12,829 research outputs found
Refining interaction search through signed iterative Random Forests
Advances in supervised learning have enabled accurate prediction in
biological systems governed by complex interactions among biomolecules.
However, state-of-the-art predictive algorithms are typically black-boxes,
learning statistical interactions that are difficult to translate into testable
hypotheses. The iterative Random Forest algorithm took a step towards bridging
this gap by providing a computationally tractable procedure to identify the
stable, high-order feature interactions that drive the predictive accuracy of
Random Forests (RF). Here we refine the interactions identified by iRF to
explicitly map responses as a function of interacting features. Our method,
signed iRF, describes subsets of rules that frequently occur on RF decision
paths. We refer to these rule subsets as signed interactions. Signed
interactions share not only the same set of interacting features but also
exhibit similar thresholding behavior, and thus describe a consistent
functional relationship between interacting features and responses. We describe
stable and predictive importance metrics to rank signed interactions. For each
SPIM, we define null importance metrics that characterize its expected behavior
under known structure. We evaluate our proposed approach in biologically
inspired simulations and two case studies: predicting enhancer activity and
spatial gene expression patterns. In the case of enhancer activity, s-iRF
recovers one of the few experimentally validated high-order interactions and
suggests novel enhancer elements where this interaction may be active. In the
case of spatial gene expression patterns, s-iRF recovers all 11 reported links
in the gap gene network. By refining the process of interaction recovery, our
approach has the potential to guide mechanistic inquiry into systems whose
scale and complexity is beyond human comprehension
A Multi-Layered Study on Harmonic Oscillations in Mammalian Genomics and Proteomics
Cellular, organ, and whole animal physiology show temporal variation predominantly featuring 24-h (circadian) periodicity. Time-course mRNA gene expression profiling in mouse liver showed two subsets of genes oscillating at the second (12-h) and third (8-h) harmonic of the prime (24-h) frequency. The aim of our study was to identify specific genomic, proteomic, and functional properties of ultradian and circadian subsets. We found hallmarks of the three oscillating gene subsets, including different (i) functional annotation, (ii) proteomic and electrochemical features, and (iii) transcription factor binding motifs in upstream regions of 8-h and 12-h oscillating genes that seemingly allow the link of the ultradian gene sets to a known circadian network. Our multifaceted bioinformatics analysis of circadian and ultradian genes suggests that the different rhythmicity of gene expression impacts physiological outcomes and may be related to transcriptional, translational and post-translational dynamics, as well as to phylogenetic and evolutionary components
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Synergistic drug combinations from electronic health records and gene expression.
ObjectiveUsing electronic health records (EHRs) and biomolecular data, we sought to discover drug pairs with synergistic repurposing potential. EHRs provide real-world treatment and outcome patterns, while complementary biomolecular data, including disease-specific gene expression and drug-protein interactions, provide mechanistic understanding.MethodWe applied Group Lasso INTERaction NETwork (glinternet), an overlap group lasso penalty on a logistic regression model, with pairwise interactions to identify variables and interacting drug pairs associated with reduced 5-year mortality using EHRs of 9945 breast cancer patients. We identified differentially expressed genes from 14 case-control human breast cancer gene expression datasets and integrated them with drug-protein networks. Drugs in the network were scored according to their association with breast cancer individually or in pairs. Lastly, we determined whether synergistic drug pairs found in the EHRs were enriched among synergistic drug pairs from gene-expression data using a method similar to gene set enrichment analysis.ResultsFrom EHRs, we discovered 3 drug-class pairs associated with lower mortality: anti-inflammatories and hormone antagonists, anti-inflammatories and lipid modifiers, and lipid modifiers and obstructive airway drugs. The first 2 pairs were also enriched among pairs discovered using gene expression data and are supported by molecular interactions in drug-protein networks and preclinical and epidemiologic evidence.ConclusionsThis is a proof-of-concept study demonstrating that a combination of complementary data sources, such as EHRs and gene expression, can corroborate discoveries and provide mechanistic insight into drug synergism for repurposing
From data towards knowledge: Revealing the architecture of signaling systems by unifying knowledge mining and data mining of systematic perturbation data
Genetic and pharmacological perturbation experiments, such as deleting a gene
and monitoring gene expression responses, are powerful tools for studying
cellular signal transduction pathways. However, it remains a challenge to
automatically derive knowledge of a cellular signaling system at a conceptual
level from systematic perturbation-response data. In this study, we explored a
framework that unifies knowledge mining and data mining approaches towards the
goal. The framework consists of the following automated processes: 1) applying
an ontology-driven knowledge mining approach to identify functional modules
among the genes responding to a perturbation in order to reveal potential
signals affected by the perturbation; 2) applying a graph-based data mining
approach to search for perturbations that affect a common signal with respect
to a functional module, and 3) revealing the architecture of a signaling system
organize signaling units into a hierarchy based on their relationships.
