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Modeling and design of RNA-only structures

BIOMEDICAL DATA SCIENCE PRESENTS:
BIODS 260C
4/13/23 1:30PM-2:50PM
MSOB X303 (SEE ZOOM DETAILS BELOW)
Rhiju Das
Associate Professor of Biochemistry; Stanford University

Title:

Modeling and design of RNA-only structures

Abstract:

The discovery and design of biologically important RNA molecules has lagged behind proteins, in part due to the general difficulty of three-dimensional RNA structural characterization. What are the prospects for an AlphaFold for RNA? I’ll describe some recent progress in modeling RNA structure from old-fashioned and new machine learning, cryoelectron microscopy, and internet-scale competitions hosted on the Eterna, Kaggle, and CASP platforms.

Reading/viewing list:

“RNA structure: a renaissance begins?” https://www.nature.com/articles/s41592-021-01132-4
“RNA secondary structure packages evaluated and improved by high-throughput experiments”

https://www.nature.com/articles/s41592-022-01605-0

Zoom link: https://stanford.zoom.us/j/94324405118? pwd=WnR3Y1dqK3plYWREN0RNVjRlNnhEUT09&from=addon

Meeting ID: 943 2440 5118

Password: 366430

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Communication-Efficient Distributed Estimation and Inference for Cox’s Model

BIOMEDICAL DATA SCIENCE PRESENTS:
BIODS 260C
4/6/23 1:30PM-2:50PM
MSOB X303 (SEE ZOOM DETAILS BELOW)
Jianqing Fan
Princeton University

TITLE:

Communication-Efficient Distributed Estimation and Inference for Cox’s Model

ABSTRACT:

Motivated by multi-center biomedical studies that cannot share individual data due to privacy and ownership concerns, we develop communication-efficient iterative distributed algorithms for estimation and inference in the high- dimensional sparse Cox proportional hazards model. We demonstrate that our estimator, with a relatively small number of iterations, achieves the same convergence rate as the ideal full-sample estimator under very mild conditions. To construct confidence intervals for linear combinations of high-dimensional hazard regression coefficients, we introduce a novel debiased method, establish central limit theorems, and provide consistent variance estimators that yield asymptotically valid distributed confidence intervals. In addition, we provide valid and powerful distributed hypothesis tests for any of its coordinate elements based on decorrelated score test. We allow time-dependent covariates as well as censored survival times. Extensive numerical experiments on both simulated and real data lend further support to our theory and demonstrate that our communication-efficient distributed estimators, confidence intervals, and hypothesis tests improve upon alternative methods. (Joint work with Pierre Bayle and Zhipeng Lou).

Website: https://fan.princeton.edu/

Zoom link: https://stanford.zoom.us/j/94324405118? pwd=WnR3Y1dqK3plYWREN0RNVjRlNnhEUT09&from=addon

Meeting ID: 943 2440 5118

Password: 366430

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The Spatial Landscape of Progression and Immunoediting in Primary Melanoma at Single-Cell Resolution

BIODS 260
Dr. Ajit Johnson Nirmal
9/29/22
1:30 pm-2:30 pm
Dr. Ajit Johnson Nirmal, Instructor, Dana-Farber Cancer Institute

Seminar Title: The Spatial Landscape of Progression and Immunoediting in Primary Melanoma at Single-Cell Resolution

Abstract: Cutaneous melanoma is a highly immunogenic malignancy that is surgically curable at early stages but life-threatening when metastatic. Here we integrate high-plex imaging, 3D high-resolution microscopy, and spatially resolved microregion transcriptomics to study immune evasion and immunoediting in primary melanoma. We find that recurrent cellular neighborhoods involving tumor, immune, and stromal cells change significantly along a progression axis involving precursor states, melanoma in situ, and invasive tumor. Hallmarks of immunosuppression are already detectable in precursor regions. When tumors become locally invasive, a consolidated and spatially restricted suppressive environment forms along the tumor–stromal boundary. This environment is established by cytokine gradients that promote expression of MHC-II and IDO1, and by PD1–PDL1-mediated cell contacts involving macrophages, dendritic cells, and T cells. A few millimeters away, cytotoxic T cells synapse with melanoma cells in fields of tumor regression. Thus, invasion and immunoediting can coexist within a few millimeters of each other in a single specimen.

Suggested reading: 

The Spatial Landscape of Progression and Immunoediting in Primary Melanoma at Single-Cell Resolution

Zoom link: HTTPS://STANFORD.ZOOM.US/J/95499501659? PWD=TLFQTLBRM1JSEGXXWXFFELFIQLBCZZ09&FROM=ADDON PASSWORD: 406712

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Precision health for all: developing inclusive datasets and algorithms

BIODS 260
Roxana Daneshjou
10/06/22
1:30 pm-2:30 pm
Dr. Roxana Daneshjou

Location: MSOB x303 

Seminar Title: Precision health for all: developing inclusive datasets and algorithms

Abstract: Large biomedical datasets coupled with machine learning tools have the potential to transform the practice of dermatology. For example, analysis of skin disease images could help triage patients prior to the clinical visit and precision genomic medicine could identify personalized treatments for skin disease. However, biased datasets and algorithms that exclude underrepresented groups could exacerbate existing health disparities in dermatology. This talk will discuss working towards inclusive precision medicine through three examples: 1) assessing fairness in datasets and AI algorithms used for diagnosing disease in dermatology 2) developing an inclusive patient-facing algorithm to improve the quality of images submitted for teledermatology and 3) developing a pharmacogenomics algorithm that accounts for population diversity.  In order to develop a data-driven approach to dermatology that improves health disparities, rather than exacerbating them, we must be mindful of developing inclusive datasets and algorithms.

Suggested Readings:

Zoom link:
Meeting ID: 983 6641 4259
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