Computational Biology (ML4Bio) Postdoctoral Fellow – Gibson Lab

Opening for a Postdoctoral Research Fellow to join the Gibson Lab https://gibsonlab.io at Harvard Medical School and Brigham and Women’s Hospital. We leverage tools from machine learning and control theory to understand biological systems. Control theoretic concepts are integrated both in the design of our optimization schemes and statistical machine learning models, as well as in the design of our in vitro and in vivo experiments. Our main area of focus is the microbiome and microbial dynamics more specifically. Applications include the design of bacteriotherapies (bugs-as-drugs), developing methods to learn microbial dynamics at ecosystem-scale, studying the impact of phages on microbial communities, methods for tracking low abundance pathogens, and methods for integrating multiple data modalities and prior knowledge (from other studies or databases) in time-series models. We focus on Bayesian methods that propagate measurement uncertainty throughout the model so that we can access confidence in model parameters and to help prioritize follow-up experiments. ML techniques applied include variational inference, Bayesian non-parametric models, and relaxation techniques (for making discrete models differentiable).

The specific project(s) you will be working on will fall under one of the following grants. Follow the links below for more details:

The candidate will also be encouraged to design their own experiments as well, which would then be carried out by staff in the germ-free mouse facility or in a collaborating wet lab. In addition to addressing our biological questions we also include key experimental components that can aid in validating our methods that would otherwise not be included in a purely hypothesis driven experiment. For a candidate wanting some wet lab experience we are looking to develop new single-molecule enzyme-linked immunosorbent assays (digital ELISA) to measure low abundance host inflammatory markers in feces (please mention this explicitly in your cover letter if interested).

Qualifications

  • PhD in computer science, applied mathematics, ecology, computational biology, systems biology, statistics, or other quantitative discipline
  • Excellent publication track record
  • Strong mathematical background with track record developing novel models and methods
  • Solid programming skills in Python; this isn’t a software engineering job, but you will need to be able to develop efficient implementations and apply your work to real biomedical data
  • Ability to reside in the U.S. and legally work in the country.

About the lab environment

The Gibson Lab is located in the Division of Computational Pathology at Brigham and Women’s Hospital (BWH), a Harvard Medical School teaching hospital, which is the second largest non-university recipient of NIH research funding. The broad mandate of the Division of Computational Pathology is to develop and apply advanced computational methods for furthering the understanding, diagnosis, and treatment of human diseases. The Division is situated within the BWH Department of Pathology, which houses over 40+ established investigators, 50+ postdoctoral research fellows, and 100+ research support staff. In addition, BWH is part of the greater Longwood Medical Area in Boston, a rich, stimulating environment conducive to intellectual development and research collaborations, which includes the Harvard Medical School quad, Harvard School of Public Health, Boston Children’s Hospital, and the Dana Farber Cancer Institute. Many of our lab members also have appointments at the Massachusetts Institute of Technology and the Broad Institute.

Applications Process

Submit: (1) brief research statement (not to exceed 2 pages); (2) curriculum vitae; (3) two most relevant publications; (4) names and contact information of three individuals who can serve as references to: Travis Gibson, tegibson@bwh.harvard.edu. If you wish to chat briefly over Zoom before submitting materials to learn more details about our ongoing work, please inquire about this possibility.

We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, gender identity, sexual orientation, pregnancy and pregnancy-related conditions or any other characteristic protected by law.

October ABC Seminar: Weiruo Zhang, PhD, Stanford University

October ABC Seminar: Weiruo Zhang, PhD, Stanford University

Spatial biology is a new frontier that has become accessible through advances in spatial profiling technologies, such as multiplexed in situ imaging spatial proteomics, which can provide single-cell resolution up to 60 markers. In this talk, I will introduce a computational analysis pipeline that performs integrative analysis of spatial proteomics and single-cell RNA sequencing to identify clinically-relevant cellular interactions. The pipeline features (1) CELESTA, an unsupervised machine learning method for cell type identification in multiplexed spatial proteomics data; (2) a geospatial statistical method to identify cell-cell colocalizations; and (3) an integrative coupling of spatial proteomics and single-cell RNA sequencing data that identified cell-cell crosstalk associated with lymph node metastasis in head and neck cancer which we have validated through mouse model studies.

Research link:
https://profiles.stanford.edu/weiruo-zhang

Dr. Zhang is currently a Research Engineer at the Department of Biomedical Data Science and the Center for Cancer Systems Biology, Stanford School of Medicine. Dr. Zhang received her M.S. and Ph.D. in Electrical Engineering, both from Stanford University, with a focus on bioinformatics and developing computational algorithms for metabolomics data analysis. Her current research at Stanford primarily focuses on developing and implementing computational methods to integrate and analyze single-cell and spatial multi-omics data, such as single-cell RNA sequencing, spatial proteomics and spatial transcriptomics. Her research aims to apply quantitative approaches that bridge multi-omics, imaging, machine learning, and artificial intelligence to decipher biology for cancer progression and guide treatment responses.

 

Gerber Lab awarded $3.1 Million Five Year NIH-NIGMS R35 Grant “Probabilistic deep learning models and integrated biological experiments for analyzing dynamic and heterogeneous microbiomes”

Gerber Lab awarded $3.1 Million Five Year NIH-NIGMS R35 Grant “Probabilistic deep learning models and integrated biological experiments for analyzing dynamic and heterogeneous microbiomes”

This work will leverage deep learning technologies to advance the microbiome field beyond finding associations in data, to accurately predicting the effects of perturbations on microbiota, elucidating mechanisms through which the microbiota affects the host, and improving bacteriotherapies to enable their success in the clinic. New deep learning models will be developed that address specific challenges for the microbiome, including noisy/small datasets, highly heterogenous human microbiomes, the need for direct interpretability of model outputs, complex multi-modal datasets, and constraints imposed by biological principles. Computational models and biological experiments will be directly coupled through reinforcing cycles of predicting, testing predictions with new experiments, and improving models. An important objective will also be to make computational tools widely available to the research community, through release of quality open-source software.

