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The mission of our lab is to provide machine learning tools that extract meaningful insight from high-throughput, high-dimensional biomedical data. We work with data such as: Single-Cell RNA sequencing, Gut microbiome sequencing, Biomedical imaging, and 旋风加速器app官网. We are part of 旋风ios as well as Computer Science, and are located in the Cardiovascular Research Center on the 7th floor of 300 George st.

指纹app下载下载地址-雷轰加速器

David van Dijk, et al. "Recovering Gene Interactions from Single-Cell Data Using Data Diffusion." Cell (2018), http://doi.org/10.1016/j.cell.2018.05.061
Kevin R. Moon*, David van Dijk*, et al. “Visualizing Structure and Transitions for Biological Data Exploration.“ 旋风加速官网下载ios(2024) [in press], bioRxiv 120378; doi: http://doi.org/10.1101/120378
Matthew Amodio*旋风加速器ios下载二维码*, et al. “Exploring Single-Cell Data with Deep Multitasking Neural Networks.“ Nature Methods (2024) [in press], QQ旋风下载文件,会员加速时说旋风服务器正在下载文件,完成 ...:QQ旋风下载文件,会员加速时说旋风服务器正在下载文件,完成后加速,该怎么处理 ... 答：人格保证，不是，移动下载官网的QQ 等软件都飞快可众证明，你说的情况我碰到过，是因为QQ和电信合作比较早，所众大多数资源服务器托管在电信机房，电信为了打压 ...

指纹app下载下载地址-雷轰加速器

(Markov Affinity-based Graph Imputation of Cells)

Imputation and denoising of single-cell RNA-seq data using manifold learning. van Dijk, et al. Cell, 2018

指纹app下载下载地址-雷轰加速器

Screen Shot 2024-04-08 at 9.49.31 AM.png

(Potential of Heat-diffusion for Affinity-based Transition Embedding)

Embedding high-dimensional data into low dimensions for visualization. link (in press at Nature. Biotech)

MELD

(Manifold Enhancement of Latent Dimensions)

Graph signal processing tool to analyze multiple scRNA-seq samples from two or more conditions. link (bioRxiv)

PET/CT scans

Fig-2-99m-Tc-tetrofosmin-SPECT-shows-ischemia-in-the-anteroapical-segment-of-the-left.png

We’re developing tools to visualize, extract features, and predict clinical phenotypes in PET, SPECT, CT, ultrasound, and various other medical imaging data for large patient cohorts.

指纹app下载下载地址-雷轰加速器

指纹app下载下载地址-雷轰加速器

Are you excited about machine learning and do you want to make an impact in biology and medicine? The van Dijk Lab is recruiting interns, students, postdocs, programmers, and staff researchers. Background in CS, Math, or Engineering is preferred, no background in biology is required. You should be interested in working with real world data and interested in either developing new algorithms or applying existing ones to data, or both!

The van Dijk lab is part of Yale Internal Medicine (Cardiology) as well as 旋风加速器.apk. We are located at the Yale Medical School, which allows us to work closely with clinicians and have access to the most exciting datasets. Our goal is to impact both biomedicine (e.g. publish in biological and medical journals) and computer science (e.g. publish at CS and Math conferences).

For more information, send an email to: david.vandijk (at) yale.edu

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