Applying this framework to a compendium of yeast perturbation-response data, we
have successfully recovered many well-known signal transduction pathways; in
addition, our analysis have led to many hypotheses regarding the yeast signal
transduction system; finally, our analysis automatically organized perturbed
genes as a graph reflecting the architect of the yeast signaling system.
Importantly, this framework transformed molecular findings from a gene level to
a conceptual level, which readily can be translated into computable knowledge
in the form of rules regarding the yeast signaling system, such as "if genes
involved in MAPK signaling are perturbed, genes involved in pheromone responses
will be differentially expressed"
Wide-Scale Analysis of Human Functional Transcription Factor Binding Reveals a Strong Bias towards the Transcription Start Site
We introduce a novel method to screen the promoters of a set of genes with
shared biological function, against a precompiled library of motifs, and find
those motifs which are statistically over-represented in the gene set. The gene
sets were obtained from the functional Gene Ontology (GO) classification; for
each set and motif we optimized the sequence similarity score threshold,
independently for every location window (measured with respect to the TSS),
taking into account the location dependent nucleotide heterogeneity along the
promoters of the target genes. We performed a high throughput analysis,
searching the promoters (from 200bp downstream to 1000bp upstream the TSS), of
more than 8000 human and 23,000 mouse genes, for 134 functional Gene Ontology
classes and for 412 known DNA motifs. When combined with binding site and
location conservation between human and mouse, the method identifies with high
probability functional binding sites that regulate groups of biologically
related genes. We found many location-sensitive functional binding events and
showed that they clustered close to the TSS. Our method and findings were put
to several experimental tests. By allowing a "flexible" threshold and combining
our functional class and location specific search method with conservation
between human and mouse, we are able to identify reliably functional TF binding
sites. This is an essential step towards constructing regulatory networks and
elucidating the design principles that govern transcriptional regulation of
expression. The promoter region proximal to the TSS appears to be of central
importance for regulation of transcription in human and mouse, just as it is in
bacteria and yeast.Comment: 31 pages, including Supplementary Information and figure
Transcription factor target prediction using multiple short expression time series from Arabidopsis thaliana
BACKGROUND: The central role of transcription factors (TFs) in higher eukaryotes has led to much interest in deciphering transcriptional regulatory interactions. Even in the best case, experimental identification of TF target genes is error prone, and has been shown to be improved by considering additional forms of evidence such as expression data. Previous expression based methods have not explicitly tried to associate TFs with their targets and therefore largely ignored the treatment specific and time dependent nature of transcription regulation. RESULTS: In this study we introduce CERMT, Covariance based Extraction of Regulatory targets using Multiple Time series. Using simulated and real data we show that using multiple expression time series, selecting treatments in which the TF responds, allowing time shifts between TFs and their targets and using covariance to identify highly responding genes appear to be a good strategy. We applied our method to published TF - target gene relationships determined using expression profiling on TF mutants and show that in most cases we obtain significant target gene enrichment and in half of the cases this is sufficient to deliver a usable list of high-confidence target genes. CONCLUSION: CERMT could be immediately useful in refining possible target genes of candidate TFs using publicly available data, particularly for organisms lacking comprehensive TF binding data. In the future, we believe its incorporation with other forms of evidence may improve integrative genome-wide predictions of transcriptional networks
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