DIVISION NEWS
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Gerber Lab’s “MDITRE: Scalable and Interpretable Machine Learning for Predicting Host Status from Temporal Microbiome Dynamics” is mSystems Editor’s Pick
Longitudinal microbiome data sets are being generated with increasing regularity, and there is broad recognition that these studies are critical for unlocking the mechanisms through which the microbiome impacts human […]
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Mahmood Lab’s Pan-cancer integrative histology-genomic analysis is featured on cover of Cancer Cell
The rapidly emerging field of computational pathology has demonstrated promise in developing objective prognostic models from histology images. However, most prognostic models are either based on histology or genomics alone […]
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The Massachusetts Lab for Artificial Intelligence/Deep Learning for the Microbiome
Through a $3.3M grant from the Massachusetts Life Science Center and in-kind support from Brigham and Women’s Hospital and Mass General Brigham, the BWH Massachusetts Host-Microbiome Center (MHMC) and Division […]
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Gerber lab study showing gut metabolites predict C. diff recurrence
Clostridioides difficile infection (CDI) is the most common hospital acquired infection in the USA, with recurrence rates > 15%. Although primary CDI has been extensively linked to gut microbial dysbiosis, less […]
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Mahmood Lab’s deep learning-enabled assessment of cardiac transplant rejection study is published in Nature Medicine
Endomyocardial biopsy (EMB) screening represents the standard of care for detecting allograft rejections after heart transplant. Manual interpretation of EMBs is affected by substantial interobserver and intraobserver variability, which often […]
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Gibson Lab receives $2.2 Million NIH R35 grant “Machine Learning and Control Principles for Computational Biology “
Grant Abstract: With our increasing ability to measure biological data at scale and the digitalization of health records, computational thinking is becoming ever more important in the biological science and […]
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Gibson Lab receives $450K NIH R21 grant “Tracking the microbiome: purpose-built machine learning tools for tracking microbial strains over time”
Grant Abstract: Approximately 150 million people annually experience urinary tract infections (UTI), the most common cause of which is uropathogenic Escherichia coli (UPEC). The gut is a known reservoir of […]
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Mahmood Lab’s study on AI-based cancer origin prediction using conventional histology is published in Nature
Cancer of unknown primary (CUP) origin is an enigmatic group of diagnoses in which the primary anatomical site of tumour origin cannot be determined1,2. This poses a considerable challenge, as […]
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Mahmood Lab’s CLAM method, A Deep-Learning-based Pipeline for Data Efficient and Weakly Supervised Whole-Slide-level Analysis, published in Nature Biomedical Engineering
Deep-learning methods for computational pathology require either manual annotation of gigapixel whole-slide images (WSIs) or large datasets of WSIs with slide-level labels and typically suffer from poor domain adaptation and […]
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$2.9M grant from the National Science Foundation “The rules of microbiota colonization of the mammalian gut”
The Gerber lab in collaboration with the Wang lab at Columbia and the Gibson Lab at BWH have received a $2.9M grant from the National Science Foundation to develop and apply novel […]