machine learning

Summary

Summary: A type of ARTIFICIAL INTELLIGENCE that enable COMPUTERS to independently initiate and execute LEARNING when exposed to new data.

Top Publications

  1. Pardakhti M, Moharreri E, Wanik D, Suib S, Srivastava R. Machine Learning Using Combined Structural and Chemical Descriptors for Prediction of Methane Adsorption Performance of Metal Organic Frameworks (MOFs). ACS Comb Sci. 2017;19:640-645 pubmed publisher
    ..b>Machine learning (ML) algorithms trained on fundamental material properties can potentially provide quick and accurate methods ..
  2. Šícho M, de Bruyn Kops C, Stork C, Svozil D, Kirchmair J. FAME 2: Simple and Effective Machine Learning Model of Cytochrome P450 Regioselectivity. J Chem Inf Model. 2017;57:1832-1846 pubmed publisher
    ..The best models have been integrated into a newly developed software package (FAME 2), which is available free of charge from the authors. ..
  3. Riaz F, Niazi M. Towards social autonomous vehicles: Efficient collision avoidance scheme using Richardson's arms race model. PLoS ONE. 2017;12:e0186103 pubmed publisher
    ..876% as compared with the IEEE 802.11n-based existing state of the art mirroring neuron-based collision avoidance scheme...
  4. Kreif N, Tran L, Grieve R, Destavola B, Tasker R, Petersen M. Estimating the comparative effectiveness of feeding interventions in the paediatric intensive care unit: a demonstration of longitudinal targeted maximum likelihood estimation. Am J Epidemiol. 2017;: pubmed publisher
    ..targeted maximum likelihood based estimation (TMLE), a double-robust method that can be coupled with machine learning, has been proposed...
  5. Beaulieu Jones B, Greene C. Reproducibility of computational workflows is automated using continuous analysis. Nat Biotechnol. 2017;35:342-346 pubmed publisher
    ..Continuous analysis allows reviewers, editors or readers to verify reproducibility without manually downloading and rerunning code and can provide an audit trail for analyses of data that cannot be shared. ..
  6. Zhang S, Fan X. Computational Methods for Predicting ncRNA-protein Interactions. Med Chem. 2017;13:515-525 pubmed publisher
    ..aspects: i) the dataset construction; ii) the sequence and structural feature representation, and iii) the machine learning algorithm...
  7. Monaro M, Gamberini L, Sartori G. The detection of faked identity using unexpected questions and mouse dynamics. PLoS ONE. 2017;12:e0177851 pubmed publisher
    ..Parameters that encode mouse movement were analyzed using machine learning classifiers and the results indicate that the mouse trajectories and errors on unexpected questions ..
  8. Glasser M, Coalson T, Robinson E, Hacker C, Harwell J, Yacoub E, et al. A multi-modal parcellation of human cerebral cortex. Nature. 2016;536:171-178 pubmed publisher
  9. Segura Bedmar I, Martinez P, Revert R, Moreno Schneider J. Exploring Spanish health social media for detecting drug effects. BMC Med Inform Decis Mak. 2015;15 Suppl 2:S6 pubmed publisher
    ..Moreover, patients use lay terminology to refer to diseases, symptoms and indications that is not usually included in lexical resources in languages other than English. ..

More Information

Publications62

  1. Abawajy J, Kelarev A, Chowdhury M, Jelinek H. Enhancing Predictive Accuracy of Cardiac Autonomic Neuropathy Using Blood Biochemistry Features and Iterative Multitier Ensembles. IEEE J Biomed Health Inform. 2016;20:408-15 pubmed publisher
    ..The best predictive accuracy of 99.57% has been obtained by the AIME combining decorate on top tier with bagging on middle tier based on random forest. Practitioners can use these findings to increase the accuracy of CAN diagnosis. ..
  2. da Silva R, Dorrestein P, Quinn R. Illuminating the dark matter in metabolomics. Proc Natl Acad Sci U S A. 2015;112:12549-50 pubmed publisher
  3. Cook D, Schmitter Edgecombe M, Dawadi P. Analyzing Activity Behavior and Movement in a Naturalistic Environment Using Smart Home Techniques. IEEE J Biomed Health Inform. 2015;19:1882-92 pubmed publisher
    ..We analyze the data using machine learning techniques and reveal that differences between healthy older adults and adults with Parkinson disease not ..
