Oxford Nanopore has built the first and only real-time, portable DNA sequencer - the MinION. It is being used to bring DNA information to researchers in many sectors, including biomedical/cancer research, environmental monitoring, agriculture, food/ water testing, and education. Oxford Nanopore is using GPUs to make sure that genomic data can be processed in real time, delivering potential benefits of rapid insights to users in any environment. Leila Luheshi and Rosemary Dokos will talk about current and potential healthcare applications of Nanopore technology, and how GPUs will turn sequence data into rapid insights for disease or environmental management.
The MULTI-X platform simplifies the logistical challenges of deploying AI and ML solutions by providing pre-configured environments with ad-hoc scalable computing resources to quickly build, test, share and reproduce scientific applications. Its comprehensible modular framework accelerates the development and reduces the burden and cost of implementing AI solutions. The talk will include details of two exemplary deployments in the area of Cardiac Image Analysis, presented together with the outcome of the analysis of 5000 subjects of the UK-Biobank database. Developing and deploying AI solutions for clinical research use cases can be complex, resource intensive, and therefore expensive and challenging to implement for many researchers, groups and healthcare organisations. In the era of Big-Data and the IoT, the most critical problems are related to the secure access and management of large heterogeneous datasets, the deployment of GPU-accelerated massive parallel processing systems, and the setup of development environments encompassing complex ML tools and applications. Two exemplary use cases of the implementation of GPU-enabled AI solutions in the area of Cardiac Image Analysis, both developed and deployed in MULTI-X, will be presented together with the outcome of the analysis of 5000 Subjects of the UK-Biobank database.
Learn about the newest advancements in brain reading and neuroprosthetics, made possible by deep learning techniques, experimentation and augmented reality. Brain reading enables us to observe what people are seeing and perceiving just by analysing their 3D brain scans, recorded while subjects are observing things in an MRI scanner. Brain writing is, to an extent, opposite, performed by direct stimulation of the brain via microelectrode arrays with the aim of inducing percepts. In developing a new generation of cortical neuroprostheses to stimulate the visual cortex, we are now working toward restoring meaningful visual perception in blind people.
A Norwegian oil company needed to fix 1 million images of seabed, taken with artificial light, to be able to create a geological orthophoto. Due to light absorption in the water, all images were bright in center and dark at the sides. The poor quality of these images stopped us from machine analysis, so we used NVIDIA CUDA to create a routine that automatically analyzed all images one by one and fixed the inconsistent lighting. Afterwards, the images could be analysed with machine learning. The routine analysed every image separately and repaired them automatically.
In this talk we will give an overview of the benefits GPU computing can provide to the Structural Bioinformatics field. We will explain how most of biomolecular simulations methods can be efficiently accelerated using massively computational architectures and will show several fundamental research and technology transfer success cases.
We believe that medicine will be more precise and affordable. Physicians will integrate relevant patient data and insights at the point of decision for precise diagnostics. Therapy will be tailored to the characteristics of both the patient and disease ? resulting in the right treatment for the right patient at the right time. AI-powered decision support could help to balance the need for personalization when it matters and standardization to reduce unwarranted variations.
It is not always easy to accelerate a complex serial algorithm with CUDA parallelization. A case in point is that of aligning bisulfite-treated DNA (bsDNA) sequences to a reference genome. A simple CUDA adaptation of a CPU-based implementation can improve the speed of this particular kind of sequence alignment, but it's possible to achieve order-of-magnitude improvements in throughput by organizing the implementation so as to ensure that the most compute-intensive parts of the algorithm execute on GPU threads.
We'll disscuss how GPUs are playing a central role in making advances in Ion Torrent's targeted sequencing workflow and talk about the S5 DNA sequencer from Ion Torrent that is enabling democratization of sequencing market and accelerating research in precision medicine at a breathtaking pace with the help of GPUs. We'll highlight our work in liquid biopsy and non-invasive prenatal testing and how the breadth in technology offerings in semiconductor chips gives us the scale of sequencing from small panels to exomes. We'll discuss our analysis pipeline and the latest and greatest in algorithm development and acceleration on GPUs as well as our experiences ranging from Fermi to Pascal GPU architectures.
Deep learning models give state-of-the-art results on diverse problems, but their lack of interpretability is a major problem. Consider a model trained to predict which DNA mutations cause disease: if the model performs well, it has likely identified patterns that biologists would like to understand. However, this is difficult if the model is a black box. We present algorithms that provide detailed explanations for individual predictions made by a deep learning model and discover recurring patterns across the entire dataset. Our algorithms address significant limitations of existing interpretability methods. We show examples from genomics where the use of deep learning in conjunction with our interpretability algorithms leads to novel biological insights.
Protecting crew health is a critical concern for NASA in preparation of long duration, deep-space missions like Mars. Spaceflight is known to affect immune cells. Splenic B-cells decrease during spaceflight and in ground-based physiological models. The key technical innovation presented by our work is end-to-end computation on the GPU with the GPU Data Frame (GDF), running on the DGXStation, to accelerate the integration of immunoglobulin gene-segments, junctional regions, and modifications that contribute to cellular specificity and diversity. Study results are applicable to understanding processes that induce immunosuppressionlike cancer therapy, AIDS, and stressful environments here on earth.
The NVIDIA Genomics Group has developed a deep learning platform to transform noisy, low-quality DNA sequencing data into clean, high-quality data. Hundreds of DNA sequencing protocols are used to profile phenomena such as protein-DNA binding and DNA accessibility. For example, the ATAC-seq protocol identifies open genomic sites by sequencing open DNA fragments; genome-wide fragment counts provide a profile of DNA accessibility. Recent advances enable profiling from smaller patient samples than previously possible. To reduce sequencing cost, we developed a convolutional neural network that denoises data from a small number of DNA fragments, making the data suitable for various downstream tasks. Our platform aims to accelerate adoption of DNA sequencers by minimizing data requirements.
Nanopore sequencing is a breakthrough technology that marries cutting edge semiconductor processes together with biochemistry, achieving fast, scalable, single molecule DNA sequencing. The challenge is real-time processing of gigabytes of data per second in a compact benchtop instrument. GPUDirect, together with the cuDNN library, enables Roche to maximize the effectiveness of Tesla V100 GPUs in their next generation sequencing instrument. Attendees will learn how these pieces come together to build a streaming AI inference engine to solve a signal processing workflow. Analysis and performance comparisons of the new TensorCore units, available on Volta hardware, will be included.cal cuDNN API