1,721,323 research outputs found
A network approach for low dimensional signatures from high throughput data
Full text linkOne of the main objectives of high-throughput genomics studies is to obtain a low-dimensional set of observables—a signature—for sample classification purposes (diagnosis, prognosis, stratification). Biological data, such as gene or protein expression, are commonly characterized by an up/down regulation behavior, for which discriminant-based methods could perform with high accuracy and easy interpretability. To obtain the most out of these methods features selection is even more critical, but it is known to be a NP-hard problem, and thus most feature selection approaches focuses on one feature at the time (k-best, Sequential Feature Selection, recursive feature elimination). We propose DNetPRO, Discriminant Analysis with Network PROcessing, a supervised network-based signature identification method. This method implements a network-based heuristic to generate one or more signatures out of the best performing feature pairs. The algorithm is easily scalable, allowing efficient computing for high number of observables ([Formula: see text] –[Formula: see text] ). We show applications on real high-throughput genomic datasets in which our method outperforms existing results, or is compatible with them but with a smaller number of selected features. Moreover, the geometrical simplicity of the resulting class-separation surfaces allows a clearer interpretation of the obtained signatures in comparison to nonlinear classification models
Multiscale characterization of ageing and cancer progression by a novel network entropy measure
No full textWe characterize different cell states, related to cancer and ageing phenotypes, by a measure of entropy of network ensembles, integrating gene expression profiling values and protein interaction network topology. In our case studies, network entropy, that by definition estimates the number of possible network instances satisfying the given constraints, can be interpreted as a measure of the ‘‘parameter space’’ available to the cell. Network entropy was able to characterize specific pathological conditions: normal versus cancer cells, primary tumours that developed metastasis or relapsed, and extreme longevity samples. Moreover, this approach has been applied at different scales, from whole network to specific subnetworks (biological pathways defined on a priori biological knowledge) and single nodes (genes), allowing a deeper understanding of the cell processes involved
Inflammaging and human longevity in the omics era
No full textInflammaging is a recent theory of aging originally proposed in 2000 where data and conceptualizations regarding the aging of the immune system (immunosenescence) and the evolution of immune responses from invertebrates to mammals converged. This theory has received an increasing number of citations and experimental confirmations. Here we present an updated version of inflammaging focused on omics data â particularly on glycomics â collected on centenarians, semi-supercentenarians and their offspring. Accordingly, we arrived to the following conclusions: i) inflammaging has a structure where specific combinations of pro- and anti-inflammatory mediators are involved; ii) inflammaging is systemic and more complex than we previously thought, as many organs, tissues and cell types participate in producing pro- and anti-inflammatory stimuli defined âmolecular garbageâ; iii) inflammaging is dynamic, can be propagated locally to neighboring cells and systemically from organ to organ by circulating products and microvesicles, and amplified by chronic age-related diseases constituting a âlocal fireâ, which in turn produces additional inflammatory stimuli and molecular garbage; iv) an integrated Systems Medicine approach is urgently needed to let emerge a robust and highly informative set/combination of omics markers able to better grasp the complex molecular core of inflammaging in elderly and centenarians
rFBP: Replicated Focusing Belief Propagation algorithm
Full text linkThe rFBP project implements a scikit-learn compatible machine-learning binary classifier leveraging fully connected neural networks with a learning algorithm (Replicated Focusing Belief Propagation, rFBP) that is quickly converging and robust (less prone to brittle overfitting) for ill-posed datasets (very few samples compared to the number of features). The current implementation works only with binary features such as one-hot encoding for categorical data.
This library has already been widely used to successfully predict source attribution starting from GWAS (Genome Wide Association Studies) data. That study was trying to predict the animal origin for an infectious bacterial disease inside the H2020 European project COMPARE (Grant agreement ID: 643476). A full description of the pipeline used in this study is available in the abstract and slides provided into the publications folder of the project.
Algorithm application on real data:
Classification of Genome Wide Association data by Belief Propagation Neural network, CCS Italy 2019, Conference paper
Classification of Genome Wide Association data by Belief Propagation Neural network, CCS Italy 2019, Conference slide
COVID-19 Lung Segmentation
Full text linkThe COVID-19 Lung Segmentation project provides a novel, unsupervised and fully auto-
mated pipeline for the semantic segmentation of ground-glass opacity (GGO) areas in chest
Computer Tomography (CT) scans of patients affected by COVID-19. In the project we
provide a series of scripts and functions for the automated segmentation of lungs 3D areas,
segmentation of GGO areas, and estimation of radiomic features
Biological applications of conductive polymers
No full textStarting from the last decade, conductive polymers have been employed to realize a wide variety of devices applied in biological research, thanks to their favourable electrical, mechanical and biocompatibility properties in respect to traditional inorganic semiconductors. In this abstract, a few examples that illustrate the coupling between organic electronics and biology are considered, with a particular regard to poly(3,4-ethylenedioxytiophene) (PEDOT) based devices
CATS: a bioinformatic tool for automated Cas9 nucleases activity comparison in clinically relevant contexts
Full text linkIntroduction: With the growing number of Cas9 nucleases available to genetic
engineers, selecting the most suitable one for a given application can be
challenging. A major complication arises from the differing protospacer
adjacent motif (PAM) sequence requirements of each Cas9 variant, which
makes direct comparisons difficult. To ensure a fair comparison, it is essential
to identify common target sites that are not biased by the natural genetic
landscape of the chosen target.
Methods: To address this challenge, we developed CATS (Comparing Cas9
Activities by Target Superimposition), a novel bioinformatic tool. CATS
automates the detection of overlapping PAM sequences across different Cas9
nucleases and identifies allele-specific targets, particularly those arising from
pathogenic mutations. One of the key parameters in CATS is the proximity of PAM
sites, which helps minimize sequence composition bias. The tool integrates data
from continuously updated sources and includes ClinVar information to facilitate
the targeting of disease-causing mutations.
Results: CATS significantly reduces the time and effort required for CRISPR/Cas9
experimental design. It streamlines the comparison of Cas9 nucleases with
different PAM requirements, enabling researchers to select the most
appropriate nuclease for their specific target. The tool’s automation, speed,
and user-friendly interface make it accessible to researchers regardless of
their computational expertise.
Discussion: By enabling the identification of overlapping PAMs and allele-specific
targets, CATS supports the implementation of Cas9-based applications in both
research and clinical settings. Its ability to incorporate genetic variants makes it
particularly useful for designing therapeutic approaches that selectively target
mutated alleles while sparing healthy ones. Ultimately, CATS contributes to the
development of more effective and precise genetic therapies
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