21 research outputs found
On Learning with LAD
The logical analysis of data, LAD, is a technique that yields two-class
classifiers based on Boolean functions having disjunctive normal form (DNF)
representation. Although LAD algorithms employ optimization techniques, the
resulting binary classifiers or binary rules do not lead to overfitting. We
propose a theoretical justification for the absence of overfitting by
estimating the Vapnik-Chervonenkis dimension (VC dimension) for LAD models
where hypothesis sets consist of DNFs with a small number of cubic monomials.
We illustrate and confirm our observations empirically
De Novo Structural Pattern Mining in Cellular Electron Cryotomograms
Electron cryotomography enables 3D visualization of cells in a near-native state at molecular resolution. The produced cellular tomograms contain detailed information about a plethora of macromolecular complexes, their structures, abundances, and specific spatial locations in the cell. However, extracting this information in a systematic way is very challenging, and current methods usually rely on individual templates of known structures. Here, we propose a framework called “Multi-Pattern Pursuit” for de novo discovery of different complexes from highly heterogeneous sets of particles extracted from entire cellular tomograms without using information of known structures. These initially detected structures can then serve as input for more targeted refinement efforts. Our tests on simulated and experimental tomograms show that our automated method is a promising tool for supporting large-scale template-free visual proteomics analysis
Visualizing insulin vesicle neighborhoods in β cells by cryo-electron tomography
Subcellular neighborhoods, comprising specific ratios of organelles and proteins, serve a multitude of biological functions and are of particular importance in secretory cells. However, the role of subcellular neighborhoods in insulin vesicle maturation is poorly understood. Here, we present single-cell multiple distinct tomogram acquisitions of β cells for in situ visualization of distinct subcellular neighborhoods that are involved in the insulin vesicle secretory pathway. We propose that these neighborhoods play an essential role in the specific function of cellular material. In the regions where we observed insulin vesicles, a measurable increase in both the fraction of cellular volume occupied by vesicles and the average size (diameter) of the vesicles was apparent as sampling moved from the area near the nucleus toward the plasma membrane. These findings describe the important role of the nanometer-scale organization of subcellular neighborhoods on insulin vesicle maturation
Visualizing insulin vesicle neighborhoods in β cells by cryo-electron tomography
Subcellular neighborhoods, comprising specific ratios of organelles and proteins, serve a multitude of biological functions and are of particular importance in secretory cells. However, the role of subcellular neighborhoods in insulin vesicle maturation is poorly understood. Here, we present single-cell multiple distinct tomogram acquisitions of β cells for in situ visualization of distinct subcellular neighborhoods that are involved in the insulin vesicle secretory pathway. We propose that these neighborhoods play an essential role in the specific function of cellular material. In the regions where we observed insulin vesicles, a measurable increase in both the fraction of cellular volume occupied by vesicles and the average size (diameter) of the vesicles was apparent as sampling moved from the area near the nucleus toward the plasma membrane. These findings describe the important role of the nanometer-scale organization of subcellular neighborhoods on insulin vesicle maturation
Understanding the functional impact of copy number alterations in breast cancer using a network modeling approach
Copy number alterations (CNAs) are thought to account for 85% of the
variation in gene expression observed among breast tumours. The expression of
cis-associated genes is impacted by CNAs occurring at proximal loci of these
genes, whereas the expression of trans-associated genes is impacted by CNAs
occurring at distal loci. While a majority of these CNA-driven genes
responsible for breast tumourigenesis are cis-associated, trans-associated
genes are thought to further abet the development of cancer and influence
disease outcomes in patients. Here we present a network-based approach that
integrates copy-number and expression profiles to identify putative cis- and
trans-associated genes in breast cancer pathogenesis. We validate these cis-
and trans-associated genes by employing them to subtype a large cohort of
breast tumours obtained from the METABRIC consortium, and demonstrate that
these genes accurately reconstruct the ten subtypes of breast cancer. We
observe that individual breast cancer subtypes are driven by distinct sets of
cis- and trans-associated genes. Among the cis-associated genes, we recover
several known drivers of breast cancer (e.g. CCND1, ERRB2, MDM2 and ZNF703) and
some novel putative drivers (e.g. BRF2 and SF3B3). siRNA-mediated knockdown of
BRF2 across a panel of breast cancer cell lines showed significant reduction
specifically in cell proliferation in HER2+ lines, thereby indicating that BRF2
could be a context-dependent oncogene and potentially targetable in these
lines. Among the trans-associated genes, we identify modules of immune-response
(CD2, CD19, CD38 and CD79B), mitotic/cell-cycle kinases (e.g. AURKB, MELK, PLK1
and TTK), and DNA-damage response genes (e.g. RFC4 and FEN1).Comment: 23 pages, 2 tables, 7 figure
De Novo Structural Pattern Mining in Cellular Electron Cryotomograms
Electron cryotomography enables 3D visualization of cells in a near-native state at molecular resolution. The produced cellular tomograms contain detailed information about a plethora of macromolecular complexes, their structures, abundances, and specific spatial locations in the cell. However, extracting this information in a systematic way is very challenging, and current methods usually rely on individual templates of known structures. Here, we propose a framework called “Multi-Pattern Pursuit” for de novo discovery of different complexes from highly heterogeneous sets of particles extracted from entire cellular tomograms without using information of known structures. These initially detected structures can then serve as input for more targeted refinement efforts. Our tests on simulated and experimental tomograms show that our automated method is a promising tool for supporting large-scale template-free visual proteomics analysis
Visualizing insulin vesicle neighborhoods in β cells by cryo-electron tomography
Subcellular neighborhoods, comprising specific ratios of organelles and proteins, serve a multitude of biological functions and are of particular importance in secretory cells. However, the role of subcellular neighborhoods in insulin vesicle maturation is poorly understood. Here, we present single-cell multiple distinct tomogram acquisitions of β cells for in situ visualization of distinct subcellular neighborhoods that are involved in the insulin vesicle secretory pathway. We propose that these neighborhoods play an essential role in the specific function of cellular material. In the regions where we observed insulin vesicles, a measurable increase in both the fraction of cellular volume occupied by vesicles and the average size (diameter) of the vesicles was apparent as sampling moved from the area near the nucleus toward the plasma membrane. These findings describe the important role of the nanometer-scale organization of subcellular neighborhoods on insulin vesicle maturation
Inferring synthetic lethal interactions from mutual exclusivity of genetic events in cancer
Background: Synthetic lethality (SL) refers to the genetic interaction between two or more genes where only their co-alteration (e.g. by mutations, amplifications or deletions) results in cell death. In recent years, SL has emerged as an attractive therapeutic strategy against cancer: by targeting the SL partners of altered genes in cancer cells, these cells can be selectively killed while sparing the normal cells. Consequently, a number of studies have attempted prediction of SL interactions in human, a majority by extrapolating SL interactions inferred through large-scale screens in model organisms. However, these predicted SL interactions either do not hold in human cells or do not include genes that are (frequently) altered in human cancers, and are therefore not attractive in the context of cancer therapy