  4. Kuan P, Powers S, He S, Li K, Zhao X, Huang B. A systematic evaluation of nucleotide properties for CRISPR sgRNA design. BMC Bioinformatics. 2017;18:297 pubmed publisher
    ..An R package predictSGRNA implementing the predictive model is available at http://www.ams.sunysb.edu/~pfkuan/softwares.html#predictsgrna . ..
  5. Li J, Zheng S, Chen B, Butte A, Swamidass S, Lu Z. A survey of current trends in computational drug repositioning. Brief Bioinform. 2016;17:2-12 pubmed publisher
    ..Finally, we highlight potential opportunities and use-cases, including a few target areas such as cancers. We conclude with a brief discussion of the remaining challenges in computational drug repositioning. ..
  6. McSkimming D, Rasheed K, Kannan N. Classifying kinase conformations using a machine learning approach. BMC Bioinformatics. 2017;18:86 pubmed publisher
    ..We utilize an unbiased informatics based machine learning approach to classify all eukaryotic protein kinase conformations deposited in the PDB...
  7. Hu Y, Hase T, Li H, Prabhakar S, Kitano H, Ng S, et al. A machine learning approach for the identification of key markers involved in brain development from single-cell transcriptomic data. BMC Genomics. 2016;17:1025 pubmed publisher
    ..the analysis of single-cell RNA-seq data, obtained from neocortical cells and neural progenitor cells, using machine learning algorithms (Support Vector machine (SVM) and Random Forest (RF))...
  8. Kilicoglu H, Rosemblat G, Rindflesch T. Assigning factuality values to semantic relations extracted from biomedical research literature. PLoS ONE. 2017;12:e0179926 pubmed publisher
    ..We compared this approach to a supervised machine learning method that uses a rich feature set based on the annotated corpus...
  9. Geertz Hansen H, Kiemer L, Nielsen M, Stanchev K, Blom N, Brunak S, et al. Protein features as determinants of wild-type glycoside hydrolase thermostability. Proteins. 2017;85:2036-2044 pubmed publisher
    ..The presented prediction method is made publicly available at http://www.cbs.dtu.dk/services/ThermoP. ..
  10. Bogunovic H, Montuoro A, Baratsits M, Karantonis M, Waldstein S, Schlanitz F, et al. Machine Learning of the Progression of Intermediate Age-Related Macular Degeneration Based on OCT Imaging. Invest Ophthalmol Vis Sci. 2017;58:BIO141-BIO150 pubmed publisher
    ..Finally, a machine learning method based on a sparse Cox proportional hazard regression was developed to estimate a risk score and ..
  11. Mort M, Sterne Weiler T, Li B, Ball E, Cooper D, Radivojac P, et al. MutPred Splice: machine learning-based prediction of exonic variants that disrupt splicing. Genome Biol. 2014;15:R19 pubmed publisher
    ..MutPred Splice is available at http://mutdb.org/mutpredsplice. ..
  12. Cui J, Liu X, Wang Y, Liu H. Deep reconstruction model for dynamic PET images. PLoS ONE. 2017;12:e0184667 pubmed publisher
    ..The qualitative and quantitative results of the procedure, including the data based on a Monte Carlo simulation and real patient data demonstrates the effectiveness of our method...
  13. Zhou Q, Flores A, Glenn N, Walters R, Han B. A machine learning approach to estimation of downward solar radiation from satellite-derived data products: An application over a semi-arid ecosystem in the U.S. PLoS ONE. 2017;12:e0180239 pubmed publisher
    ..This paper presents a machine learning approach in the form of a random forest (RF) model for estimating daily downward solar radiation flux at the ..
  14. Tajbakhsh N, Shin J, Gurudu S, Hurst R, Kendall C, Gotway M, et al. Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?. IEEE Trans Med Imaging. 2016;35:1299-1312 pubmed publisher
  15. Haj Rachid M. Two Efficient Techniques to Find Approximate Overlaps between Sequences. Biomed Res Int. 2017;2017:2731385 pubmed publisher
    ..The number of mismatches (hamming distance) is used to define the approximate matching between strings in our work. ..
  16. Silva J, Carvalho T, Basso M, Deguchi M, Pereira W, Sobrinho R, et al. Geminivirus data warehouse: a database enriched with machine learning approaches. BMC Bioinformatics. 2017;18:240 pubmed publisher
    ..Data mining approaches, mainly supported by machine learning (ML) techniques, are a natural means for high-throughput data analysis in the context of genomics, ..
