Matthew Ruffalo
Overview
Explore the profile of Matthew Ruffalo including associated specialties, affiliations and a list of published articles.
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14
Citations
214
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Recent Articles
1.
Borner K, Blood P, Silverstein J, Ruffalo M, Satija R, Teichmann S, et al.
Nat Methods
. 2025 Mar;
PMID: 40082611
The Human BioMolecular Atlas Program (HuBMAP) aims to construct a 3D Human Reference Atlas (HRA) of the healthy adult body. Experts from 20+ consortia collaborate to develop a Common Coordinate...
2.
Sun H, Yu S, Martinez Casals A, Backstrom A, Lu Y, Lindskog C, et al.
bioRxiv
. 2024 Sep;
PMID: 39345395
Identifying cell types in highly multiplexed images is essential for understanding tissue spatial organization. Current cell type annotation methods often rely on extensive reference images and manual adjustments. In this...
3.
Borner K, Blood P, Silverstein J, Ruffalo M, Satija R, Teichmann S, et al.
bioRxiv
. 2024 Jun;
PMID: 38826261
The Human BioMolecular Atlas Program (HuBMAP) aims to construct a reference 3D structural, cellular, and molecular atlas of the healthy adult human body. The HuBMAP Data Portal (https://portal.hubmapconsortium.org) serves experimental...
4.
Dayao M, Trevino A, Kim H, Ruffalo M, DAngio H, Preska R, et al.
Bioinformatics
. 2023 Jun;
39(39 Suppl 1):i140-i148.
PMID: 37387167
Motivation: Spatial proteomics data have been used to map cell states and improve our understanding of tissue organization. More recently, these methods have been extended to study the impact of...
5.
Song Q, Ruffalo M, Bar-Joseph Z
Nucleic Acids Res
. 2023 Feb;
51(7):e38.
PMID: 36762475
Inference of global gene regulatory networks from omics data is a long-term goal of systems biology. Most methods developed for inferring transcription factor (TF)-gene interactions either relied on a small...
6.
Yuan Y, Cosme Jr C, Adams T, Schupp J, Sakamoto K, Xylourgidis N, et al.
PLoS Comput Biol
. 2022 Sep;
18(9):e1010468.
PMID: 36095011
Studies comparing single cell RNA-Seq (scRNA-Seq) data between conditions mainly focus on differences in the proportion of cell types or on differentially expressed genes. In many cases these differences are...
7.
Ruffalo M, Bar-Joseph Z
BMC Cancer
. 2019 Apr;
19(1):370.
PMID: 31014259
Background: Most methods that integrate network and mutation data to study cancer focus on the effects of genes/proteins, quantifying the effect of mutations or differential expression of a gene and...
8.
Ruffalo M, Thomas R, Chen J, Lee A, Oesterreich S, Bar-Joseph Z
PLoS Comput Biol
. 2019 Feb;
15(2):e1006730.
PMID: 30742607
Prediction of response to specific cancer treatments is complicated by significant heterogeneity between tumors in terms of mutational profiles, gene expression, and clinical measures. Here we focus on the response...
9.
Alavi A, Ruffalo M, Parvangada A, Huang Z, Bar-Joseph Z
Nat Commun
. 2018 Nov;
9(1):4768.
PMID: 30425249
Single cell RNA-Seq (scRNA-seq) studies profile thousands of cells in heterogeneous environments. Current methods for characterizing cells perform unsupervised analysis followed by assignment using a small set of known marker...
10.
Ruffalo M, Stojanov P, Pillutla V, Varma R, Bar-Joseph Z
BMC Syst Biol
. 2017 Oct;
11(1):96.
PMID: 29017547
Background: Translating in vitro results to clinical tests is a major challenge in systems biology. Here we present a new Multi-Task learning framework which integrates thousands of cell line expression...