„combinatorial algorithm“
Suchergebnisse
8.292 Treffer
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CLIP-EZ: a computational tool for HITS-CLIP data analysis
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Transcriptome profile of OVCAR3 cisplatin-resistant ovarian cancer cell line
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Protein structure prediction with local adjust tabu search algorithm
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An improved independent component analysis model for 3D chromatogram separation and its solution by multi-areas genetic algorithm
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Computational identification of genetic subnetwork modules associated with maize defense response to Fusarium verticillioides
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Approximate Bayesian computation schemes for parameter inference of discrete stochastic models using simulated likelihood density
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Application of the EVEX resource to event extraction and network construction: Shared Task entry and result analysis
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Building a knowledge base to assist clinical decision-making using the Pediatric Research Database (PRD) and machine learning: a case study on pediatric asthma patients
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BactoGeNIE: a large-scale comparative genome visualization for big displays
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Analysis of miRNA expression profiles in breast cancer using biclustering
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Identification of genes and pathways involved in kidney renal clear cell carcinoma
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SWAP-Assembler: scalable and efficient genome assembly towards thousands of cores
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The sensitivity and significance analysis of parameters in the model of pH regulation on lactic acid production by Lactobacillus bulgaricus
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Developing discriminate model and comparative analysis of differentially expressed genes and pathways for bloodstream samples of diabetes mellitus type 2
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A two-layered machine learning method to identify protein O-GlcNAcylation sites with O-GlcNAc transferase substrate motifs
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A practical guideline for intracranial volume estimation in patients with Alzheimer's disease
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FusionQ: a novel approach for gene fusion detection and quantification from paired-end RNA-Seq
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Proceedings of the 2014 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) Conference
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Discovering co-occurring patterns and their biological significance in protein families
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LigandRFs: random forest ensemble to identify ligand-binding residues from sequence information alone