AI & Deep Learning Papers, Links, Articles etc
This page is to collect and save scientific citations and useful links.
At this time the research papers of interest are (1) quality reviews; (2) use cases in AI and ML use in drug discovery; (3) high-profile use cases of AI and ML in medical diagnostics; (4) regulatory and ethical aspects of AI application in drug discovery and medicine.
AI-powered drug discovery captures pharma interest https://www.nature.com/nbt/journal/v35/n7/full/nbt0717-604.html
Great intro to ML and Big Data Demystifying Big Data and Machine Learning for Healthcare https://www.crcpress.com/Demystifying-Big-Data-and-Machine-Learning-for-Healthcare/Natarajan-Frenzel-Smaltz/p/book/9781138032637
The Next Era: Deep Learning in Pharmaceutical Research https://www.ncbi.nlm.nih.gov/pubmed/27599991 - Ekins Nov 2016
https://www.pharma-iq.com/informatics/articles/is-big-pharma-really-on-cusp-of-ai-shake-out-0Review of AI June 2017 http://www.biorxiv.org/content/early/2017/05/28/142760
Github discussions is exellent https://github.com/greenelab/deep-review/issues
Excellent Blog from Bharath Ramsundar http://rbharath.github.io/what-cant-deep-learning-do/
IBM Watson A Reality Check for IBM’s AI Ambitions https://www.technologyreview.com/s/607965/a-reality-check-for-ibms-ai-ambitions/
June 2017 Next Generation AI algorithms need to make most of AI chips optimisation
Greg Diamos, Head of Systems Research at Baidu Silicon Valley AI
WSJ June 2017How AI Is Transforming Drug Creation https://www.wsj.com/articles/how-ai-is-transforming-drug-creation-1498442760
Beyond the Hype: Deep Neural Networks 2 Outperform Established Methods Using 3 A ChEMBL Bioactivity Benchmark Set http://www.biorxiv.org/content/biorxiv/early/2017/07/28/168914.full.pdf
WSJ Article March 2017 - broader business space review https://www.wsj.com/articles/how-ai-is-transforming-the-workplace-1489371060
Top 10 Recommendations for the AI Field in 2017 Oct 2017
The challenge of AI technology adoption in healthcare https://www.linkedin.com/pulse/challenge-ai-technology-adoption-healthcare-jay-chyung-md-phd/
Oct 2017 Challenages of AI adoption in Healthcare https://www.linkedin.com/pulse/challenge-ai-technology-adoption-healthcare-jay-chyung-md-phd/?trackingId=Cpa346%2FuUurbWLcO%2FhUTxw%3D%3D
Jan 2017 First FDA Approved Cloud Based Deep learning https://www.forbes.com/sites/bernardmarr/2017/01/20/first-fda-approval-for-clinical-cloud-based-deep-learning-in-healthcare/#357cbfe1161c (*** added 2017)
Fundamental of Deep Learning https://www.analyticsvidhya.com/blog/2016/03/introduction-deep-learning-fundamentals-neural-networks/?utm_content=bufferf9225&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer
Drug Makers Guide to the Galaxy: Excentia and polypharmacology https://www.nature.com/news/the-drug-maker-s-guide-to-the-galaxy-1.22683
Data is Key ingredient for AI https://www.elsevier.com/connect/5-reasons-data-is-a-key-ingredient-for-ai-applications?sf175330640=1
HBR AI for the real world https://hbr.org/2018/01/artificial-intelligence-for-the-real-world
AI for Health and Healtcare report (Mitre Group) https://www.healthit.gov/sites/default/files/jsr-17-task-002_aiforhealthandhealthcare12122017.pdf
Deep Learning for Biology https://www.nature.com/articles/d41586-018-02174-z
An automated curation procedure for addressing chemical errors and inconsistencies in public datasets used in QSAR modelling https://www.ncbi.nlm.nih.gov/pubmed/27885862
The ELF Honest Data Broker: informatics enabling public–private collaboration in a precompetitive arena https://www.sciencedirect.com/science/article/pii/S1359644615004249?via%3Dihub
