21,480 research outputs found
From patterned response dependency to structured covariate dependency: categorical-pattern-matching
Data generated from a system of interest typically consists of measurements
from an ensemble of subjects across multiple response and covariate features,
and is naturally represented by one response-matrix against one
covariate-matrix. Likely each of these two matrices simultaneously embraces
heterogeneous data types: continuous, discrete and categorical. Here a matrix
is used as a practical platform to ideally keep hidden dependency among/between
subjects and features intact on its lattice. Response and covariate dependency
is individually computed and expressed through mutliscale blocks via a newly
developed computing paradigm named Data Mechanics. We propose a categorical
pattern matching approach to establish causal linkages in a form of information
flows from patterned response dependency to structured covariate dependency.
The strength of an information flow is evaluated by applying the combinatorial
information theory. This unified platform for system knowledge discovery is
illustrated through five data sets. In each illustrative case, an information
flow is demonstrated as an organization of discovered knowledge loci via
emergent visible and readable heterogeneity. This unified approach
fundamentally resolves many long standing issues, including statistical
modeling, multiple response, renormalization and feature selections, in data
analysis, but without involving man-made structures and distribution
assumptions. The results reported here enhance the idea that linking patterns
of response dependency to structures of covariate dependency is the true
philosophical foundation underlying data-driven computing and learning in
sciences.Comment: 32 pages, 10 figures, 3 box picture
Machine Learning and Integrative Analysis of Biomedical Big Data.
Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues
Computational structure‐based drug design: Predicting target flexibility
The role of molecular modeling in drug design has experienced a significant revamp in the last decade. The increase in computational resources and molecular models, along with software developments, is finally introducing a competitive advantage in early phases of drug discovery. Medium and small companies with strong focus on computational chemistry are being created, some of them having introduced important leads in drug design pipelines. An important source for this success is the extraordinary development of faster and more efficient techniques for describing flexibility in three‐dimensional structural molecular modeling. At different levels, from docking techniques to atomistic molecular dynamics, conformational sampling between receptor and drug results in improved predictions, such as screening enrichment, discovery of transient cavities, etc. In this review article we perform an extensive analysis of these modeling techniques, dividing them into high and low throughput, and emphasizing in their application to drug design studies. We finalize the review with a section describing our Monte Carlo method, PELE, recently highlighted as an outstanding advance in an international blind competition and industrial benchmarks.We acknowledge the BSC-CRG-IRB Joint Research Program in Computational Biology. This work was supported by a grant
from the Spanish Government CTQ2016-79138-R.J.I. acknowledges support from SVP-2014-068797, awarded by the Spanish Government.Peer ReviewedPostprint (author's final draft
Statistical modeling of RNA structure profiling experiments enables parsimonious reconstruction of structure landscapes.
RNA plays key regulatory roles in diverse cellular processes, where its functionality often derives from folding into and converting between structures. Many RNAs further rely on co-existence of alternative structures, which govern their response to cellular signals. However, characterizing heterogeneous landscapes is difficult, both experimentally and computationally. Recently, structure profiling experiments have emerged as powerful and affordable structure characterization methods, which improve computational structure prediction. To date, efforts have centered on predicting one optimal structure, with much less progress made on multiple-structure prediction. Here, we report a probabilistic modeling approach that predicts a parsimonious set of co-existing structures and estimates their abundances from structure profiling data. We demonstrate robust landscape reconstruction and quantitative insights into structural dynamics by analyzing numerous data sets. This work establishes a framework for data-directed characterization of structure landscapes to aid experimentalists in performing structure-function studies
A General Spatio-Temporal Clustering-Based Non-local Formulation for Multiscale Modeling of Compartmentalized Reservoirs
Representing the reservoir as a network of discrete compartments with
neighbor and non-neighbor connections is a fast, yet accurate method for
analyzing oil and gas reservoirs. Automatic and rapid detection of coarse-scale
compartments with distinct static and dynamic properties is an integral part of
such high-level reservoir analysis. In this work, we present a hybrid framework
specific to reservoir analysis for an automatic detection of clusters in space
using spatial and temporal field data, coupled with a physics-based multiscale
modeling approach. In this work a novel hybrid approach is presented in which
we couple a physics-based non-local modeling framework with data-driven
clustering techniques to provide a fast and accurate multiscale modeling of
compartmentalized reservoirs. This research also adds to the literature by
presenting a comprehensive work on spatio-temporal clustering for reservoir
studies applications that well considers the clustering complexities, the
intrinsic sparse and noisy nature of the data, and the interpretability of the
outcome.
Keywords: Artificial Intelligence; Machine Learning; Spatio-Temporal
Clustering; Physics-Based Data-Driven Formulation; Multiscale Modelin
- …