Alex Hakansson
Discovery Institute
GeneCancerEndothelial stem cellData miningCellGene expressionSet (psychology)ReceptorCell typeConserved sequenceOperonMultiple myelomaMonoclonal gammopathy of undetermined significanceSoftwareBone marrowDrugAngiogenesisSoftware toolCell sorterSingle cell transcriptomicsVisualizationComputer scienceSource codeHopfield networkArtificial neural networkPython (programming language)Content-addressable memoryRNAComputational biologyCluster analysisAnomaly detectionIdentification (information)BiologyClassifier (UML)Interactome
6Publications
1H-index
3Citations
Publications 5
Newest
#1Sergii Domanskyi (MSU: Michigan State University)H-Index: 7
#2Alex Hakansson (DI: Discovery Institute)H-Index: 1
Last. Napoleone Ferrara (UCSD: University of California, San Diego)H-Index: 179
view all 7 authors...
VEGF inhibitor drugs have been successful, especially in ophthalmology, but not all patients respond to them. Combinations of drugs are likely to be needed for a really effective therapy of angiogenesis-related diseases. In this paper we describe naturally occurring combinations of receptors in endothelial cells that might help to identify targets for drug combinations. We also develop and share a new computational method and a software tool called DECNEO to identify them. Single-cell gene expre...
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#1Sergii Domanskyi (MSU: Michigan State University)H-Index: 7
#2Alex Hakansson (DI: Discovery Institute)H-Index: 1
Last. Carlo Piermarocchi (MSU: Michigan State University)H-Index: 28
view all 5 authors...
Motivation Analysis of singe cell RNA sequencing (scRNA-seq) typically consists of different steps including quality control, batch correction, clustering, cell identification and characterization, and visualization. The amount of scRNA-seq data is growing extremely fast, and novel algorithmic approaches improving these steps are key to extract more biological information. Here, we introduce: (i) two methods for automatic cell type identification (i.e., without expert curator) based on a voting ...
3 CitationsSource
#1S. Domanskyi (UCSD: University of California, San Diego)
#1Sergii Domanskyi (UCSD: University of California, San Diego)H-Index: 7
Last. Napoleone Ferrara (UCSD: University of California, San Diego)H-Index: 179
view all 7 authors...
VEGF inhibitor drugs have been successful, especially in ophthalmology, but not all patients respond to them. Combinations of drugs are likely to be needed for a really effective therapy of angiogenesis-related diseases. In this paper we introduce a new concept, the comberon, a term named by analogy with the operon that refers to evolutionarily conserved combinations of co-expressed genes. These genes identify potential drug targets. Our results show that single-cell gene expression data can hel...
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#1Carlo Piermarocchi (MSU: Michigan State University)H-Index: 28
#2Sergii Domanskyi (MSU: Michigan State University)H-Index: 7
Last. Giovanni Paternostro (DI: Discovery Institute)H-Index: 20
view all 4 authors...
The Hopfield neural network model is one of the simplest models able to mathematically implement Waddington’s interpretation of normal and anomalous cell phenotypes as dynamical attractors of epigenetic landscapes. Here, we propose a computational approach based on Hopfield’s associative memories that integrate gene expression data and gene interactome networks in (1) a model representing the dynamics and control of disease progression in multiple myeloma (MM), and (2) a model describing the con...
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#1Sergii Domanskyi (MSU: Michigan State University)H-Index: 7
#2Alex HakanssonH-Index: 1
Last. Carlo Piermarocchi (MSU: Michigan State University)H-Index: 28
view all 4 authors...
Associative memories in Hopfield's neural networks are mapped to gene expression pattern to model different paths of disease progression towards Multiple Myeloma (MM). The model is built using single cell RNA-seq data from bone marrow aspirates of MM patients as well as patients diagnosed with Monoclonal Gammopathy of Undetermined Significance (MGUS) and Smoldering Multiple Myeloma (SMM), two medical conditions that often progress to full MM. Results: We identify different clusters of MGUS, SMM,...
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