about
Sequence-specific bias correction for RNA-seq data using recurrent neural networks.Reversal of cancer gene expression correlates with drug efficacy and reveals therapeutic targetsRecognition of prokaryotic and eukaryotic promoters using convolutional deep learning neural networksDanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences.Partitioned learning of deep Boltzmann machines for SNP data.Deep learning in bioinformatics.A multi-scale convolutional neural network for phenotyping high-content cellular images.Machine learning and computer vision approaches for phenotypic profilingPerformance of Deep and Shallow Neural Networks, the Universal Approximation Theorem, Activity Cliffs, and QSAR.Using neural networks for reducing the dimensions of single-cell RNA-Seq data.A deep ensemble model to predict miRNA-disease association.Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data.Gene Prediction in Metagenomic Fragments with Deep Learning.Collaborative representation-based classification of microarray gene expression data.Particle swarm optimization-based automatic parameter selection for deep neural networks and its applications in large-scale and high-dimensional data.Transcriptomic Changes Following Valproic Acid Treatment Promote Neurogenesis and Minimize Secondary Brain Injury.Deep Learning and its Applications in Biomedicine.Opportunities and obstacles for deep learning in biology and medicine.Genetic and Systematic Approaches Toward G Protein-Coupled Abiotic Stress Signaling in PlantsMultimodal detection of PD-L1: reasonable biomarkers for immune checkpoint inhibitorDeep learning-based transcriptome data classification for drug-target interaction predictionImproving classification of pollen grain images of the POLEN23E dataset through three different applications of deep learning convolutional neural networks
P2860
Q31162024-AE14C432-311A-48AB-863F-B95271AA0E8FQ33903831-C9A10AFB-FE31-44B0-B84C-D6B14240F7ACQ36269903-0AFFA9EA-7A45-41FA-B91B-C55223EF8665Q37021556-6651A424-21D6-421A-9A54-839F5216CA98Q38706551-B68852BA-66E2-4E6E-8700-A55FCCA433BEQ38836385-1C6B1C84-238F-480C-8835-2CFFEB12F2A8Q38958887-2CFCEC6D-D66E-4550-885C-EE96029A3DCBQ39035630-505C8C03-5086-498E-A7FD-341803A5922BQ39247822-4833510D-24B3-4AEA-861F-09DD5069D9A1Q42777960-AB1F5F87-FEDA-48C2-B6F0-374AF79E301CQ45334190-27410053-868E-4550-947F-B77620DAFB55Q45944093-FE9A9A8A-98AA-4CA7-A9AC-123E54A4BCE9Q46237755-49D66F9A-4694-4244-BD24-5EEAC883AD41Q47136273-6EC358FE-A4B6-4DFA-82BB-E6418138B73AQ47149071-E5A31B8E-16CB-4EB1-91A6-0AF5519ACF64Q47563352-9E1B360B-36A2-421B-BDDA-E723932AE850Q50420138-80E16A7E-FAFD-4B27-8146-3771605D6FD4Q52331906-1B123EB8-E1D1-4841-9DA4-6BE454A78156Q57191828-84373D14-A414-4DAE-A3C2-A8F01C6E88D2Q57494139-A0F3A36F-B1BF-476B-912C-57EF59AF7E5FQ58700262-8B800BCF-D016-486A-A94D-0F61D480B0AAQ58741335-9218F3D8-8672-4EE4-BE21-470F0027042F
P2860
description
2016 nî lūn-bûn
@nan
2016 թուականի Փետրուարին հրատարակուած գիտական յօդուած
@hyw
2016 թվականի փետրվարին 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
Gene expression inference with deep learning.
@ast
Gene expression inference with deep learning.
@en
type
label
Gene expression inference with deep learning.
@ast
Gene expression inference with deep learning.
@en
prefLabel
Gene expression inference with deep learning.
@ast
Gene expression inference with deep learning.
@en
P2093
P2860
P356
P1433
P1476
Gene expression inference with deep learning.
@en
P2093
Aravind Subramanian
Rajiv Narayan
Xiaohui Xie
Yifei Chen
P2860
P304
P356
10.1093/BIOINFORMATICS/BTW074
P407
P577
2016-02-11T00:00:00Z