Computational Biology and Bioinformatics Research using Neural Networks Published in Nature
Computational biology, a biology branch involving the use of computers and computer science to understand and model the structures and processes of life. It involves the use of computational methods (e.g. algorithms) for the representation and simulation of biological systems, as well as for the interpretation of experimental data, often on a very large scale. (David B. Searls, accessed May 22, 2020). I have tried to collect and curate some publications that related to the computational biology and bioinformatics using neural networks and the results were listed here. Please enjoy it!
Last updated: May 23, 2020
Source : Nature
| No. | Year | Title | URL |
|---|---|---|---|
| 1 | 2020 | CNN-Peaks: ChIP-Seq peak detection pipeline using convolutional neural networks that imitate human visual inspection | Scientific Reports 10, 1–12 |
| 2 | 2020 | Nonlinear neural network dynamics accounts for human confidence in a sequence of perceptual decisions | Scientific Reports 10, 1–16 |
| 3 | 2020 | Convolutional neural networks explain tuning properties of anterior, but not middle, face-processing areas in macaque inferotemporal cortex | Communications Biology 3, 1–14 |
| 4 | 2020 | Application of a convolutional neural network for predicting the occurrence of ventricular tachyarrhythmia using heart rate variability features | Scientific Reports 10, 1–7 |
| 5 | 2020 | A neural network trained for prediction mimics diverse features of biological neurons and perception | Nature Machine Intelligence 2, 210–219 |
| 6 | 2020 | Training instance segmentation neural network with synthetic datasets for crop seed phenotyping | Communications Biology 3, 1–12 |
| 7 | 2020 | Exploring the limit of using a deep neural network on pileup data for germline variant calling | Nature Machine Intelligence 2, 220–227 |
| 8 | 2020 | Convolutional neural network for classification of two-dimensional array images generated from clinical information may support diagnosis of rheumatoid arthritis | Scientific Reports 10, 1–7 |
| 9 | 2020 | Siamese neural networks for continuous disease severity evaluation and change detection in medical imaging | npj Digital Medicine 3, 1–9 |
| 10 | 2020 | SAEROF: an ensemble approach for large-scale drug-disease association prediction by incorporating rotation forest and sparse autoencoder deep neural network | Scientific Reports 10, 1–11 |
| 11 | 2020 | An empirical comparison of neural networks and machine learning algorithms for EEG gait decoding | Scientific Reports 10, 1–17 |
| 12 | 2020 | Separability and geometry of object manifolds in deep neural networks | Nature Communications 11, 1–13 |
| 13 | 2020 | Surface protein imputation from single cell transcriptomes by deep neural networks | Nature Communications 11, 1–10 |
| 14 | 2020 | RefDNN: a reference drug based neural network for more accurate prediction of anticancer drug resistance | Scientific Reports 10, 1–11 |
| 15 | 2020 | Single-cell dispensing and ‘real-time’ cell classification using convolutional neural networks for higher efficiency in single-cell cloning | Scientific Reports 10, 1–9 |
| 16 | 2020 | Brian2GeNN: accelerating spiking neural network simulations with graphics hardware | Scientific Reports 10, 1–12 |
| 17 | 2020 | Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks | Nature Medicine 26, 52–58 |
| 18 | 2020 | Deep-Channel uses deep neural networks to detect single-molecule events from patch-clamp data | Communications Biology 3, 1–10 |
| 19 | 2019 | Prediction of IDH and TERT promoter mutations in low-grade glioma from magnetic resonance images using a convolutional neural network | Scientific Reports 9, 1–8 |
| 20 | 2019 | A Long Short-Term Memory neural network for the detection of epileptiform spikes and high frequency oscillations | Scientific Reports 9, 1–10 |
| 21 | 2019 | Deep learning and taphonomy: high accuracy in the classification of cut marks made on fleshed and defleshed bones using convolutional neural networks | Scientific Reports 9, 1–12 |
| 22 | 2019 | RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning | Nature Communications 10, 1–13 |
| 23 | 2019 | DeepSynth: Three-dimensional nuclear segmentation of biological images using neural networks trained with synthetic data | Scientific Reports 9, 1–15 |
| 24 | 2019 | Deep-UV excitation fluorescence microscopy for detection of lymph node metastasis using deep neural network | Scientific Reports 9, 1–12 |