Feb 2018: AI Startups https://blog.benchsci.com/startups-using-artificial-intelligence-in-drug-discovery?lipi=urn%3Ali%3Apage%3Ad_flagship3_feed%3BmEHpBm1fQ%2BKkySB%2FSg8G%2Fw%3D%3D#step11
J. R. Soc. Interface 15: 20170387. http://dx.doi.org/10.1098/rsif.2017.0387
Computer system predicts products of chemical reactions: Machine learning approach could aid the design of industrial processes for drug manufacturing http://people.csail.mit.edu/tommi/papers/Connor_etal_ACS_2017.pdf
ANN used for Image analysis - Novartis NIBR https://www.novartis.com/stories/discovery/machine-learning-poised-accelerate-drug-discovery and the original publication in Bioinformatics: https://academic.oup.com/bioinformatics/article/33/13/2010/2997285?searchresult=1
The failure of IBM Watson: https://www.statnews.com/2018/07/25/ibm-watson-recommended-unsafe-incorrect-treatments/
An optimistic editorial from Nature references success of Wuxi NextCode: https://www.nature.com/articles/d41586-018-05267-x
AI interpretation of radiology images is hard, harder than initially anticipated: https://www.technologyreview.com/s/610552/google-x-ray-project-shows-ai-wont-replace-doctors-any-time-soon/
PLASTER, Nvidia's methodology for assessment of AI performance (PDF): https://images.nvidia.com/content/pdf/plaster-deep-learning-framework.pdf
UK national platform for AI? http://news.top-consultant.com//New-White-Paper-Calls-for-Government-wide-AI-Platform-19336.html
How Technology can tame scientific literature https://www.nature.com/articles/d41586-018-06617-5
Oncology use case: http://gate250.com/rk/2018FLAG_UsingArtificialIntelligence_Whitepaper_digital-Final.pdf
Rules of Machine Learning http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf - could be expanded
"Winner's Curse" - How to do better science in AI: https://openreview.net/pdf?id=rJWF0Fywf
A list of biomedical start-ups that claim AI as a core technology: https://blog.benchsci.com/startups-using-artificial-intelligence-in-drug-discovery
AI used to predict CRISPR DNA cuts; in the future, this may be used for gene therapy: http://www.bio-itworld.com/2018/11/08/machine-learning-predicts-how-dna-breaks-under-crispr.aspx
Augmenting Medicinal Chemist with data https://doi.org/10.1016/j.drudis.2018.03.011
Review of Forrester Wave views on AI and Automation https://www.forbes.com/sites/gilpress/2018/11/06/ai-and-automation-2019-predictions-from-forrester/#788550a54cb5 Nov 2018
Ethics in AI: Rabbi Jonathan Sacks explores how we should respond to the ways in which AI is transforming our world https://www.bbc.co.uk/sounds/play/b0bgrw3k
What it means to open AI’s black box? http://usblogs.pwc.com/emerging-technology/to-open-ai-black-box/
Drug Discovery Maps, a Machine Learning Model That Visualizes and Predicts Kinome–Inhibitor Interaction Landscapes https://pubs.acs.org/doi/full/10.1021/acs.jcim.8b00640 (*** added 2018)
Tuning artificial intelligence on the de novo design of natural-product-inspired retinoid X receptor modulators https://www.nature.com/articles/s42004-018-0068-1
Artificial intelligence and its potential in oncology https://doi.org/10.1016/j.artmed.2018.08.008
Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review, by Bernal, J., Artificial Intelligence In Medicine,
Transforming Computational Drug Discovery with Machine Learning and AI ACS Med. Chem. Lett. 2018, 9, 11, 1065-1069
AI in Pharma, by LEK Consulting: https://www.lek.com/insights/artificial-intelligence-life-sciences-formula-pharma-success-across-drug-lifecycle#.XAvu0zCNM7M.linkedin
Are Ontologies relevant in a Machine Learning-centric world? By Lee Harland, based on our own Boston AI workshop: https://www.scibite.com/are-ontologies-relevant-in-a-machine-learning-centric-world/
