32,860 research outputs found
ModuleDigger: an itemset mining framework for the detection of cis-regulatory modules
Background: The detection of cis-regulatory modules (CRMs) that mediate transcriptional responses in eukaryotes remains a key challenge in the postgenomic era. A CRM is characterized by a set of co-occurring transcription factor binding sites (TFBS). In silico methods have been developed to search for CRMs by determining the combination of TFBS that are statistically overrepresented in a certain geneset. Most of these methods solve this combinatorial problem by relying on computational intensive optimization methods. As a result their usage is limited to finding CRMs in small datasets (containing a few genes only) and using binding sites for a restricted number of transcription factors (TFs) out of which the optimal module will be selected.
Results: We present an itemset mining based strategy for computationally detecting cis-regulatory modules (CRMs) in a set of genes. We tested our method by applying it on a large benchmark data set, derived from a ChIP-Chip analysis and compared its performance with other well known cis-regulatory module detection tools.
Conclusion: We show that by exploiting the computational efficiency of an itemset mining approach and combining it with a well-designed statistical scoring scheme, we were able to prioritize the biologically valid CRMs in a large set of coregulated genes using binding sites for a large number of potential TFs as input
Compositional optimization of hard-magnetic phases with machine-learning models
Machine Learning (ML) plays an increasingly important role in the discovery
and design of new materials. In this paper, we demonstrate the potential of ML
for materials research using hard-magnetic phases as an illustrative case. We
build kernel-based ML models to predict optimal chemical compositions for new
permanent magnets, which are key components in many green-energy technologies.
The magnetic-property data used for training and testing the ML models are
obtained from a combinatorial high-throughput screening based on
density-functional theory calculations. Our straightforward choice of
describing the different configurations enables the subsequent use of the ML
models for compositional optimization and thereby the prediction of promising
substitutes of state-of-the-art magnetic materials like NdFeB with
similar intrinsic hard-magnetic properties but a lower amount of critical
rare-earth elements.Comment: 12 pages, 6 figure
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