228 .- 1436-2236. ; 12:2, s. 121-125
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Tidskriftsartikel (refereegranskat)abstract
- The large-scale bacterial artificial chromosome-end sequencing project of Nile tilapia (Oreochromis niloticus) has generated extensive sequence data that allowed the examination of the repeat content in this fish genome and building of a repeat library specific for this species. This library was established based on Tilapiini repeat sequences from GenBank, sequences orthologous to the repeat library of zebrafish in Repbase, and novel repeats detected by genome analysis using MIRA assembler. We estimate that repeats constitute about 14% of the tilapia genome and also give estimates for the occurrence of the different repeats based on the Basic Local Alignment Search Tool searches within the database of known tilapia sequences. The frequent occurrence of novel repeats in the tilapia genome indicates the importance of using the species-specific repeat masker prior to sequence analyses. A web tool based on the RepeatMasker software was designed to assist tilapia genomics.
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| 57. |
- Stathis, Dimitrios, 1989-, et al.
(författare)
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Approximate Computing Applied to Bacterial Genome Identification using Self-Organizing Maps
- 2019
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Ingår i: 2019 IEEE Computer Society Annual Symposium On VLSI (ISVLSI 2019). - : IEEE. - 9781728133911 ; , s. 562-569
-
Konferensbidrag (refereegranskat)abstract
- In this paper we explore the design space of a self-organizing map (SOM) used for rapid and accurate identification of bacterial genomes. This is an important health care problem because even in Europe, 70% of prescriptions for antibiotics is wrong. The SOM is trained on Next Generation Sequencing (NGS) data and is able to identify the exact strain of bacteria. This is in contrast to conventional methods that require genome assembly to identify the bacterial strain. SOM has been implemented as an synchoros VLSI design and shown to have 3-4 orders better computational efficiency compared to GPUs. To further lower the energy consumption, we exploit the robustness of SOM by successively lowering the resolution to gain further improvements in efficiency and lower the implementation cost without substantially sacrificing the accuracy. We do an in depth analysis of the reduction in resolution vs. loss in accuracy as the basis for designing a system with the lowest cost and acceptable accuracy using NGS data from samples containing multiple bacteria from the labs of one of the co-authors. The objective of this method is to design a bacterial recognition system for battery operated clinical use where the area, power and performance are of critical importance. We demonstrate that with 39% loss in accuracy in 12 hits and 1% in 16 bit representation can yield significant savings in energy and area.
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| 58. |
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| 59. |
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| 60. |
- Torabi Moghadam, Behrooz, et al.
(författare)
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An unsupervised approach subgroups cancer types by distinct local DNA methylation patterns
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Cancer is one of the most common causes of death in humans. It can arise from many different cell types, and even cancers originating from the same tissue can constitute a heterogeneous group of diseases. While cytogenetics, the analysis of mutations and karyotypic alterations, has greatly improved the accuracy of diagnosis, it is likely that there are more categories in which cancers can be divided than is known today. Moreover, new biomarkers confirming existing classification schemes are desirable. Here, we interrogated the DNA methylation (DNAm) landscape as a novel indicator for discerning cancer subtypes.We developed and applied an unsupervised method, methylSaguaro, which is based on the combination of a Hidden Markov Model and a Neural Net. We first compared the concept of hypothesizing patterns and grouping to statistical methods that require a priori hypotheses to perform enrichment tests. We then analyzed samples from four cancer groups, Gliomas, Chronic Lymphocytic Leukemia (CLL), Renal Cell Carcinomas (RCC), and Acute Myeloid Leukemia (AML). On gliomas and CLL, we confirmed known cancer groupings in DNAm that perfectly correspond to known mutations. On Renal Cell Carcinomas, our method disagrees with the histological classification on 4% of the samples, and finds a novel cluster, suggesting that there might be a novel subtype that was hitherto unknown. On AML, methylSaguaro spreads the samples out on a continuous spectrum, enriching one end with patients assessed as having “poor” risk based on cytogenetics, but indicating that DNAm patterns would suggest a different risk assessment. Since methylSaguaro reports both the patterns and the specific sites behind the signals, we analyzed regions and genes indicative of subtypes across the cancers, revealing 41 genes affected by alterations in more than one cancer. In summary, we expect that DNAm, coupled with a hypothesis-free analysis method, will add to the set of clinical instruments to diagnose, assess, and treat cancer.
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