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Nowadays, researchers are using machine learning to discover potentially valuable knowledge in medical data more than ever before. In electronics, the BOM represents the list of components used on the printed wiring board or printed circuit board. From an international perspective, the United States, China, South Korea and England are the core strengths, and the research institutions are mainly China and the United States. Xiong, H. Prediction of potential drug targets based on simple sequence properties. Social network usage by consumers is highly diverse: The monitoring host controls a variety of sensors to monitor noise, rain, burglary, carbon monoxide concentration, etc.
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Press Release. Go With A Leader! Smith, J. Transforming computational drug discovery with machine learning and AI. ACS Med. Lenselink, E. Beyond the hype: Gaulton, A. Massively multitask networks for drug discovery. Preprint at arXiv https: Gutlein, M.
Filtered circular fingerprints improve either prediction or runtime performance while retaining interpretability. Mayr, A. This research paper describes the methodology being used by the winners of almost all categories of the Tox21 Challenge. Keiser, M. Relating protein pharmacology by ligand chemistry. Preuer, K. Unterthiner, T. Toxicity prediction using deep learning.
Li, B. Development of a drug-response modeling framework to identify cell line derived translational biomarkers that can predict treatment outcome to erlotinib or sorafenib. In this paper, a translational predictive biomarker is used to demonstrate that predictive models can be generated from preclinical training data sets and then be applied to clinical patient samples to stratify patients, infer the mechanism of action of a drug and select appropriate disease indications.
Bridging the translational innovation gap through good biomarker practice.
Kraus, V. Biomarkers as drug development tools: Shi, L. Zhan, F. The molecular classification of multiple myeloma. Blood , — Shaughnessy, J. A validated gene expression model of high-risk multiple myeloma is defined by deregulated expression of genes mapping to chromosome 1. High-risk myeloma: Decaux, O. Prediction of survival in multiple myeloma based on gene expression profiles reveals cell cycle and chromosomal instability signatures in high-risk patients and hyperdiploid signatures in low-risk patients: Mulligan, G.
Gene expression profiling and correlation with outcome in clinical trials of the proteasome inhibitor bortezomib. Costello, J. A community effort to assess and improve drug sensitivity prediction algorithms.
This paper is an effort to collect and objectively evaluate various ML approaches by teams around the world on multi-omics data sets and various compounds. The data sets and results are continuously used as benchmarks for new method developments and validation.
Bunte, K. Sparse group factor analysis for biclustering of multiple data sources. Bioinformatics 32 , — Huang, C. Open source machine-learning algorithms for the prediction of optimal cancer drug therapies.
Improving drug sensitivity prediction using different types of data. CPT Pharmacometrics Syst. Kim, E. Boyiadzis, M. Significance and implications of FDA approval of pembrolizumab for biomarker-defined disease. Cancer 6 , 35 Tasaki, S. Multi-omics monitoring of drug response in rheumatoid arthritis in pursuit of molecular remission. This work identifies molecular signatures that are resistant to drug treatments and illustrates a multi-omics approach to understanding drug response.
A machine-learning heuristic to improve gene score prediction of polygenic traits. Khera, A. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Ding, J. Interpretable dimensionality reduction of single cell transcriptome data with deep generative models. Rashid, S. Project Dhaka: Preprint at bioRxiv https: Wang, D.
Genomics Proteomics Bioinformatics 16 , — Pierson, E. Genome Biol. Wang, B. Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning. Methods 14 , Tan, J. ADAGE-based integration of publicly available Pseudomonas aeruginosa gene expression data with denoising autoencoders illuminates microbe-host interactions.
Way, G. Extracting a biologically relevant latent space from cancer transcriptomes with variational autoencoders. Casanova, R. Morphoproteomic characterization of lung squamous cell carcinoma fragmentation, a histological marker of increased tumor invasiveness. Cancer Res. Nirschl, J. Angermueller, C. Deep learning for computational biology. Finnegan, A. Maximum entropy methods for extracting the learned features of deep neural networks.
Hutson, M. Artificial intelligence faces reproducibility crisis. Science , — Veltri, R. Quantitative nuclear grade QNG: Beck, A.
Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Lee, G. Nuclear shape and architecture in benign fields predict biochemical recurrence in prostate cancer patients following radical prostatectomy: Focus 3 , — Lu, C. Mani, N. Quantitative assessment of the spatial heterogeneity of tumor-infiltrating lymphocytes in breast cancer. Breast Cancer Res. Giraldo, N. Janowczyk, A. Deep learning for digital pathology image analysis: This article is the first comprehensive review of DL in the context of digital pathology images.
The paper also systematically explains and presents approaches for training and validating DL classifiers for a number of image-based problems in digital pathology, including cell detection, segmentation and tissue classification. Sharma, H. Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology. Imaging Graph. Korbar, B. Deep learning for classification of colorectal polyps on whole-slide images.
Bychkov, D. Deep learning based tissue analysis predicts outcome in colorectal cancer. Cruz-Roa, A. Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent. This is one of the first papers to apply DL to identify regions of breast cancer on digital pathology images and shows that the algorithmic approach outperforms breast cancer pathologists. Romo-Bucheli, D. This article applies DL to identify the presence and location of tubules in breast pathology images and subsequently demonstrates that the number of detected tubules correlates with the risk assessments of breast cancer via a genomic test.
It is one of the first papers to show how DL can be used to establish genotype—phenotype associations. A deep learning based strategy for identifying and associating mitotic activity with gene expression derived risk categories in estrogen receptor positive breast cancers. Cytometry A 91 , — Saltz, J. Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images.
Cell Rep. This large-scale study utilizes DL to identify lymphocytes across all images and relate spatial characteristics of lymphocytes to molecular assessments. Corredor, G. Spatial architecture and arrangement of tumor-infiltrating lymphocytes for predicting likelihood of recurrence in early-stage non-small cell lung cancer. In this paper, the spatial arrangement, and not just the density, of tumour-infiltrating lymphocytes in early-stage lung cancer pathology images is shown to be prognostic of recurrence.
A comprehensive comparison is provided, showing that computer-extracted features of spatial arrangement of tumour-infiltrating lymphocytes are more prognostic than manual pathologist enumeration of tumour-infiltrating lymphocyte density. Cohen, O.
Coudray, N. Classification and mutation prediction from non—small cell lung cancer histopathology images using deep learning. Turkki, R. Antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples. Norgeot, B. Esteva, A. A guide to deep learning in healthcare. Yang, Z. Clinical assistant diagnosis for electronic medical record based on convolutional neural network. Steele, A.
Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease. Mohr, D. Personal sensing: Gkotsis, G. Characterisation of mental health conditions in social media using Informed Deep Learning. Koscielny, S. Why most gene expression signatures of tumors have not been useful in the clinic. Odell, S. The art of curation at a biological database: Plant Biol. Download references. The authors thank E. Birney and E.
Papa for helpful comments, M. Segler for contributing to the small-molecule optimization subsection and A. Janowczyk for providing the pathology images in Figure 4. Correspondence to Jessica Vamathevan. Processors designed to accelerate the rendering of graphics and that can handle tens of thousands of operations per cycle.
Processors designed to solve every computational problem in a general fashion and that can handle tens of operations per cycle. The cache and memory are designed to be optimal for any general programming problem. Co-processors manufactured by Google that are designed to accelerate deep learning tasks developed using TensorFlow a programming framework and can handle up to , operations per cycle.