| 25 | 2019 | CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network | Scientific Reports 9, 1–9 |
| 26 | 2019 | DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput | Nature Methods 17, 41–44 |
| 27 | 2019 | Human-level recognition of blast cells in acute myeloid leukaemia with convolutional neural networks | Nature Machine Intelligence 1, 538–544 |
| 28 | 2019 | Exploring single-cell data with deep multitasking neural networks | Nature Methods 16, 1139–1145 |
| 29 | 2019 | Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs | Nature Methods 16, 1153–1160 |
| 30 | 2019 | Massive computational acceleration by using neural networks to emulate mechanism-based biological models | Nature Communications 10, 1–9 |
| 31 | 2019 | Improved fragment sampling for ab initio protein structure prediction using deep neural networks | Nature Machine Intelligence 1, 347–355 |
| 32 | 2019 | A critique of pure learning and what artificial neural networks can learn from animal brains | Nature Communications 10, 1–7 |
| 33 | 2019 | DeepInsight: A methodology to transform a non-image data to an image for convolution neural network architecture | Scientific Reports 9, 1–7 |
| 34 | 2019 | Wx: a neural network-based feature selection algorithm for transcriptomic data | Scientific Reports 9, 1–9 |
| 35 | 2019 | Learning cellular morphology with neural networks | Nature Communications 10, 1–12 |
| 36 | 2019 | Detection of DNA base modifications by deep recurrent neural network on Oxford Nanopore sequencing data | Nature Communications 10, 1–11 |
| 37 | 2019 | Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction | Nature Machine Intelligence 1, 269–276 |
| 38 | 2019 | Modeling in-vivo protein-DNA binding by combining multiple-instance learning with a hybrid deep neural network | Scientific Reports 9, 1–12 |
| 39 | 2019 | Interpretable classification of Alzheimer’s disease pathologies with a convolutional neural network pipeline | Nature Communications 10, 1–14 |
| 40 | 2019 | Leveraging implicit knowledge in neural networks for functional dissection and engineering of proteins | Nature Machine Intelligence 1, 225–235 |
| 41 | 2019 | A convolutional neural network-based system to prevent patient misidentification in FDG-PET examinations | Scientific Reports 9, 1–9 |
| 42 | 2019 | Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks | Scientific Reports 9, 1–8 |
| 43 | 2019 | Deep convolutional neural networks for accurate somatic mutation detection | Nature Communications 10, 1–10 |
| 44 | 2019 | Characterisation of nonlinear receptive fields of visual neurons by convolutional neural network | Scientific Reports 9, 1–17 |
| 45 | 2019 | A multi-task convolutional deep neural network for variant calling in single molecule sequencing | Nature Communications 10, 1–11 |
| 46 | 2019 | Robust mouse tracking in complex environments using neural networks | Communications Biology 2, 1–11 |
| 47 | 2019 | Training deep neural networks for binary communication with the Whetstone method | Nature Machine Intelligence 1, 86–94 |
| 48 | 2019 | DeepSeqPan, a novel deep convolutional neural network model for pan-specific class I HLA-peptide binding affinity prediction | Scientific Reports 9, 1–10 |
| 49 | 2019 | Convolutional neural networks can accurately distinguish four histologic growth patterns of lung adenocarcinoma in digital slides | Scientific Reports 9, 1–12 |
| 50 | 2019 | Characterization of deep neural network features by decodability from human brain activity | Scientific Data 6, 1–12 |
| 51 | 2019 | Designing neural networks through neuroevolution | Nature Machine Intelligence 1, 24–35 |
| 52 | 2019 | Task representations in neural networks trained to perform many cognitive tasks | Nature Neuroscience 22, 297–306 |
| 53 | 2019 | An integrated iterative annotation technique for easing neural network training in medical image analysis | Nature Machine Intelligence 1, 112–119 |
| 54 | 2019 | Encoding information into autonomously bursting neural network with pairs of time-delayed pulses | Scientific Reports 9, 1–11 |
| 55 | 2019 | Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network | Nature Medicine 25, 65–69 |
| 56 | 2019 | SignalP 5.0 improves signal peptide predictions using deep neural networks | Nature Biotechnology 37, 420–423 |
| 57 | 2019 | Critical synchronization and 1/f noise in inhibitory/excitatory rich-club neural networks | Scientific Reports 9, 1–13 |
| 58 | 2018 | Fast animal pose estimation using deep neural networks | Nature Methods 16, 117–125 |
