A5065673127- Single-cell and spatial transcriptomics
- Bioinformatics and Genomic Networks
- Gene Regulatory Network Analysis
- Gene expression and cancer classification
- Computational Drug Discovery Methods
- RNA and protein synthesis mechanisms
- RNA Research and Splicing
- Genomics and Phylogenetic Studies
- Cell Image Analysis Techniques
- Fungal and yeast genetics research
- MicroRNA in disease regulation
- Microbial Metabolic Engineering and Bioproduction
- Machine Learning in Bioinformatics
- Extracellular vesicles in disease
- Genomics and Chromatin Dynamics
- Genetics, Bioinformatics, and Biomedical Research
- CRISPR and Genetic Engineering
- Protein Structure and Dynamics
- Immune cells in cancer
- RNA modifications and cancer
- Neuroinflammation and Neurodegeneration Mechanisms
- Genetics, Aging, and Longevity in Model Organisms
- Pluripotent Stem Cells Research
- Molecular Biology Techniques and Applications
- Cancer-related molecular mechanisms research
Sanofi (United States)
2023-2025
Carnegie Mellon University
2016-2025
Sanofi (China)
2023-2024
Sanofi (France)
2023-2024
Salk Institute for Biological Studies
2013
University of Pittsburgh
2008-2012
Columbia University
2011
Massachusetts Institute of Technology
2001-2003
Whitehead Institute for Biomedical Research
2003
Hebrew University of Jerusalem
1998
We have determined how most of the transcriptional regulators encoded in eukaryote Saccharomyces cerevisiae associate with genes across genome living cells. Just as maps metabolic networks describe potential pathways that may be used by a cell to accomplish processes, this network regulator-gene interactions describes yeast cells can use regulate global gene expression programs. information identify motifs, simplest units architecture, and demonstrate an automated process motifs assemble...
Abstract Background Time series microarray experiments are widely used to study dynamical biological processes. Due the cost of experiments, and also in some cases limited availability material, about 80% time short (3–8 points). Previously gene expression data has been mainly analyzed using more general analysis tools not designed for unique challenges opportunities inherent data. Results We introduce Short Time-series Expression Miner (STEM) first software program specifically STEM...
We present the first practical algorithm for optimal linear leaf ordering of trees that are generated by hierarchical clustering. Hierarchical clustering has been extensively used to analyze gene expression data, and we show how can reveal biological structure is not observed with an existing heuristic method. For a tree n leaves, there 2(n-1) orderings consistent tree. Our runs in time O(n(4)), further improvements make running our practical.
Time series expression experiments are used to study a wide range of biological systems. More than 80% all time datasets short (8 points or fewer). These present unique challenges. On account the large number genes profiled (often tens thousands) and small many patterns expected arise at random. Most clustering algorithms unable distinguish between real random patterns.We an algorithm specifically designed for data. Our works by assigning predefined set model profiles that capture potential...
Environmental stresses are universally encountered by microbes, plants, and animals. Yet systematic studies of stress-responsive transcription factor (TF) networks in multicellular organisms have been limited. The phytohormone abscisic acid (ABA) influences the expression thousands genes, allowing us to characterize complex regulatory networks. Using chromatin immunoprecipitation sequencing, we identified genome-wide targets 21 ABA-related TFs construct a comprehensive network Arabidopsis...
The gaseous plant hormone ethylene regulates a multitude of growth and developmental processes. How the numerous control pathways are coordinated by transcriptional response remains elusive. We characterized dynamic identifying targets master regulator signaling pathway, ETHYLENE INSENSITIVE3 (EIN3), using chromatin immunoprecipitation sequencing transcript during timecourse treatment. Ethylene-induced transcription occurs in temporal waves regulated EIN3, suggesting distinct layers control....
The influence of the high intracellular concentration macromolecules on cell physiology is increasingly appreciated, but its impact system-level cellular functions remains poorly quantified. To assess potential effect, here we develop a flux balance model Escherichia coli metabolism that takes into account systems-level constraint for enzymes catalyzing various metabolic reactions in crowded cytoplasm. We demonstrate model's predictions relative maximum growth rate wild-type and mutant E....
Abstract Protein–protein interactions play a key role in many biological systems. High‐throughput methods can directly detect the set of interacting proteins yeast, but results are often incomplete and exhibit high false‐positive false‐negative rates. Recently, different research groups independently suggested using supervised learning to integrate direct indirect data sources for protein interaction prediction task. However, sources, approaches, implementations varied. Furthermore, task...
Transformative technologies are enabling the construction of three dimensional (3D) maps tissues with unprecedented spatial and molecular resolution. Over next seven years, NIH Common Fund Human Biomolecular Atlas Program (HuBMAP) intends to develop a widely accessible framework for comprehensively mapping human body at single-cell resolution by supporting technology development, data acquisition, detailed mapping. HuBMAP will integrate its efforts other funding agencies, programs,...
Section:ChooseTop of pageAbstract <<Biology Alveolar Injur...Role ECMPreclinical ModelingThe Role Inflammation ...Genetic, Epigenetic, and ...Translation Discoverie...SummaryReferencesCITING ARTICLES
While only recently developed, the ability to profile expression data in single cells (scRNA-Seq) has already led several important studies and findings. However, this technology also raised new computational challenges. These include questions about best methods for clustering scRNA-Seq data, how identify unique group of such experiments, determine state or function specific based on their profile. To address these issues we develop test a method neural networks (NN) analysis retrieval cell...
Several methods were developed to mine gene-gene relationships from expression data. Examples include correlation and mutual information for coexpression analysis, clustering undirected graphical models functional assignments, directed pathway reconstruction. Using an encoding gene data, followed by deep neural networks we present a framework that can successfully address all of these diverse tasks. We show our method, convolutional network (CNNC), improves upon prior in tasks ranging...
Abstract Most methods for inferring gene-gene interactions from expression data focus on intracellular interactions. The availability of high-throughput spatial opens the door to that can infer such both within and between cells. To achieve this, we developed Graph Convolutional Neural networks Genes (GCNG). GCNG encodes information as a graph combines it with using supervised training. improves upon prior used analyze transcriptomics propose novel pairs extracellular interacting genes....
We present algorithms for time-series gene expression analysis that permit the principled estimation of unobserved time points, clustering, and dataset alignment. Each profile is modeled as a cubic spline (piecewise polynomial) estimated from observed data every point influences overall smooth curve. constrain coefficients genes in same class to have similar patterns, while also allowing specific parameters. show points can be reconstructed using our method with 10-15% less error when...