Computers turn neural signals into speech:http://science.sciencemag.org/content/363/6422/14?utm_campaign=wnews_sci_2019-01-03&et_rid=17144995&et_cid=2581434
How is automated text summarization done? https://arxiv.org/pdf/1707.02268.pdf
FREE ML courses (the title says 10, but the text contains links to over 30): https://www.kdnuggets.com/2018/12/10-more-free-must-see-courses-machine-learning-data-science.html
Eric Topol: High-performance medicine: the convergence of human and artificial intelligence
AI and Neural Net Summary in 21 pages:https://www.linkedin.com/feed/update/activity:6492067351771574272/ (*** added 01/2019)
Dark Secret at heart of AI - Black box https://www.technologyreview.com/s/604087/the-dark-secret-at-the-heart-of-ai/
Classification of ligand-binding pockets in proteins with a convolutional neural network: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006718 (but only works well for nucleotide and heme binding sites, so only in a couple of highly specialized cases)
Overfitting dangers with small datasets: https://www.chemistryworld.com/news/dispute-over-reaction-prediction-puts-machine-learnings-pitfalls-in-spotlight/3009912.article
Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks (Nature): https://www.nature.com/articles/s41598-019-40041-7.pdf
AI recognizes speech‐based markers for posttraumatic stress disorder in US veterans, with 89% classification rate: https://onlinelibrary.wiley.com/doi/abs/10.1002/da.22890
A machine learning approach for somatic mutation discovery for diagnostics: https://stm.sciencemag.org/content/10/457/eaar7939 and the full-length research article: https://stm.sciencemag.org/content/11/489/eaat6177?utm_source=STAT+Newsletters&utm_campaign=50dc0470e5-MR_COPY_01&utm_medium=email&utm_term=0_8cab1d7961-50dc0470e5-149702353
Learn the basics of statistics: https://www.statisticshowto.datasciencecentral.com/probability-and-statistics/ Well, not only the basics! Some advanced topics too.
On the interpretability of Neural Net models: https://appsilon.com/please-explain-black-box/
Predict risk of lung cancer by a Google AI model? Claimed 94% AUC, but refused to release the code: https://www.nature.com/articles/s41591-019-0447-x
What is the difference between AI, ML, and Deep Learning models? See here: https://www.edureka.co/blog/ai-vs-machine-learning-vs-deep-learning/ and here: https://www.geospatialworld.net/blogs/difference-between-ai%EF%BB%BF-machine-learning-and-deep-learning/ (*** added 05/2019)
Review: AI in Drug Design: https://www.mdpi.com/1420-3049/23/10/2520/htm
Is China way ahead of the West in AI use in medicine? See here (may require registration for full-length paper): https://www.statnews.com/2019/06/05/china-leapfrogging-us-using-ai-in-medicine/
ML Flow, a system for managing the AI model life cycle, and deployment to the cloud: https://mlflow.org/
An interesting lecture in the history of science. Bernard Widrow, a Stanford professor, talks about the Least Mean-Squares (LMS) adaptive algorithm used to train first neuron models. The device shown in the lecture dates back to 1959 and is actually an electro-mechanical switch box. It has parts that broke due to age, but still functions, and can be trained to tell apart letters of the alphabet. Part 1 and part 2.
Clinical validity and technical validity of AI methods in medicine: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6526255/#r10
In a review of 516 research papers in the diagnostic analysis of medical images with AI, only 31 (or 6%) performed external validation, and none used best practices in clinical trial design: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6389801/ The papers reviewed were published between Jan 1st and Aug 18th 2018.
