about
Novel tools for quantifying secondary growthSequence-based prediction of protein protein interaction using a deep-learning algorithm.DeepCpG: accurate prediction of single-cell DNA methylation states using deep learningKnowledge about the presence or absence of miRNA isoforms (isomiRs) can successfully discriminate amongst 32 TCGA cancer typesMachine learning in computational biology to accelerate high-throughput protein expression.In silico prediction of novel therapeutic targets using gene-disease association data.Development and assessment of a lysophospholipid-based deep learning model to discriminate geographical origins of white rice.Optimizing drug development in oncology by clinical trial simulation: Why and how?Looking beyond the cancer cell for effective drug combinations.Machine learning applications in cell image analysis.DeepSite: Protein binding site predictor using 3D-convolutional neural networks.Deep learning for healthcare: review, opportunities and challenges.The Next Era: Deep Learning in Pharmaceutical Research.Accurate Classification of Protein Subcellular Localization from High-Throughput Microscopy Images Using Deep Learning.Performance of Deep and Shallow Neural Networks, the Universal Approximation Theorem, Activity Cliffs, and QSAR.Sensitive detection of rare disease-associated cell subsets via representation learning.Machine Learning-Assisted Network Inference Approach to Identify a New Class of Genes that Coordinate the Functionality of Cancer NetworksAnalysing Microbial Community Composition through Amplicon Sequencing: From Sampling to Hypothesis Testing.Limb-Enhancer Genie: An accessible resource of accurate enhancer predictions in the developing limb.Models of Models: A Translational Route for Cancer Treatment and Drug Development.Denoising genome-wide histone ChIP-seq with convolutional neural networks.Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning.Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data.Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets.Automated analysis of seizure semiology and brain electrical activity in presurgery evaluation of epilepsy: A focused survey.Machine Learning Approaches in Cardiovascular Imaging.The dimensionalities of lesion-deficit mapping.Learning the High-Dimensional Immunogenomic Features That Predict Public and Private Antibody Repertoires.Gene Prediction in Metagenomic Fragments with Deep Learning.Conserved non-coding elements: developmental gene regulation meets genome organization.Protein-Protein Interactions Prediction Using a Novel Local Conjoint Triad Descriptor of Amino Acid Sequences.Particle swarm optimization-based automatic parameter selection for deep neural networks and its applications in large-scale and high-dimensional data.Towards precision medicine: from quantitative imaging to radiomics.On the prediction of DNA-binding proteins only from primary sequences: A deep learning approach.Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks.Deep Omics.Generating Modeling Data From Repeat-Dose Toxicity Reports.Statistical and integrative system-level analysis of DNA methylation data.Supervised Machine Learning for Population Genetics: A New Paradigm.Deep Learning based multi-omics integration robustly predicts survival in liver cancer.
P2860
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P2860
description
2016 nî lūn-bûn
@nan
2016 թուականի Յուլիսին հրատարակուած գիտական յօդուած
@hyw
2016 թվականի հուլիսին հրատարակված գիտական հոդված
@hy
2016年の論文
@ja
2016年論文
@yue
2016年論文
@zh-hant
2016年論文
@zh-hk
2016年論文
@zh-mo
2016年論文
@zh-tw
2016年论文
@wuu
name
Deep learning for computational biology
@nl
Deep learning for computational biology.
@ast
Deep learning for computational biology.
@en
type
label
Deep learning for computational biology
@nl
Deep learning for computational biology.
@ast
Deep learning for computational biology.
@en
prefLabel
Deep learning for computational biology
@nl
Deep learning for computational biology.
@ast
Deep learning for computational biology.
@en
P2860
P921
P3181
P356
P1476
Deep learning for computational biology.
@en
P2093
Christof Angermueller
Tanel Pärnamaa
P2860
P3181
P356
10.15252/MSB.20156651
P407
P577
2016-07-29T00:00:00Z