Research about Deep Learning Application in Biology Published in bioRxiv
Deep learning is a set of machine learning algorithms that attempt to model high-level abstractions in input data using multiple non-linear transformations. Some of the major advances in deep learning have been made in speech recognition , computer vision, and natural language processing. Considering that data volume is growing exponentially, deep learning is becoming increasingly important in the predictive analysis of big data (Li et. al, 2016). I have tried to collect and curate some publications form bioRxiv that related to the deep learning application in biology, and the results were listed here. Please enjoy it!
Last updated: May 17, 2020
Source : bioRxiv
| No. | Year | Title | URL |
|---|---|---|---|
| 1 | 2020 | Polishing Copy Number Variant Calls on Exome Sequencing Data via Deep Learning | View |
| 2 | 2020 | JavaDL: a Java-based Deep Learning Tool to Predict Drug Responses | View |
| 3 | 2020 | Leverging Deep Learning to Simulate Coronavirus Spike proteins has the potential to predict future Zoonotic sequences | View |
| 4 | 2020 | Deep Learning Improves Macromolecules Localization and Identification in 3D Cellular Cryo-Electron Tomograms | View |
| 5 | 2020 | Predicting Tumor Cell Response to Synergistic Drug Combinations Using a Novel Simplified Deep Learning Model | View |
| 6 | 2020 | Interpretable Deep Learning for De Novo Design of Cell-Penetrating Abiotic Polymers | View |
| 7 | 2020 | On the Relation of Gene Essentiality to Intron Structure: A Computational and Deep Learning Approach | View |
| 8 | 2020 | DeeReCT-APA: Prediction of Alternative Polyadenylation Site Usage Through Deep Learning | View |
| 9 | 2020 | Deep Learning Enable Untargeted Metabolite Extraction from High Throughput Coverage Data-Independent Acquisition | View |
| 10 | 2020 | Accurate Identification of SARS-CoV-2 from Viral Genome Sequences using Deep Learning | View |
| 11 | 2020 | Phenotype Prediction using a Tensor Representation and Deep Learning from Data Independent Acquisition Mass Spectrometry | View |
| 12 | 2020 | Predicting Endometrial Cancer Subtypes and Molecular Features from Histopathology Images Using Multi-resolution Deep Learning Models | View |
| 13 | 2020 | Predicting and Visualizing STK11 Mutation in Lung Adenocarcinoma Histopathology Slides Using Deep Learning | View |
| 14 | 2019 | Deep Docking - a Deep Learning Approach for Virtual Screening of Big Chemical Datasets | View |
| 15 | 2019 | A Deep Learning approach predicts the impact of point mutations in intronic flanking regions on micro-exon splicing definition | View |
| 16 | 2019 | DeepSleep: Fast and Accurate Delineation of Sleep Arousals at Millisecond Resolution by Deep Learning | View |
| 17 | 2019 | DeepSide: A Deep Learning Framework for Drug Side Effect Prediction | View |
| 18 | 2019 | UPCLASS: a Deep Learning-based Classifier for UniProtKB Entry Publications | View |
| 19 | 2019 | Modeling Abiotic Niches of Crops and Wild Ancestors Using Deep Learning: A Generalized Approach | View |
| 20 | 2019 | Linnaeus: Interpretable Deep Learning Classification of Single Cell Transcript Data | View |
| 21 | 2019 | Image-based Cell Phenotyping Using Deep Learning | View |
| 22 | 2019 | DAVI:Deep Learning Based Tool for Alignment and Single Nucleotide Variant identification | View |
| 23 | 2019 | Detection and Classification of Cardiac Arrhythmias by a Challenge-Best Deep Learning Neural Network Model | View |
| 24 | 2019 | DeepCLIP: Predicting the effect of mutations on protein-RNA binding with Deep Learning | View |
| 25 | 2019 | NuSeT: A Deep Learning Tool for Reliably Separating and Analyzing Crowded Cells | View |
| 26 | 2019 | DeepMF: Deciphering the Latent Patterns in Omics Profiles with a Deep Learning Method | View |
| 27 | 2019 | Deep Learning-Based Point-Scanning Super-Resolution Imaging | View |
| 28 | 2019 | Intercostal Space Prediction Using Deep Learning In Fully Endoscopic Mitral Valve Surgery | View |
| 29 | 2019 | RootNav 2 | View |
| 30 | 2019 | DeepPrime2Sec: Deep Learning for Protein Secondary Structure Prediction from the Primary Sequences | View |
| 31 | 2019 | Janggu: Deep Learning for Genomics | View |
| 32 | 2019 | MethylNet: A Modular Deep Learning Approach to Methylation Prediction | View |
| 33 | 2019 | A Simple Deep Learning Approach for Detecting Duplications and Deletions in Next-Generation Sequencing Data | View |
| 34 | 2019 | CRISPRpred(SEQ): a sequence based tool for sgRNA on target activity prediction [(almost) beating Deep Learning pipelines by | View |
| 35 | 2019 | An Accurate Bioinformatics Tool For Anti-Cancer Peptide Generation Through Deep Learning Omics | View |
| 36 | 2019 | Feasibility of Automated Deep Learning Design for Medical Image Classification by Healthcare Professionals with Limited Coding Experience | View |
| 37 | 2019 | Detecting Novel Sequence Signals in Targeting Peptides Using Deep Learning | View |
| 38 | 2019 | Deep Learning Approach to Identifying Breast Cancer Subtypes Using High-Dimensional Genomic Data | View |