FDA: Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) - Discussion Paper and Request for Feedback: https://www.regulations.gov/document?D=FDA-2019-N-1185-0001 (download the actual document in PDF format from this page)
Example of QSAR in 3D using Convolutional Neural Network: https://www.jstage.jst.go.jp/article/cpb/67/5/67_c18-00757/_html/-char/en
Nature Review: Technologies to watch in 2019: https://www.nature.com/articles/d41586-019-00218-6
Machine Learning for Medical Ultrasound: Status, Methods, and Future Opportunities. This review includes organ segmentation, an apparently hard topic. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5886811/
Predict Adverse Drug Reactions (ADRs) through drug-gene interactions: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5473874/ This review might be of interest in view of our recent drug repurposing datathon, where prediction of ADRs played a large role. (*** added 06/2019)
Privacy of patients and data donors in the age of genomics and big data mining: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1741-0
Automated de novo molecular design by hybrid machine intelligence and rule-driven chemical synthesis. By Alexander Button, Daniel Merk, Jan A. Hiss & Gisbert Schneider, Nature Machine Intelligence, volume 1, pages307–315 (2019)
Review: Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery: https://pubs.acs.org/doi/full/10.1021/acs.chemrev.8b00728
Machine and deep learning meet genome-scale metabolic modeling: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007084
Disease gene prediction for molecularly uncharacterized diseases: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007078
Not quite ML, but curious: Identifying determinants of persistent MRSA bacteremia using mathematical modeling: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007087
A popular piece on using AI to mine Pubmed data in an attempt to diagnose a rare disease: "How an AI expert took on his toughest project ever: writing code to save his son’s life", but what is really interesting, is that this project has a public github page, from which one can get one's own copy of the software
Artificial intelligence for assisting diagnostics and assessment of Parkinson's disease-A review. Main point is that after 48 completed studies, there is still not enough data.
Putting benchmarks in their rightful place: The heart of computational biology. Editorial
Ten simple rules for writing and sharing computational analyses in Jupyter Notebooks. Best practices
Three pitfalls to avoid in machine learning (Nature, Google) (*** added 07/2019)
Review: Deep learning in drug discovery: opportunities, challenges and future prospects
Learning Health System for Breast Cancer: Pilot Project Experience. "It is possible to extract, read, and combine data from the EHR to view the patient journey. The agreement between NLP and the gold standard was high, which supports validity."
WE-E-213CD-06: A Locally Adaptive, Intensity-Based Label Fusion Method for Multi- Atlas Auto-Segmentation. Comparison of proprietary anatomy segmentation methods for medical images.
Best practices of AI use in medicine: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6599029/
A collection of papers on use of AI and ML in diagnostic imaging: The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review; State-of-the-Art Deep Learning in Cardiovascular Image Analysis; Artificial intelligence in cardiovascular imaging: state of the art and implications for the imaging cardiologist; Radiomics and Artificial Intelligence for Biomarker and Prediction Model Development in Oncology
The combination of computational chemistry and computational materials science with machine learning and artificial intelligence This is relevant to AI in CADD
"Learning rates of state-of-the-art artificial learning algorithms can be improved by adopting fundamental principles that govern the dynamics of the brain" Biological learning curves outperform existing ones in artificial intelligence algorithms
Automated Diagnosis of Lymphoma with Digital Pathology Images Using Deep Learning
Yearbook of Medical Informatics, published by Thieme Journals in August 2019, contains many reviews on the AI applications in medical research and practice, for instance, "Recent Advances in Using Natural Language Processing to Address Public Health Research Questions Using Social Media and ConsumerGenerated Data", "Artificial Intelligence in Health: New Opportunities, Challenges, and Practical Implications", and many more. (*** added 08/2019)
Exploring microRNA Regulation of Cancer with Context-Aware Deep Cancer Classifier
PLATYPUS: A Multiple–View Learning Predictive Framework for Cancer Drug Sensitivity Prediction
Tumor classification based on image analysis: A Novel Method for Classifying Liver and Brain Tumors Using Convolutional Neural Networks, Discrete Wavelet Transform and Long Short-Term Memory Networks
Towards trustable machine learning "Clinical implementations of machine learning that are accurate, robust and interpretable will eventually gain the trust of healthcare providers and patients"
Gartner on ML in business (very general and not focused on pharma, but could be a good primer): https://www.gartner.com/binaries/content/assets/events/keywords/catalyst/catus8/preparing_and_architecting_for_machine_learning.pdf
Review: Looking beyond the hype: Applied AI and machine learning in translational medicine
Special issue of the Journal of the American College of Radiology on AI and Data Science, with many application cases of AI to clinical image processing
Statistical considerations for testing an AI algorithm used for prescreening lung CT images. Although a highly specialized use case is employed here, the statistical concepts are broadly valid.
Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Report from A. Zhavoronkov's group to Nature Biotechnology.
Extracting biological age from biomedical data via deep learning: too much of a good thing? An attempt to create a CNN predictor of the natural "biological clock"
Computational prediction of diagnosis and feature selection on mesothelioma patient health records. Random forest outperformed all models tried, but MCC is low for all model categories. Data is available via the Irvine depository.
Modelling the prevalence of diabetes mellitus risk factors based on artificial neural network and multiple regression. Logistic regression outperformes a neural net. (*** added 09/2019)
AI used to make sonogram movies of living heart. STAT news report.
User-centric design of AI products: People+AI Guidebook from Google
AAIH whitepaper describes the basic AI techniques and discusses some applications in healthcare: https://www.theaaih.org/pdf/1571334853.pdf
AI in health care delivery: https://catalyst.nejm.org/health-care-ai-systems-changing-delivery/
WanDB, a package to log hyperparameters and output metrics from your runs, explore model architectures, and compare results. (FYI only. We at Pistoia Alliance have NOT tested this).
Bradshaw molecular design tool GSK Oct 2019 https://link.springer.com/article/10.1007/s10822-019-00234-8
Looking beyond the hype: Applied AI and machine learning in translational medicine
Characterizing Artificial Intelligence Applications in Cancer Research: A Latent Dirichlet Allocation Analysis. Trends in AI publications in medicine.
How far have decision tree models come for data mining in drug discovery?
A collection of psychiatry-themed use cases of AI came out this month:
Complexity in mood disorder diagnosis: fMRI connectivity networks predicted medication‐class of response in complex patients
Exploring the Utility of Community-Generated Social Media Content for Detecting Depression: An Analytical Study on Instagram
Machine learning in major depression: From classification to treatment outcome prediction
Prediction Models of Functional Outcomes for Individuals in the Clinical High-Risk State for Psychosis or With Recent-Onset Depression Apparently, ML-based prognostic engine out-performs human psychiatrists
The plan to mine the world’s research papers. Nature on a controversial plan to subvert the copyright
Discovery of Novel Conotoxin Candidates Using Machine Learning. De-novo design of toxic peptides. Interesting but unfortunately does not include experimental validation of the pharmaceutical properties of these sequences
Why deep-learning AIs are so easy to fool DNNs are brilliant at highly specific tasks and brittle when unexpected input arrives. Nature editorial on adversarial learning in DNNs.
Artificial Intelligence, Responsibility Attribution, and a Relational Justification of Explainability. Responsibility to explain AI results is not only for the regulators, but mainly for patients.
Key challenges for delivering clinical impact with artificial intelligence. "Robust clinical evaluation, using metrics that are intuitive to clinicians and ideally go beyond measures of technical accuracy to include quality of care and patient outcomes, is essential. Further work is required (1) to identify themes of algorithmic bias and unfairness while developing mitigations to address these, (2) to reduce brittleness and improve generalisability, and (3) to develop methods for improved interpretability of machine learning predictions." (*** added 10/2019)
A good primer, An Ophthalmologist’s Guide to Deciphering Studies in Artificial Intelligence
Reliable Prediction of Human Cytochrome P450 Inhibition Using Artificial Intelligence Approaches
Artificial‐Intelligence‐Driven Organic Synthesis—En Route towards Autonomous Synthesis?
Has Drug Design Augmented by Artificial Intelligence Become a Reality? By Ola Engkvist
Transfer learning for biomedical named entity recognition with neural networks
Fréchet ChemNet Distance: A Metric for Generative Models for Molecules in Drug Discovery
AI algorithm detects melanoma in skin images with accuracy matching that of a specialist MD
Artificial intelligence applications for pediatric oncology imaging. A review of a multitude of AI application in medical imaging in many modalities. Contrary to the title, case studies are not limited to juvenile patients, so the review is broadly applicable.
Design of metalloproteins and novel protein folds using variational autoencoders. Automated protein engineering. Follows the best practice of making code fully and freely available (see ref).
