| 1. |
- Greve, Christian, et al.
(författare)
-
Flexible Machine Learning Algorithms for Clinical Gait Assessment Tools
- 2022
-
Ingår i: Sensors. - : MDPI. - 1424-8220. ; 22:13
-
Tidskriftsartikel (refereegranskat)abstract
- The current gold standard of gait diagnostics is dependent on large, expensive motion-capture laboratories and highly trained clinical and technical staff. Wearable sensor systems combined with machine learning may help to improve the accessibility of objective gait assessments in a broad clinical context. However, current algorithms lack flexibility and require large training datasets with tedious manual labelling of data. The current study tests the validity of a novel machine learning algorithm for automated gait partitioning of laboratory-based and sensor-based gait data. The developed artificial intelligence tool was used in patients with a central neurological lesion and severe gait impairments. To build the novel algorithm, 2% and 3% of the entire dataset (567 and 368 steps in total, respectively) were required for assessments with laboratory equipment and inertial measurement units. The mean errors of machine learning-based gait partitions were 0.021 s for the laboratory-based datasets and 0.034 s for the sensor-based datasets. Combining reinforcement learning with a deep neural network allows significant reduction in the size of the training datasets to <5%. The low number of required training data provides end-users with a high degree of flexibility. Non-experts can easily adjust the developed algorithm and modify the training library depending on the measurement system and clinical population.
|
|
| 2. |
- Rivas-Carrillo, Salvador Daniel, et al.
(författare)
-
Chapulin : a leap forward on mobile element and structural variant identification
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Transposable elements represent a substantial proportion of eukaryotic genomes, where they can disrupt or enhance gene expression on the host. However, identification at population scale where often short sequencing signals are available is challenging. Current approaches rely on parsing sequence alignment files looking for anomalies on read length, read orientation and read depth, but they are often slow and complicated to install. Here, we present the Chapulin, a portable cross-platform, open-sourced Rust application for structural variant identification and characterization, including transposable elements. By using concurrent computing and native execution, Chapulin identifies a large fraction of mobile element insertions while outperforming existing transposable element tools. Chapulin was designed to be versatile and robust, in order to accommodate the demands of current data, such as population-scale studies or clinical samples
|
|
| 3. |
- Stathis, Dimitrios, 1989-, et al.
(författare)
-
Approximate Computing Applied to Bacterial Genome Identification using Self-Organizing Maps
- 2019
-
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.
|
|
| 4. |
- Torabi Moghadam, Behrooz, 1982-, et al.
(författare)
-
PiiL : visualization of DNA methylation and gene expression data in gene pathways
- 2017
-
Ingår i: BMC Genomics. - : Springer Science and Business Media LLC. - 1471-2164. ; 18
-
Tidskriftsartikel (refereegranskat)abstract
- Background: DNA methylation is a major mechanism involved in the epigenetic state of a cell. It has been observed that the methylation status of certain CpG sites close to or within a gene can directly affect its expression, either by silencing or, in some cases, up-regulating transcription. However, a vertebrate genome contains millions of CpG sites, all of which are potential targets for methylation, and the specific effects of most sites have not been characterized to date. To study the complex interplay between methylation status, cellular programs, and the resulting phenotypes, we present PiiL, an interactive gene expression pathway browser, facilitating analyses through an integrated view of methylation and expression on multiple levels.Results: PiiL allows for specific hypothesis testing by quickly assessing pathways or gene networks, where the data is projected onto pathways that can be downloaded directly from the online KEGG database. PiiL provides a comprehensive set of analysis features that allow for quick and specific pattern searches. Individual CpG sites and their impact on host gene expression, as well as the impact on other genes present in the regulatory network, can be examined. To exemplify the power of this approach, we analyzed two types of brain tumors, Glioblastoma multiform and lower grade gliomas.Conclusion: At a glance, we could confirm earlier findings that the predominant methylation and expression patterns separate perfectly by mutations in the IDH genes, rather than by histology. We could also infer the IDH mutation status for samples for which the genotype was not known. By applying different filtering methods, we show that a subset of CpG sites exhibits consistent methylation patterns, and that the status of sites affect the expression of key regulator genes, as well as other genes located downstream in the same pathways.PiiL is implemented in Java with focus on a user-friendly graphical interface. The source code is available under the GPL license from https://github.com/behroozt/PiiL.git.
|
|
| 5. |
- Yang, Yu, et al.
(författare)
-
RiBoSOM : Rapid bacterial genome identification using self-organizing map implemented on the synchoros SiLago platform
- 2018
-
Ingår i: ACM International Conference Proceeding Series. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450364942 ; , s. 105-114
-
Konferensbidrag (refereegranskat)abstract
- Artificial Neural Networks have been applied to many traditional machine learning applications in image and speech processing. More recently, ANNs have caught attention of the bioinformatics community for their ability to not only speed up by not having to assemble genomes but also work with imperfect data set with duplications. ANNs for bioinformatics also have the added attraction of better scaling for massive parallelism compared to traditional bioinformatics algorithms. In this paper, we have adapted Self-organizing Maps for rapid identification of bacterial genomes called BioSOM. BioSOM has been implemented on a design of two coarse grain reconfigurable fabrics customized for dense linear algebra and streaming scratchpad memory respectively. These fabrics are implemented in a novel synchoros VLSI design style that enables composition by abutment. The synchoricity empowers rapid and accurate synthesis from Matlab models to create near ASIC like efficient solution. This platform, called SiLago (Silicon Lego) is benchmarked against a GPU implementation. The SiLago implementation of BioSOMs in four different dimensions, 128, 256, 512 and 1024 Neurons, were trained for two E Coli strains of bacteria with 40K training vectors. The results of SiLago implementation were benchmarked against a GPU GTX 1070 implementation in the CUDA framework. The comparison reveals 4 to 140X speed up and 4 to 5 orders of improvement in energy-delay product compared to implementation on GPU. This extreme efficiency comes with the added benefit of automated generation of GDSII level design from Matlab by using the Synchoros VLSI design style.
|
|