  17. Xing J, Lu W, Liu R, Wang Y, Xie Y, Zhang H, et al. Machine-Learning-Assisted Approach for Discovering Novel Inhibitors Targeting Bromodomain-Containing Protein 4. J Chem Inf Model. 2017;57:1677-1690 pubmed publisher
    ..Moreover, inverting the machine-learning model provided easy access to structure-activity relationship (SAR) interpretation for hit-to-lead optimization. ..
  18. Anastasiadi M, Mohareb F, Redfern S, Berry M, Simmonds M, Terry L. Biochemical Profile of Heritage and Modern Apple Cultivars and Application of Machine Learning Methods To Predict Usage, Age, and Harvest Season. J Agric Food Chem. 2017;65:5339-5356 pubmed publisher
    ..Furthermore, advanced machine learning methods were applied with the objective to identify whether the phenolic and sugar composition of an apple ..
  19. Acharjee A, Ament Z, West J, Stanley E, Griffin J. Integration of metabolomics, lipidomics and clinical data using a machine learning method. BMC Bioinformatics. 2016;17:440 pubmed publisher
    ..and lipidomics datasets with classical toxicological parameters we developed a hypotheses free, data driven machine learning approach...
  20. Muratov E, Lewis M, Fourches D, Tropsha A, Cox W. Computer-Assisted Decision Support for Student Admissions Based on Their Predicted Academic Performance. Am J Pharm Educ. 2017;81:46 pubmed publisher
    ..Random Forest machine learning technique was used to develop a binary classification model based on 11 pre-admission parameters. Results...
  21. Zhao B, Wang J, Wu F. Computational Methods to Predict Protein Functions from Protein-Protein Interaction Networks. Curr Protein Pept Sci. 2017;18:1120-1131 pubmed publisher
    ..We then focus on the various approaches available in categories of supervised and unsupervised methods for predicting protein functions. Finally, we discuss challenges and future works in this field. ..
  22. Engchuan W, Dhindsa K, Lionel A, Scherer S, Chan J, Merico D. Performance of case-control rare copy number variation annotation in classification of autism. BMC Med Genomics. 2015;8 Suppl 1:S7 pubmed publisher
    ..Here, we used machine learning methods to classify ASD subjects and controls, based on rare CNV data and comprehensive gene annotations...
  23. Van Segbroeck M, Knoll A, Levitt P, Narayanan S. MUPET-Mouse Ultrasonic Profile ExTraction: A Signal Processing Tool for Rapid and Unsupervised Analysis of Ultrasonic Vocalizations. Neuron. 2017;94:465-485.e5 pubmed publisher
    ..MUPET thus serves as a new tool for USV repertoire analyses, with the capability to be adapted for use with other species. ..
  24. Kong Z, Zou Y, Liu T. Implementation of real-time energy management strategy based on reinforcement learning for hybrid electric vehicles and simulation validation. PLoS ONE. 2017;12:e0180491 pubmed publisher
    ..Results indicate that proposed method has better fuel economy than stationary one and is more effective in real-time control. ..
  25. Cheng B, Liu M, Zhang D, Munsell B, Shen D. Domain Transfer Learning for MCI Conversion Prediction. IEEE Trans Biomed Eng. 2015;62:1805-1817 pubmed publisher
    b>Machine learning methods have successfully been used to predict the conversion of mild cognitive impairment (MCI) to Alzheimer's disease (AD), by classifying MCI converters (MCI-C) from MCI nonconverters (MCI-NC)...
  26. Xia X, Brophy S, Zhou S. Learning Differentially Expressed Gene Pairs in Microarray Data. Stud Health Technol Inform. 2017;235:191-195 pubmed
  27. McGinnis R, Mahadevan N, Moon Y, Seagers K, Sheth N, Wright J, et al. A machine learning approach for gait speed estimation using skin-mounted wearable sensors: From healthy controls to individuals with multiple sclerosis. PLoS ONE. 2017;12:e0178366 pubmed publisher
    ..In this paper, we present a machine learning method for estimating gait speed using a configurable array of skin-mounted, conformal accelerometers...
  28. Walsh M, Willem de Smalen A, Mor S. Wetlands, wild Bovidae species richness and sheep density delineate risk of Rift Valley fever outbreaks in the African continent and Arabian Peninsula. PLoS Negl Trop Dis. 2017;11:e0005756 pubmed publisher
    ..Africa and the Arabian Peninsula as a function of a suite of biotic and abiotic landscape features using machine learning methods...
