about
Deep learning for computational biology.Crop improvement using life cycle datasets acquired under field conditionsFFPred 3: feature-based function prediction for all Gene Ontology domains.Novel tools for quantifying secondary growthIn-silico modeling of granulomatous diseasesLearning from Heterogeneous Data Sources: An Application in Spatial ProteomicsNeuroblastoma, a Paradigm for Big Data Science in Pediatric Oncology.From Data to Improved Decisions: Operations Research in Healthcare Delivery.A machine learning approach for viral genome classification.Reproducible quantitative proteotype data matrices for systems biology.GOexpress: an R/Bioconductor package for the identification and visualisation of robust gene ontology signatures through supervised learning of gene expression dataUse of big data in drug development for precision medicine.Performance Evaluation and Online Realization of Data-driven Normalization Methods Used in LC/MS based Untargeted Metabolomics Analysis.Physico-chemical fingerprinting of RNA genesThe effect of mislabeled phenotypic status on the identification of mutation-carriers from SNP genotypes in dairy cattle.Deep biomarkers of human aging: Application of deep neural networks to biomarker development.Genome-enabled prediction using probabilistic neural network classifiersMachine learning approaches in MALDI-MSI: clinical applications.ML2Motif-Reliable extraction of discriminative sequence motifs from learning machines.Open Source Bayesian Models. 3. Composite Models for Prediction of Binned ResponsesReflections on the Field of Human Genetics: A Call for Increased Disease Genetics TheoryAntimicrobial Resistance Prediction in PATRIC and RAST.Transcriptome Profiling of Antimicrobial Resistance in Pseudomonas aeruginosaMachine learning, statistical learning and the future of biological research in psychiatry.Multiparametric MRI-based differentiation of WHO grade II/III glioma and WHO grade IV glioblastomaA Meta-Analysis Based Method for Prioritizing Candidate Genes Involved in a Pre-specific Function.The association of variants in PNPLA3 and GRP78 and the risk of developing hepatocellular carcinoma in an Italian population.Systematic evaluation of supervised classifiers for fecal microbiota-based prediction of colorectal cancer.Towards an integrated food safety surveillance system: a simulation study to explore the potential of combining genomic and epidemiological metadata.In silico prediction of novel therapeutic targets using gene-disease association data.Integrated Computational Analysis of Genes Associated with Human Hereditary Insensitivity to Pain. A Drug Repurposing Perspective.Discovery of novel therapeutic properties of drugs from transcriptional responses based on multi-label classification.Machine learning for epigenetics and future medical applications.Regulatory element-based prediction identifies new susceptibility regulatory variants for osteoporosis.A new genome-mining tool redefines the lasso peptide biosynthetic landscape.Deep learning for healthcare: review, opportunities and challenges.Systematic Computational Identification of Variants That Activate Exonic and Intronic Cryptic Splice Sites.Systematic review of blood transcriptome profiling in neuropsychiatric disorders: guidelines for biomarker discovery.Deep learning in bioinformatics.Walking through the statistical black boxes of plant breeding.
P2860
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P2860
description
2015 nî lūn-bûn
@nan
2015年の論文
@ja
2015年論文
@yue
2015年論文
@zh-hant
2015年論文
@zh-hk
2015年論文
@zh-mo
2015年論文
@zh-tw
2015年论文
@wuu
2015年论文
@zh
2015年论文
@zh-cn
name
Machine learning applications in genetics and genomics.
@en
type
label
Machine learning applications in genetics and genomics.
@en
prefLabel
Machine learning applications in genetics and genomics.
@en
P2860
P356
P1476
Machine learning applications in genetics and genomics.
@en
P2093
Maxwell W Libbrecht
P2860
P2888
P304
P356
10.1038/NRG3920
P577
2015-05-07T00:00:00Z
P6179
1040939097