ATAC-seq is a widely-applied assay used to measure genome-wide chromatin accessibility; however, its ability to detect active regulatory regions can depend on the depth of sequencing coverage and the signal-to-noise ratio. Here we introduce AtacWorks, a deep learning toolkit to denoise sequencing coverage and identify regulatory peaks at base-pair resolution from low cell count, low-coverage, or low-quality ATAC-seq data. Models trained by AtacWorks can detect peaks from cell types not seen in the training data, and are generalizable across diverse sample preparations and experimental platforms. We demonstrate that AtacWorks enhances the sensitivity of single-cell experiments by producing results on par with those of conventional methods using ~10 times as many cells, and further show that this framework can be adapted to enable cross-modality inference of protein-DNA interactions. Finally, we establish that AtacWorks can enable new biological discoveries by identifying active regulatory regions associated with lineage priming in rare subpopulations of hematopoietic stem cells. ATAC-seq measures chromatin accessibility as a proxy for the activity of DNA regulatory regions across the genome. Here the authors present AtacWorks, a deep learning tool to denoise and identify accessible chromatin regions from low cell count, low-coverage, or low-quality ATAC-seq data.
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I trained a classifier on images of animals and gave it an image of myself, it's 98% confident I'm a dog. This is an exploration of a possible Bayesian fix. I trained a multi-class classifier on images of cats, dogs and wild animals and passed an image of myself, it's 98% confident I'm a dog. The problem isn't that I passed an inappropriate image because models in the real world are passed all sorts of garbage. It's that the model is overconfident about an image far away from the training data.
Artificial intelligence researchers at Facebook claim they have developed software that can predict the likelihood of a Covid patient deteriorating or needing oxygen based on their chest X-rays. Facebook, which worked with academics at NYU Langone Health's predictive analytics unit and department of radiology on the research, says that the software could help doctors avoid sending at-risk patients home too early, while also helping hospitals plan for oxygen demand. The 10 researchers involved in the study -- five from Facebook AI Research and five from the NYU School of Medicine -- said they have developed three machine-learning "models" in total, that are all slightly different. One tries to predict patient deterioration based on a single chest X-ray, another does the same with a sequence of X-rays, and a third uses a single X-ray to predict how much supplemental oxygen (if any) a patient might need. "Our model using sequential chest X-rays can predict up to four days (96 hours) in advance if a patient may need more intensive care solutions, generally outperforming predictions by human experts," the authors said in a blog post published Friday.
If you're a programmer, you want to explore deep learning, and need a platform to help you do it – this tutorial is exactly for you. Google Colab is a great platform for deep learning enthusiasts, and it can also be used to test basic machine learning models, gain experience, and develop an intuition about deep learning aspects such as hyperparameter tuning, preprocessing data, model complexity, overfitting and more. Colaboratory by Google (Google Colab in short) is a Jupyter notebook based runtime environment which allows you to run code entirely on the cloud. This is necessary because it means that you can train large scale ML and DL models even if you don't have access to a powerful machine or a high speed internet access. Google Colab supports both GPU and TPU instances, which makes it a perfect tool for deep learning and data analytics enthusiasts because of computational limitations on local machines.
IMAGE: SMU Assistant Professor Sun Qianru says highly diverse training data is critical to ensure the machine sees a wide range of examples and counterexamples that cancel out spurious patterns. SMU Office of Research and Tech Transfer - Artificial Intelligence, or AI, makes us look better in selfies, obediently tells us the weather when we ask Alexa for it, and rolls out self-drive cars. It is the technology that enables machines to learn from experience and perform human-like tasks. As a whole, AI contains many subfields, including natural language processing, computer vision, and deep learning. Most of the time, the specific technology at work is machine learning, which focuses on the development of algorithms that analyses data and makes predictions, and relies heavily on human supervision.
Hyperparameter tuning is one of the fundamental steps in the machine learning routine. Also known as hyperparameter optimisation, the method entails searching for the best configuration of hyperparameters to enable optimal performance. Machine learning algorithms require user-defined inputs to achieve a balance between accuracy and generalisability. This process is known as hyperparameter tuning. There are various tools and approaches available to tune hyperparameters.
You're looking for a complete Convolutional Neural Network (CNN) course that teaches you everything you need to create a Image Recognition model in Python, right? You've found the right Convolutional Neural Networks course! Identify the Image Recognition problems which can be solved using CNN Models. Create CNN models in Python using Keras and Tensorflow libraries and analyze their results. Have a clear understanding of Advanced Image Recognition models such as LeNet, GoogleNet, VGG16 etc.