  29. Chen W, Liu J, He S. Prior knowledge guided active modules identification: an integrated multi-objective approach. BMC Syst Biol. 2017;11:8 pubmed publisher
    ..Integrating knowledge of functional groups into the identification of active module is an effective method and provides a flexible control of balance between pure data-driven method and prior information guidance. ..
  30. Fierst J, Murdock D. Decontaminating eukaryotic genome assemblies with machine learning. BMC Bioinformatics. 2017;18:533 pubmed publisher
    ..We introduce a novel application of an established machine learning method, a decision tree, that can rigorously classify sequences...
  31. Ribeiro de Paula D, Ziegler E, Abeyasinghe P, Das T, Cavaliere C, Aiello M, et al. A method for independent component graph analysis of resting-state fMRI. Brain Behav. 2017;7:e00626 pubmed publisher
    ..To date, the spatial patterns of the networks have been analyzed with techniques developed for volumetric data...
  32. Yang L, Orenstein Y, Jolma A, Yin Y, Taipale J, Shamir R, et al. Transcription factor family-specific DNA shape readout revealed by quantitative specificity models. Mol Syst Biol. 2017;13:910 pubmed publisher
    ..Overall, this work suggests a way of obtaining mechanistic insights into TF-DNA binding without relying on experimentally solved all-atom structures. ..
  33. Dai W, Duch W, Abdullah A, Xu D, Chen Y. Recent Advances in Learning Theory. Comput Intell Neurosci. 2015;2015:395948 pubmed publisher
  34. Hengl T, Mendes de Jesus J, Heuvelink G, Ruiperez Gonzalez M, Kilibarda M, Blagotić A, et al. SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE. 2017;12:e0169748 pubmed publisher
    ..derivatives, climatic images and global landform and lithology maps), which were used to fit an ensemble of machine learning methods-random forest and gradient boosting and/or multinomial logistic regression-as implemented in the R ..
  35. Takeuchi M, Inuzuka R, Hayashi T, Shindo T, Hirata Y, Shimizu N, et al. Novel Risk Assessment Tool for Immunoglobulin Resistance in Kawasaki Disease: Application Using a Random Forest Classifier. Pediatr Infect Dis J. 2017;36:821-826 pubmed publisher
    ..A random forest (RF) classifier, a tree-based machine learning technique, was applied to these data...
  36. Kurczab R, Bojarski A. The influence of the negative-positive ratio and screening database size on the performance of machine learning-based virtual screening. PLoS ONE. 2017;12:e0175410 pubmed publisher
    The machine learning-based virtual screening of molecular databases is a commonly used approach to identify hits...
  37. Hu L, Li H, Cai Z, Lin F, Hong G, Chen H, et al. A new machine-learning method to prognosticate paraquat poisoned patients by combining coagulation, liver, and kidney indices. PLoS ONE. 2017;12:e0186427 pubmed publisher
    ..Then, a new, intelligent, machine learning-based system was established to effectively provide prognostic analysis of PQ-poisoning patients based on a ..
  38. Obermeyer Z, Lee T. Lost in Thought - The Limits of the Human Mind and the Future of Medicine. N Engl J Med. 2017;377:1209-1211 pubmed publisher
  39. Yu N, Yu Z, Pan Y. A deep learning method for lincRNA detection using auto-encoder algorithm. BMC Bioinformatics. 2017;18:511 pubmed publisher
    ..As our knowledge, this is the first application to adopt the deep learning techniques for identifying lincRNA transcription sequences. ..
  40. Liu M, Barton E, Jennings R, Oldenburg D, Whirry J, White D, et al. Unsupervised learning techniques reveal heterogeneity in memory CD8+ T cell differentiation following acute, chronic and latent viral infections. Virology. 2017;509:266-279 pubmed publisher
    ..Data from cluster models were combined and visualized by principal component analysis (PCA) demonstrating memory CD8+ T cells from latent infection occupy an intermediate differentiation space. ..
  41. Zhang M, Su Q, Lu Y, Zhao M, Niu B. Application of Machine Learning Approaches for Protein-protein Interactions Prediction. Med Chem. 2017;13:506-514 pubmed publisher
    ..In this review, in order to study the application of machine learning in predicting PPI, some machine learning approaches such as support vector machine (SVM), artificial neural ..