RePORTER Link

 

2023-24 ABC Seminar Speakers Announced

2023-24 ABC Seminar Speakers Announced

Developed by the Computational Pathology Division at Brigham and Women’s Hospital, the Advanced Biomedical Computation (ABC) Seminar, held monthly during the academic year, showcases innovative research from around the globe by up-and-coming investigators that are developing and applying advanced computational methods to solve biomedical problems.

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Mahmood receives 2023 Young Mentors Award from Harvard Medical School

Mahmood receives 2023 Young Mentors Award from Harvard Medical School

Faisal Mahmood, PhD received the 2023 Young Mentor Award from Harvard Medical School for his outstanding contributions to mentorship.

The Young Mentor Award is one of several Excellence in Mentoring Awards that HMS established to recognize the value of quality mentoring relationships and the impact they have on the professional development and career advancement in medicine, teaching, research and administration. Created in 2005, the Young Mentor Award honors HMS faculty who are still in the early stages of their career but are devoting their time to providing mentoring for others.

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Gerber Lab at ICML Workshop on Computational Biology 2023

Gerber Lab at ICML Workshop on Computational Biology 2023

The ICML Workshop on Computational Biology (WCB) highlights how ML approaches can be tailored to making both translational and basic scientific discoveries with biological data, such as genetic sequences, cellular features or protein structures and imaging datasets, among others. It aims to bring together interdisciplinary ML researchers working in areas such as computational genomics; neuroscience; metabolomics; proteomics; bioinformatics; cheminformatics; pathology; radiology; evolutionary biology; population genomics; phenomics; ecology, cancer biology; causality; representation learning and disentanglement to present recent advances and open questions to the machine learning community.

The Gerber Lab had the following two papers accepted:

Gerber GK, Bhattarai SK, Du M, Glickman MS, Bucci V. Discovery of Host-Microbiome Interactions Using Multi-Modal, Sparse, Time-Aware, Bayesian Network-Structured Neural Topic Models. International Conference on Machine Learning Workshop on Computational Biology, 2023.

Uppal G, Urtecho G, Richardson M, Moody T, Wang HH, Gerber GK. MC-SPACE: Microbial communities from spatially associated counts engine. International Conference on Machine Learning Workshop on Computational Biology, 2023.

 

Algorithmic fairness in artificial intelligence for medicine and healthcare: Nature Biomedical Engineering

Algorithmic fairness in artificial intelligence for medicine and healthcare: Nature Biomedical Engineering

In healthcare, the development and deployment of insufficiently fair systems of artificial intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models stratified across subpopulations have revealed inequalities in how patients are diagnosed, treated and billed. In this Perspective, we outline fairness in machine learning through the lens of healthcare, and discuss how algorithmic biases (in data acquisition, genetic variation and intra-observer labelling variability, in particular) arise in clinical workflows and the resulting healthcare disparities. We also review emerging technology for mitigating biases via disentanglement, federated learning and model explainability, and their role in the development of AI-based software as a medical device.

Chen RJ, Wang JJ, Williamson DFK, Chen TY, Lipkova J, Lu MY, Sahai S, Mahmood F. Algorithmic fairness in artificial intelligence for medicine and healthcare. Nat Biomed Eng. 2023 06; 7(6):719-742. PMID: 37380750.

Jan. 30, 2023: Lambda Moses, California Institute of Technology

“From geospatial to spatial -omics with SpatialFeatureExperiment and Voyager”; https://partners.zoom.us/j/82826415806; To bring more of the geospatial tradition to spatial -omics, we developed SpatialFeatureExperiment (SFE), which extends the existing Bioconductor data structures SingleCellExperiment (SCE) and SpatialExperiment (SPE) with Simple Features to represent the geometries of Visium spots and cell segmentation and perform geometric operations. We developed the Voyager package that performs exploratory spatial data analysis (ESDA) on SFE objects.

Mahmood Lab develops deep learning model for transforming tissue images: Nature Biomedical Engineering 2022

Mahmood Lab develops deep learning model for transforming tissue images: Nature Biomedical Engineering 2022

Histological artefacts in cryosectioned tissue can hinder rapid diagnostic assessments during surgery. Formalin-fixed and paraffin-embedded (FFPE) tissue provides higher quality slides, but the process for obtaining them is laborious (typically lasting 12–48 h) and hence unsuitable for intra-operative use. Here we report the development and performance of a deep-learning model that improves the quality of cryosectioned whole-slide images by transforming them into the style of whole-slide FFPE tissue within minutes. The model consists of a generative adversarial network incorporating an attention mechanism that rectifies cryosection artefacts and a self-regularization constraint between the cryosectioned and FFPE images for the preservation of clinically relevant features. Transformed FFPE-style images of gliomas and of non-small-cell lung cancers from a dataset independent from that used to train the model improved the rates of accurate tumour subtyping by pathologists.

Ozyoruk, K.B., Can, S., Darbaz, B. et al. A deep-learning model for transforming the style of tissue images from cryosectioned to formalin-fixed and paraffin-embedded. Nat. Biomed. Eng 6, 1407–1419 (2022). https://doi.org/10.1038/s41551-022-00952-9