| 59 | 2018 | A neural network based model effectively predicts enhancers from clinical ATAC-seq samples | Scientific Reports 8, 1–15 |
| 60 | 2018 | In vitro neural networks minimise variational free energy | Scientific Reports 8, 1–14 |
| 61 | 2018 | A deep convolutional neural network approach for astrocyte detection | Scientific Reports 8, 1–7 |
| 62 | 2018 | Activations of deep convolutional neural networks are aligned with gamma band activity of human visual cortex | Communications Biology 1, 1–12 |
| 63 | 2018 | Meeting brain–computer interface user performance expectations using a deep neural network decoding framework | Nature Medicine 24, 1669–1676 |
| 64 | 2018 | Performance of the deep convolutional neural network based magnetic resonance image scoring algorithm for differentiating between tuberculous and pyogenic spondylitis | Scientific Reports 8, 1–10 |
| 65 | 2018 | A universal SNP and small-indel variant caller using deep neural networks | Nature Biotechnology 36, 983–987 |
| 66 | 2018 | A memristive plasticity model of voltage-based STDP suitable for recurrent bidirectional neural networks in the hippocampus | Scientific Reports 8, 1–12 |
| 67 | 2018 | Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science | Nature Communications 9, 1–12 |
| 68 | 2018 | Warfarin maintenance dose Prediction for Patients undergoing heart valve replacement— a hybrid model with genetic algorithm and Back-Propagation neural network | Scientific Reports 8, 1–11 |
| 69 | 2018 | E-I balance emerges naturally from continuous Hebbian learning in autonomous neural networks | Scientific Reports 8, 1–12 |
| 70 | 2018 | Accurate identification of RNA editing sites from primitive sequence with deep neural networks | Scientific Reports 8, 1–12 |
| 71 | 2018 | Directional preparation of anticoagulant-active sulfated polysaccharides from Enteromorpha prolifera using artificial neural networks | Scientific Reports 8, 1–9 |
| 72 | 2017 | Supervised learning in spiking neural networks with FORCE training | Nature Communications 8, 1–15 |
| 73 | 2018 | Enhancing Hi-C data resolution with deep convolutional neural network HiCPlus | Nature Communications 9, 1–9 |
| 74 | 2017 | Leveraging uncertainty information from deep neural networks for disease detection | Scientific Reports 7, 1–14 |
| 75 | 2018 | Feasibility and resolution limits of opto-magnetic imaging of neural network activity in brain slices using color centers in diamond | Scientific Reports 8, 1–14 |
| 76 | 2017 | Convolutional neural networks for automated annotation of cellular cryo-electron tomograms | Nature Methods 14, 983–985 |
| 77 | 2017 | Efficient probabilistic inference in generic neural networks trained with non-probabilistic feedback | Nature Communications 8, 1–14 |
| 78 | 2017 | Kinase inhibitor screening using artificial neural networks and engineered cardiac biowires | Scientific Reports 7, 1–12 |
| 79 | 2017 | Zebrafish tracking using convolutional neural networks | Scientific Reports 7, 1–11 |
| 80 | 2017 | Dermatologist-level classification of skin cancer with deep neural networks | Nature 542, 115–118 |
| 81 | 2016 | Can computational efficiency alone drive the evolution of modularity in neural networks? | Scientific Reports 6, 1–10 |
| 82 | 2016 | Hybrid computing using a neural network with dynamic external memory | Nature 538, 471–476 |
| 83 | 2017 | A spiking neural network model of 3D perception for event-based neuromorphic stereo vision systems | Scientific Reports 7, 1–12 |
| 84 | 2016 | Physical connections between different SSVEP neural networks | Scientific Reports 6, 1–9 |
| 85 | 2015 | A neural network that finds a naturalistic solution for the production of muscle activity | Nature Neuroscience 18, 1025–1033 |
| 86 | 2015 | Analyzing animal behavior via classifying each video frame using convolutional neural networks | Scientific Reports 5, 1–13 |
| 87 | 2013 | Self-organized noise resistance of oscillatory neural networks with spike timing-dependent plasticity | Scientific Reports 3, 1–6 |
| 88 | 2013 | Two-way communication with neural networks in vivo using focused light | Nature Protocols 8, 1184–1203 |
| 89 | 2013 | Artificial neural networks and prostate cancer—tools for diagnosis and management | Nature Reviews Urology 10, 174–182 |
| 90 | 2013 | Robust timing and motor patterns by taming chaos in recurrent neural networks | Nature Neuroscience 16, 925–933 |
| 91 | 2015 | A closer look at the apparent correlation of structural and functional connectivity in excitable neural networks | Scientific Reports 5, 1–5 |