| P50 | author | Zhu-Hong You | Q87953399 |
| P2093 | author name string | Li Cheng | |
| | Xiao Li | |
| | Xi Zhou | |
| | Tong-Hai Jiang | |
| | Zhan-Heng Chen | |
| | Hai-Cheng Yi | |
| P2860 | cites work | Studies on anticancer activities of antimicrobial peptides | Q24650885 |
| | Long short-term memory | Q24805158 |
| | Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008 | Q27860674 |
| | Prediction of FAD binding sites in electron transport proteins according to efficient radial basis function networks and significant amino acid pairs | Q28828373 |
| | Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences | Q29547172 |
| | Cancer drug resistance: an evolving paradigm | Q29616694 |
| | IPMiner: hidden ncRNA-protein interaction sequential pattern mining with stacked autoencoder for accurate computational prediction | Q30376610 |
| | The use of therapeutic peptides to target and to kill cancer cells | Q30418414 |
| | A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties of functional gene network | Q33614389 |
| | Using manifold embedding for assessing and predicting protein interactions from high-throughput experimental data | Q33684509 |
| | Learning to forget: continual prediction with LSTM. | Q34060132 |
| | Predicting RNA-protein interactions using only sequence information | Q34109026 |
| | Phase I study of NGR-hTNF, a selective vascular targeting agent, in combination with cisplatin in refractory solid tumors | Q34163587 |
| | Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes | Q34340793 |
| | Predicting anticancer peptides with Chou's pseudo amino acid composition and investigating their mutagenicity via Ames test | Q34371112 |
| | RPI-Pred: predicting ncRNA-protein interaction using sequence and structural information | Q35089055 |
| | Incorporating efficient radial basis function networks and significant amino acid pairs for predicting GTP binding sites in transport proteins. | Q36268702 |
| | PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction | Q36322043 |
| | Cancer treatment using peptides: current therapies and future prospects | Q36515785 |
| | Cationic antimicrobial peptides as novel cytotoxic agents for cancer treatment | Q36543072 |
| | iACP: a sequence-based tool for identifying anticancer peptides | Q37086151 |
| | From antimicrobial to anticancer peptides. A review | Q37207728 |
| | Promises of apoptosis-inducing peptides in cancer therapeutics | Q37862306 |
| | Alpha-helical cationic anticancer peptides: a promising candidate for novel anticancer drugs. | Q38266323 |
| | Predicting human protein subcellular locations by the ensemble of multiple predictors via protein-protein interaction network with edge clustering coefficients. | Q38400766 |
| | Subcellular localization prediction for human internal and organelle membrane proteins with projected gene ontology scores | Q38495154 |
| | CancerPPD: a database of anticancer peptides and proteins. | Q38951733 |
| | Highly Efficient Framework for Predicting Interactions Between Proteins | Q39004588 |
| | In silico approaches for designing highly effective cell penetrating peptides. | Q39447373 |
| | iRNA-PseU: Identifying RNA pseudouridine sites | Q42319212 |
| | Accurate Identification of Cancerlectins through Hybrid Machine Learning Technology | Q42402244 |
| | Using ensemble classifier to identify membrane protein types | Q44727018 |
| | Incorporating deep learning with convolutional neural networks and position specific scoring matrices for identifying electron transport proteins. | Q45946974 |
| | Inverse-Free Extreme Learning Machine With Optimal Information Updating. | Q45954213 |
| | Cancer statistics, 2018. | Q47191906 |
| | Detection of Interactions between Proteins by Using Legendre Moments Descriptor to Extract Discriminatory Information Embedded in PSSM. | Q47935698 |
| | In silico models for designing and discovering novel anticancer peptides. | Q51150438 |
| | iRNA-3typeA: Identifying Three Types of Modification at RNA's Adenosine Sites. | Q55162021 |
| | A Deep Learning Framework for Robust and Accurate Prediction of ncRNA-Protein Interactions Using Evolutionary Information. | Q55280238 |
| | Combining High Speed ELM Learning with a Deep Convolutional Neural Network Feature Encoding for Predicting Protein-RNA Interactions | Q57184683 |
| | ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides | Q62729907 |
| | LMTRDA: Using logistic model tree to predict MiRNA-disease associations by fusing multi-source information of sequences and similarities | Q64114766 |
| | Prediction of Self-Interacting Proteins from Protein Sequence Information Based on Random Projection Model and Fast Fourier Transform | Q64119765 |
| | Assessment of the biological and pharmacological effects of the alpha nu beta3 and alpha nu beta5 integrin receptor antagonist, cilengitide (EMD 121974), in patients with advanced solid tumors | Q80785300 |
| | Peptide-based drug design: here and now | Q81826430 |
| | Classifying the molecular functions of Rab GTPases in membrane trafficking using deep convolutional neural networks | Q89122763 |
| | Deep-Resp-Forest: A deep forest model to predict anti-cancer drug response | Q91649567 |
| | iEnhancer-5Step: Identifying enhancers using hidden information of DNA sequences via Chou's 5-step rule and word embedding | Q92043884 |
| | Gene2vec: gene subsequence embedding for prediction of mammalian N 6-methyladenosine sites from mRNA | Q93051882 |
| P4510 | describes a project that uses | scikit-learn | Q1026367 |
| P921 | main subject | deep learning | Q197536 |
| | long short-term memory | Q6673524 |
| P304 | page(s) | 1-9 | |
| P577 | publication date | 2019-05-10 | |
| P1433 | published in | Molecular Therapy. Nucleic acids | Q27724110 |
| P1476 | title | ACP-DL: A Deep Learning Long Short-Term Memory Model to Predict Anticancer Peptides Using High-Efficiency Feature Representation | |
| P478 | volume | 17 | |