A5015603009- Bioinformatics and Genomic Networks
- Gene expression and cancer classification
- Gene Regulatory Network Analysis
- Computational Drug Discovery Methods
- Complex Network Analysis Techniques
- Genetic Associations and Epidemiology
- Microbial Metabolic Engineering and Bioproduction
- Functional Brain Connectivity Studies
- RNA Research and Splicing
- Machine Learning in Bioinformatics
- Pharmacogenetics and Drug Metabolism
- Cancer therapeutics and mechanisms
- Molecular Biology Techniques and Applications
- Genomics and Chromatin Dynamics
- Mental Health Research Topics
- Metabolomics and Mass Spectrometry Studies
- Genomics and Phylogenetic Studies
- Graph theory and applications
- CRISPR and Genetic Engineering
- COVID-19 epidemiological studies
- Data-Driven Disease Surveillance
- Cell Image Analysis Techniques
- Epigenetics and DNA Methylation
- Genomic variations and chromosomal abnormalities
- Tuberculosis Research and Epidemiology
University of Trento
2011-2025
The Microsoft Research - University of Trento Centre for Computational and Systems Biology
2018-2025
Fondazione Bruno Kessler
2011-2017
Fondazione Edmund Mach
2012-2016
Abstract The availability of high-quality RNA-sequencing and genotyping data post-mortem brain collections from consortia such as CommonMind Consortium (CMC) the Accelerating Medicines Partnership for Alzheimer’s Disease (AMP-AD) enable generation a large-scale cis- eQTL meta-analysis. Here we generate cerebral cortical 1433 samples available four cohorts (identifying >4.1 million significant >18,000 genes), well cerebellar 261 874,836 >10,000 genes). We find substantially improved...
We introduce a novel implementation in ANSI C of the MINE family algorithms for computing maximal information-based measures dependence between two variables large datasets, with aim low memory footprint and ease integration within bioinformatics pipelines. provide libraries minerva (with R interface) minepy Python, MATLAB, Octave C++. The solution reduces requirement original Java implementation, has good upscaling properties, offers native parallelization interface. Low requirements are...
mlpy is a Python Open Source Machine Learning library built on top of NumPy/SciPy and the GNU Scientific Libraries. provides wide range state-of-the-art machine learning methods for supervised unsupervised problems it aimed at finding reasonable compromise among modularity, maintainability, reproducibility, usability efficiency. multiplatform, works with 2 3 distributed under GPL3 website http://mlpy.fbk.eu.
Abstract Structural variants (SVs) contribute to many disorders, yet, functionally annotating them remains a major challenge. Here, we integrate SVs with RNA-sequencing from human post-mortem brains quantify their dosage and regulatory effects. We show that genic exist at significantly lower frequencies than intergenic SVs. Functional impact of copy number (CNVs) stems both the proportion content altered loss-of-function intolerance gene. train linear model predict expression effects rare...
Introduction: Understanding drug exposure at disease target sites is pivotal to profiling new candidates in terms of tolerability and efficacy. Such quantification particularly tedious for anti-tuberculosis (TB) compounds as the heterogeneous pulmonary microenvironment due infection may alter lung permeability affect disposition. Murine models have been a longstanding support TB research so far are here used human surrogates unveil distribution several anti-TB site-of-action via novel...
Tuberculosis (TB) poses a significant threat to global health, with millions of new infections and approximately one million deaths annually. Various modeling efforts have emerged, offering tailored data-driven physiologically-based solutions for novel historical compounds. However, this diverse panorama may lack consistency, limiting result comparability. Drug-specific models are often tied commercial software developed on various platforms languages, potentially hindering access...
Comparing and classifying graphs represent two essential steps for network analysis, across different scientific applicative domains. Here we deal with both operations by introducing the Hamming-Ipsen-Mikhailov (HIM) distance, a novel metric to quantitatively measure difference between sharing same vertices. The new combines local Hamming edit distance global Ipsen-Mikhailov spectral so overcome drawbacks affecting components when considered separately. Building kernel function derived from...
The number of available algorithms to infer a biological network from dataset high-throughput measurements is overwhelming and keeps growing. However, evaluating their performance unfeasible unless 'gold standard' measure how close the reconstructed ground truth. One this stability these predictions data resampling approaches. We introduce NetSI, family Network Stability Indicators, assess quantitatively in terms inference variability due subsampling. In order evaluate stability, main NetSI...
The outcome of a functional genomics pipeline is usually partial list genomic features, ranked by their relevance in modelling biological phenotype terms classification or regression model. Due to resampling protocols meta-analysis comparison, it often the case that sets alternative feature lists (possibly different lengths) are obtained, instead just one list. Here we introduce method, based on permutations, for studying variability between ("list stability") unequal length. We provide...
Motivation :Reconstructing the topology of a gene regulatory network is one key tasks in systems biology. Despite wide variety proposed methods, very little work has been dedicated to assessment their stability properties. Here we present methodical comparison performance novel method (RegnANN) for inference based on multilayer perceptrons with three reference algorithms (ARACNE, CLR, KELLER), focussing our analysis prediction variability induced by both intrinsic structure and available...
When modeling coexpression networks from high-throughput time course data, Pearson Correlation Coefficient (PCC) is one of the most effective and popular similarity functions. However, its reliability limited since it cannot capture non-linear interactions shifts. Here we propose to overcome these two issues by employing a novel function, Dynamic Time Warping Maximal Information (DTW-MIC), combining measure taking care functional signals (MIC) identifying lag (DTW). By using...
Mixing patterns of human populations play a crucial role in shaping the spreading paths infectious diseases. The diffusion mobile and wearable devices able to record close proximity interactions represents great opportunity for gathering detailed data on social mixing populations. aim this study is investigate how are affected by onset symptomatic conditions what extent heterogeneity behavior can reflect different risk infection. We relation between individuals' symptoms, making use...
Functional genomic and epigenomic research relies fundamentally on sequencing based methods like ChIP-seq for the detection of DNA-protein interactions. These techniques return large, high dimensional data sets with visually complex structures, such as multi-modal peaks extended over large regions. Current tools visualisation exploration represent leverage these features only to a limited extent.We present DGW, an open source software package simultaneous alignment clustering multiple marks....
High-throughput technologies make it possible to produce a large amount of data representing different biological layers, examples which are genomics, proteomics, metabolomics and transcriptomics. Omics have been individually investigated understand the molecular bases various diseases, but this may not be sufficient fully capture mechanisms multilayer regulatory processes underlying complex especially cancer. To overcome problem, several multi-omics integration methods introduced commonly...