相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。Improved Prediction Model of Protein Lysine Crotonylation Sites Using Bidirectional Recurrent Neural Networks
Sian Soo Tng et al.
JOURNAL OF PROTEOME RESEARCH (2022)
Using deep neural networks and biological subwords to detect protein S-sulfenylation sites
Duyen Thi Do et al.
BRIEFINGS IN BIOINFORMATICS (2021)
Deep learning recognition of diseased and normal cell representation
Muhammad Shahid Iqbal et al.
TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES (2021)
Detection, Treatment Planning, and Genetic Predisposition of Bruxism: A Systematic Mapping Process and Network Visualization Technique
Md Belal Bin Heyat et al.
CNS & NEUROLOGICAL DISORDERS-DRUG TARGETS (2021)
RDH-based dynamic weighted histogram equalization using for secure transmission and cancer prediction
Rashid Abbasi et al.
MULTIMEDIA SYSTEMS (2021)
Brain tumor segmentation using K-means clustering and deep learning with synthetic data augmentation for classification
Amjad Rehman Khan et al.
MICROSCOPY RESEARCH AND TECHNIQUE (2021)
Progress in Detection of Insomnia Sleep Disorder: A Comprehensive Review
Md Belal Bin Heyat et al.
CURRENT DRUG TARGETS (2021)
i4mC-Deep: An Intelligent Predictor of N4-Methylcytosine Sites Using a Deep Learning Approach with Chemical Properties
Waleed Alam et al.
GENES (2021)
Prediction of N6-methyladenosine sites using convolution neural network model based on distributed feature representations
Muhammad Tahir et al.
NEURAL NETWORKS (2020)
A Novel Hybrid Machine Learning Classification for the Detection of Bruxism Patients Using Physiological Signals
Md Belal Bin Heyat et al.
APPLIED SCIENCES-BASEL (2020)
XG-ac4C: identification of N4-acetylcytidine (ac4C) in mRNA using eXtreme gradient boosting with electron-ion interaction pseudopotentials
Waleed Alam et al.
SCIENTIFIC REPORTS (2020)
A deep learning-based computational approach for discrimination of DNA N6-methyladenosine sites by fusing heterogeneous features
Muhammad Tahir et al.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2020)
PACES: prediction of N4-acetylcytidine (ac4C) modification sites in mRNA
Wanqing Zhao et al.
SCIENTIFIC REPORTS (2019)
Efficient cell classification of mitochondrial images by using deep learning
Muhammad Shahid Iqbal et al.
JOURNAL OF OPTICS-INDIA (2019)
Cell Recognition of Microscopy Images of TPEF (Two Photon Excited Florescence) Probes
Muhammad Shahid Iqbal et al.
2018 INTERNATIONAL CONFERENCE ON IDENTIFICATION, INFORMATION AND KNOWLEDGE IN THE INTERNET OF THINGS (2019)
Sleep Bruxism Detection Using Decision Tree Method by the Combination of C4-P4 and C4-A1 Channels of Scalp EEG
Md Belal Bin Heyat et al.
IEEE ACCESS (2019)
Mitochondrial Organelle Movement Classification (Fission and Fusion) via Convolutional Neural Network Approach
Muhammad Shahid Iqbal et al.
IEEE ACCESS (2019)
MODOMICS: a database of RNA modification pathways. 2017 update
Pietro Boccaletto et al.
NUCLEIC ACIDS RESEARCH (2018)
Acetylation of Cytidine in mRNA Promotes Translation Efficiency
Daniel Arango et al.
CELL (2018)
Ten quick tips for machine learning in computational biology
Davide Chicco
BIODATA MINING (2017)
Google DeepMind and healthcare in an age of algorithms
Julia Powles et al.
HEALTH AND TECHNOLOGY (2017)
A deep learning framework for modeling structural features of RNA-binding protein targets
Sai Zhang et al.
NUCLEIC ACIDS RESEARCH (2016)
Gene expression inference with deep learning
Yifei Chen et al.
BIOINFORMATICS (2016)
iNuc-STNC: a sequence-based predictor for identification of nucleosome positioning in genomes by extending the concept of SAAC and Chou's PseAAC
Muhammad Tahir et al.
MOLECULAR BIOSYSTEMS (2016)
Mastering the game of Go with deep neural networks and tree search
David Silver et al.
NATURE (2016)
A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction
Matt Spencer et al.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS (2015)
PSOFuzzySVM-TMH: identification of transmembrane helix segments using ensemble feature space by incorporated fuzzy support vector machine
Maqsood Hayat et al.
MOLECULAR BIOSYSTEMS (2015)
Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
Babak Alipanahi et al.
NATURE BIOTECHNOLOGY (2015)
Predicting effects of noncoding variants with deep learning-based sequence model
Jian Zhou et al.
NATURE METHODS (2015)
Yeast Kre33 and human NAT10 are conserved 18S rRNA cytosine acetyltransferases that modify tRNAs assisted by the adaptor Tan1/THUMPD1
Sunny Sharma et al.
NUCLEIC ACIDS RESEARCH (2015)
Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning
Rhys Heffernan et al.
SCIENTIFIC REPORTS (2015)
Deep learning of the tissue-regulated splicing code
Michael K. K. Leung et al.
BIOINFORMATICS (2014)
Predicting Backbone Cα Angles and Dihedrals from Protein Sequences by Stacked Sparse Auto-Encoder Deep Neural Network
James Lyons et al.
JOURNAL OF COMPUTATIONAL CHEMISTRY (2014)
Deep architectures for protein contact map prediction
Pietro Di Lena et al.
BIOINFORMATICS (2012)
Building Watson: An Overview of the DeepQA Project
David Ferrucci et al.
AI MAGAZINE (2010)
Fast model-based protein homology detection without alignment
Sepp Hochreiter et al.
BIOINFORMATICS (2007)
Machine learning in bioinformatics
Pedro Larranaga et al.
BRIEFINGS IN BIOINFORMATICS (2006)
Solving the protein sequence metric problem
WR Atchley et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2005)
Support vector machines for predicting rRNA-, RNA-, and DNA-binding proteins from amino acid sequence
YD Cai et al.
BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS (2003)
分享论文