  42. Zhang J, Friberg I, Kift Morgan A, Parekh G, Morgan M, Liuzzi A, et al. Machine-learning algorithms define pathogen-specific local immune fingerprints in peritoneal dialysis patients with bacterial infections. Kidney Int. 2017;92:179-191 pubmed publisher
    ..Thus, our data establish the power of non-linear mathematical models to analyze complex biomedical datasets and highlight key pathways involved in pathogen-specific immune responses. ..
  43. Elmas A, Wang X, Dresch J. The folded k-spectrum kernel: A machine learning approach to detecting transcription factor binding sites with gapped nucleotide dependencies. PLoS ONE. 2017;12:e0185570 pubmed publisher
    ..We also demonstrate the effectiveness of our new approach by reporting on its improved performance on a set of 127 genomic regions known to regulate gene expression along the anterio-posterior axis in early Drosophila embryos...
  44. Jung S, Bi Y, Davuluri R. Evaluation of data discretization methods to derive platform independent isoform expression signatures for multi-class tumor subtyping. BMC Genomics. 2015;16 Suppl 11:S3 pubmed publisher
    ..We applied an integrated machine learning framework that involves three sequential steps; feature selection, data discretization, and classification...
  45. Uzuner O, Stubbs A. Practical applications for natural language processing in clinical research: The 2014 i2b2/UTHealth shared tasks. J Biomed Inform. 2015;58 Suppl:S1-5 pubmed publisher
  46. Bogunovic H, Waldstein S, Schlegl T, Langs G, Sadeghipour A, Liu X, et al. Prediction of Anti-VEGF Treatment Requirements in Neovascular AMD Using a Machine Learning Approach. Invest Ophthalmol Vis Sci. 2017;58:3240-3248 pubmed publisher
    ..We proposed and evaluated a machine learning methodology to predict anti-VEGF treatment needs from OCT scans taken during treatment initiation...
  47. Skeem J, Manchak S, Montoya L. Comparing Public Safety Outcomes for Traditional Probation vs Specialty Mental Health Probation. JAMA Psychiatry. 2017;74:942-948 pubmed publisher
    ..Probation is a cornerstone of efforts to reduce mass incarceration. Although it is understudied, specialty probation could improve outcomes for the overrepresented group of people with mental illness...
  48. Pan I, Nolan L, Brown R, Khan R, van der Boor P, Harris D, et al. Machine Learning for Social Services: A Study of Prenatal Case Management in Illinois. Am J Public Health. 2017;107:938-944 pubmed publisher
    To evaluate the positive predictive value of machine learning algorithms for early assessment of adverse birth risk among pregnant women as a means of improving the allocation of social services...
  49. Herrero P, Pesl P, Reddy M, Oliver N, Georgiou P, Toumazou C. Advanced Insulin Bolus Advisor Based on Run-To-Run Control and Case-Based Reasoning. IEEE J Biomed Health Inform. 2015;19:1087-96 pubmed
    ..03) and from 167 ± 25 to 162 ± 23 in the adolescent population (p = 0.06). In addition, CBR(R2R) was able to completely eliminate hypoglycaemia, while the R2R alone was not able to do it in the adolescent population. ..
  50. Erard N, Knott S, Hannon G. A CRISPR Resource for Individual, Combinatorial, or Multiplexed Gene Knockout. Mol Cell. 2017;67:348-354.e4 pubmed publisher
    ..By conducting parallel loss-of-function screens, we compare our approach to existing sgRNA design and expression strategies. ..
  51. Ding F, Ge Q, Jiang D, Fu J, Hao M. Understanding the dynamics of terrorism events with multiple-discipline datasets and machine learning approach. PLoS ONE. 2017;12:e0179057 pubmed publisher
    ..In the present study, we demonstrate a novel method using relatively popular and robust machine learning methods to simulate the risk of terrorist attacks at a global scale based on multiple resources, long time ..
  52. Bellinger C, Mohomed Jabbar M, Zaïane O, Osornio Vargas A. A systematic review of data mining and machine learning for air pollution epidemiology. BMC Public Health. 2017;17:907 pubmed publisher
    ..To this end, data mining and machine learning algorithms are increasingly being applied to air pollution epidemiology...
  53. Roth H, Lu L, Liu J, Yao J, Seff A, Cherry K, et al. Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation. IEEE Trans Med Imaging. 2016;35:1170-81 pubmed publisher
    ..Our proposed methods improve performance markedly in all cases. Sensitivities improved from 57% to 70%, 43% to 77%, and 58% to 75% at 3 FPs per patient for sclerotic metastases, lymph nodes and colonic polyps, respectively. ..