| 39 | 2019 | Using Deep Learning to Annotate the Protein Universe | View |
| 40 | 2019 | Modeling the Language of Life - Deep Learning Protein Sequences | View |
| 41 | 2019 | Deep Learning with Multimodal Representation for Pancancer Prognosis Prediction | View |
| 42 | 2019 | Deep Learning on Chaos Game Representation for Proteins | View |
| 43 | 2019 | Viral host prediction with Deep Learning | View |
| 44 | 2019 | Deep Learning of Representations for Transcriptomics-based Phenotype Prediction | View |
| 45 | 2019 | Recognition Genes Which Are Relative With Cancer By Deep Learning Algorithm | View |
| 46 | 2019 | MuStARD: Deep Learning for intra- and inter-species scanning of functional genomic patterns | View |
| 47 | 2019 | E2M: A Deep Learning Framework for Associating Combinatorial Methylation Patterns with Gene Expression | View |
| 48 | 2018 | A Deep Learning Genome-Mining Strategy Improves Biosynthetic Gene Cluster Prediction | View |
| 49 | 2018 | Predicting Methylation from Sequence and Gene Expression Using Deep Learning with Attention | View |
| 50 | 2018 | DeepCapTail: A Deep Learning Framework to Predict Capsid and Tail Proteins of Phage Genomes | View |
| 51 | 2018 | Distance-based Protein Folding Powered by Deep Learning | View |
| 52 | 2018 | Root Anatomy based on Root Cross-Section Image Analysis with Deep Learning | View |
| 53 | 2018 | Systematic Prediction of Regulatory Motifs from Human ChIP-Sequencing Data Based on a Deep Learning Framework | View |
| 54 | 2018 | Tensorflow Based Deep Learning Model and Snakemake Workflow for Peptide-Protein Binding Predictions | View |
| 55 | 2018 | Taxonomic Classification of Ants (Formicidae) from Images using Deep Learning | View |
| 56 | 2018 | Robust Automated Assessment of Human Blastocyst Quality using Deep Learning | View |
| 57 | 2018 | rawMSA: Proper Deep Learning makes protein sequence profiles and feature extraction obsolete | View |
| 58 | 2018 | Skin Lesion Classification Via Combining Deep Learning Features and Clinical Criteria Representations | View |
| 59 | 2018 | Lesion Attributes Segmentation for Melanoma Detection with Deep Learning | View |
| 60 | 2018 | On the Depth of Deep Learning Models for Splice Site Identification | View |
| 61 | 2018 | Deep Learning Based Tumor Type Classification Using GeneExpression Data | View |
| 62 | 2018 | Taking a Dive: Experiments in Deep Learning for Automatic Ontology-based Annotation of Scientific Literature | View |
| 63 | 2018 | DeepAffinity: Interpretable Deep Learning of Compound Protein Affinity through Unified Recurrent and Convolutional Neural Networks | View |
| 64 | 2018 | Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images | View |
| 65 | 2018 | A Deep Learning Approach for Learning Intrinsic Protein-RNA Binding Preferences | View |
| 66 | 2018 | Deep Learning meets Topology-preserving Active Contours: towards scalable quantitative histology of cortical cytoarchitecture | View |
| 67 | 2018 | Forecasting Future Humphrey Visual Fields Using Deep Learning | View |
| 68 | 2018 | Deep Learning Global Glomerulosclerosis in Transplant Kidney Frozen Sections | View |
| 69 | 2018 | Deep Learning Predicts Tuberculosis Drug Resistance Status from Whole-Genome Sequencing Data | View |
| 70 | 2018 | Enhancer Identification using Transfer and Adversarial Deep Learning of DNA Sequences | View |
| 71 | 2018 | COSSMO: Predicting Competitive Alternative Splice Site Selection using Deep Learning | View |
| 72 | 2018 | DeepNeuron: An Open Deep Learning Toolbox for Neuron Tracing | View |
| 73 | 2018 | Link Prediction through Deep Learning | View |
| 74 | 2018 | Deep Learning Based Proarrhythmia Analysis Using Field Potentials Recorded from Human Pluripotent Stem Cells Derived Cardiomyocytes | View |
| 75 | 2018 | Breast Cancer Histopathological Image Classification: A Deep Learning Approach | View |
| 76 | 2017 | DeepGS: Predicting phenotypes from genotypes using Deep Learning | View |
| 77 | 2017 | Edge Detection of Cryptic Lamellipodia Assisted by Deep Learning | View |
| 78 | 2017 | Mapping Patient Trajectories using Longitudinal Extraction and Deep Learning in the | View |
| 79 | 2017 | A Deep Learning Model for Predicting Tumor Suppressor Genes and Oncogenes from PDB Structure | View |
| 80 | 2017 | Beyond Homology Transfer: Deep Learning for Automated Annotation of Proteins | View |
| 81 | 2017 | Separable Fully Connected Layers Improve Deep Learning Models For Genomics | View |
| 82 | 2017 | Opportunities And Obstacles For Deep Learning In Biology And Medicine | View |
| 83 | 2017 | Generalising Better: Applying Deep Learning To Integrate Deleteriousness Prediction Scores | View |
| 84 | 2017 | Deep Learning based multi-omics integration robustly predicts survival in liver cancer | View |
| 85 | 2016 | Adaptive Somatic Mutations Calls with Deep Learning and Semi-Simulated Data | View |
| 86 | 2016 | Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model | View |
| 87 | 2016 | Deep Learning and Association Rule Mining for Predicting Drug Response in Cancer | View |
| 88 | 2016 | Deep Learning-based Pipeline to Recognize Alzheimer’s Disease using fMRI Data | View |