Superior skin cancer classification by the combination of human and artificial intelligence. What is interesting about this paper (yet another description of yet another clinical classifier), are: (1) the size of the collaboration effort; and (2) the combined approach of using AI and human input.
Protein engineering with Machine Learning driven CAD: "One-shot optimization of multiple enzyme parameters: Tailoring glucose oxidase for pH and electron mediators"
Pathways to breast cancer screening artificial intelligence algorithm validation. This paper focuses on external validation of diagnostic AI algorithms. How much validation is enough? Is the US FDA standard too permissive?
The Last Mile: Where Artificial Intelligence Meets Reality discusses the challenges to the practical use of AI in the clinical setting
Clinical-grade computational pathology using weakly supervised deep learning on whole slide images (Nature Medicine)
This may be important for all innovators, but the full text is behind a pay wall: Early Identification of Patentable Medical Innovations Use AI to browse descriptions of medical discoveries and flag those that are likely to be patentable
BIPSPI: a method for the prediction of partner-specific protein–protein interfaces Alas, leave-one-out validation method is prone to overfitting
Automatic Organ Segmentation for CT Scans Based on Super-Pixel and Convolutional Neural Networks Mentions some benchmark datasets for the organ segmentation problem ("Silver 7", (ref))
An Efficient Implementation of Deep Convolutional Neural Networks for MRI Segmentation
Computational Protein Design with Deep Learning Neural Networks While interesting, the resulting accuracy of 38% indicates that there is much room for improvement
Current trends in AI drug discovery start-ups, by our member Simon Smith
167 Startups Using Artificial Intelligence in Drug Discovery, by Simon Smith. This list was started in 2017 and is continuously updated.
62 Drugs in the Artificial Intelligence in Drug Discovery Pipeline, by Simon Smith
By this moment (11.2019) the US FDA has approved at least 26 medical devices that use AI. Link Link to the original Eric Topol post from the middle of 2019 and to his Nature paper
Statement from FDA Commissioner Scott Gottlieb, M.D. on steps toward a new, tailored review framework for artificial intelligence-based medical devices: "We are exploring a framework that would allow for modifications to algorithms to be made from real-world learning and adaptation, while still ensuring safety and effectiveness of the software as a medical device is maintained. A new approach to these technologies would address the need for the algorithms to learn and adapt when used in the real world"
DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning
Artificial intelligence in clinical and genomic diagnostics. Review (*** added 11/2019)
Real-time multi-model ensemble forecasts for seasonal influenza in the U.S.
A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic- and molecular-phenotypes of epithelial ovarian cancer Nature Communications
Are we doing a good job in validation of medical devices that are powered by AI? Lifecycle Regulation of Artificial Intelligence– and Machine Learning–Based Software Devices in Medicine, JAMA
How to Read Articles That Use Machine Learning. JAMA 2019 Users’ Guides to the Medical Literature
Adversarial Controls for Scientific Machine Learning. ACS Chemical Biology 2018 "Machine learning algorithms readily exploit confounding variables and experimental artifacts instead of relevant patterns, leading to overoptimistic performance and poor model generalization."
CDSeq: A novel complete deconvolution method for dissecting heterogeneous samples using gene expression data. A machine-learning technique
Algorithms on regulatory lockdown in medicine. Science paper on the regulation of AI-driven medical technologies.
Rethinking drug design in the artificial intelligence era. Nature review with an extensive reference list. Gisbert et al..
Attitudes Of Chinese Cancer Patients Toward The Clinical Use Of Artificial Intelligence. Cancer patients trust human doctors more than AI when AI recommendations and human MD opinions differ. Going forward this topic of trust will be gaining importance
Visualizing structure and transitions in high-dimensional biological data. An algorithm to present highly complex, multi-dimensional data in a form with reduced dimensionality, suitable for human review.
PathFlowAI: A High-Throughput Workflow for Preprocessing, Deep Learning and Interpretation in Digital Pathology. Using AI to analyze liver microscopy images
Pacific Symposium in Biocomputing announcement of the workshop in ethics of AI.
DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences
Random forest prediction of Alzheimer’s disease using pairwise selection from time series data. Not AI but machine learning
Converging a Knowledge-Based Scoring Function: DrugScore2018. DrugScore2018 is a competitive scoring and objective function for structure-based ligand design purposes.
Practical Model Selection for Prospective Virtual Screening. Random Forest wins over complex NN methods.
Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions Nature Communications 2019 Gasteiger et al
Rule of thumb: Which AI / ML algorithms to apply to business problems. Has a small section on problems in health sciences. Ideally we'd be able to produce a similar guide but for problems specific to life science research.
The global landscape of AI ethics guidelines. Nature 09/2019
Artificial Intelligence in medical imaging practice: looking to the future. A short review that covers the major application advances in the field as well as the ethical part of the AI use in medical decision-making. 11/2019
Drug combination sensitivity scoring facilitates the discovery of synergistic and efficacious drug combinations in cancer. Use ML to cluster cancer cell lines based on their drug combination sensitivity profiles. PLOS Comp Bio 05/2019
Implementation of machine learning algorithms to create diabetic patient re-admission profiles using public data sources at UC Irvine Machine Learning Repository
Bayesian Nonparametric Models tutorial
Comparison Study of Computational Prediction Tools for Drug-Target Binding Affinities
From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices
Ethical considerations about artificial intelligence for prognostication in intensive care "Respect for patients’ autonomy during decision-making requires transparency of the data processing by AI models to explain the predictions derived from these models." (*** added 12/2019)
International evaluation of an AI system for breast cancer screening. This is a Google-developed tool which is claimed to achieve a higher accuracy in diagnosis of breast cancer than that by human oncologists. However, code and data used to build the classifier are not released.
Applications of Machine Learning Approaches in Emergency Medicine; a Review Article. In this paper the authors compare performance of a different ML and AI methods for prediction (diagnosis, to be precise) of various diseases from EHR records. Performance is variable and the methods used for different disease types are vastly different.
Identification of pharmacodynamic biomarker hypotheses through literature analysis with IBM Watson
Opportunities for Artificial Intelligence in Advancing Precision Medicine. Review of ML methods used to analyze "-omics" data.
Towards A Rigorous Science of Interpretable Machine Learning. Arxiv
The MELLODDY Consortium. Pharma Companies Join Forces to Train AI for Drug Discovery Using Blockchain
Addressing Bias in Artificial Intelligence in Health Care. Abstract only.
AI in biomedical sciences attracted over $5B in VC investment. Market report
Deep Learning-driven research for drug discovery: Tackling Malaria. PLOS
Contract offers unprecedented look at Google deal to obtain patient data from the University of California. UCSF allowed Google to use patient data for AI research, which raises all kinds of ethical questions.
A comparative study of deep learning architectures on melanoma detection
A Deep Learning Approach to Antibiotic Discovery (*** added 02/2020)
Machine learning in chemoinformatics and drug discovery. Review, may be valuable due to multiple references cited.
Machine learning with random subspace ensembles identifies antimicrobial resistance determinants from pan-genomes of three pathogens. PLOS Comp Bio
Systematic Review of Privacy-Preserving Distributed Machine Learning From Federated Databases in Health Care. Review
COVID-19 Open Research Dataset (CORD-19) of scholarly literature has been released. Suitable for ML.
In-Silico Molecular Binding Prediction for Human Drug Targets Using Deep Neural Multi-Task Learning. A comparison of many single-task and multi-task ML methods. Contrary to earlier observations, single-task models perform better. Multi-task models work well for groups of highly similar targets (as expected, more data results in better predictive performance).
Predicting or Pretending: Artificial Intelligence for Protein-Ligand Interactions Lack of Sufficiently Large and Unbiased Datasets. More data, better, less biased benchmarks are needed.
Large-scale comparison of machine learning methods for drug target prediction on ChEMBL.
A deep learning framework for automatic diagnosis of unipolar depression. A classifier for diagnosis of major depression from EEG data.
ARPNet: Antidepressant Response Prediction Network for Major Depressive Disorder. Not an ideal study set-up, likely resulting in overfitting. But the overall proposal to automate selection of drugs for personalized therapy is good.