Self-Organizing Maps

Cluster analysis and display of genome-wide expression patterns

Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.25.14863 · Cited by 16,184

Anomaly detection: A survey

Chandola, V., Banerjee, A., & Kumar, V. (2009) · ACM Computing Surveys · doi:10.1145/1541880.1541882 · Cited by 11,687

Collinearity: a review of methods to deal with it and a simulation study evaluating their performance

Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G., Marquéz, J. R. G., Gruber, B., Lafourcade, B., Leitão, P. J., et al. (2013) · Ecography · doi:10.1111/j.1600-0587.2012.07348.x · Cited by 9,998

Image denoising by sparse 3-D transform-domain collaborative filtering

Dabov, K., Foi, A., Katkovnik, V., & Egiazarian, K. (2007) · IEEE Transactions on Image Processing · doi:10.1109/tip.2007.901238 · Cited by 9,283

Support vector machines in remote sensing: a review

Mountrakis, G., Im, J., & Ogole, C. (2011) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2010.11.001 · Cited by 3,473

A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection

Buczak, A. L., & Guven, E. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2494502 · Cited by 3,332

Continual lifelong learning with neural networks: A review

Parisi, G. I., Kemker, R., Part, J. L., Kanan, C., & Wermter, S. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.01.012 · Cited by 3,117

Machine Learning for Fluid Mechanics

Brunton, S. L., Noack, B. R., & Koumoutsakos, P. (2020) · Annual Review of Fluid Mechanics · doi:10.1146/annurev-fluid-010719-060214 · Cited by 3,063

Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data

Monti, S., Tamayo, P., Mesirov, J., & Golub, T. (2003) · Machine Learning · doi:10.1023/a:1023949509487 · Cited by 2,116

Data-driven Soft Sensors in the process industry

Kadlec, P., Gabrys, B., & Strandt, S. (2009) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2008.12.012 · Cited by 1,845

Polypharmacology: Challenges and Opportunities in Drug Discovery: Miniperspective

Anighoro, A., Bajorath, J., & Rastelli, G. (2014) · Journal of Medicinal Chemistry · doi:10.1021/jm5006463 · Cited by 1,131

Neural network credit scoring models

West, D. (2000) · Computers & Operations Research · doi:10.1016/s0305-0548(99)00149-5 · Cited by 1,033

Global threat to agriculture from invasive species

Paini, D. R., Sheppard, A. W., Cook, D. C., De Barro, P. J., Worner, S. P., & Thomas, M. B. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1602205113 · Cited by 927

Spontaneously emerging cortical representations of visual attributes

Kenet, T., Bibitchkov, D., Tsodyks, M., Grinvald, A., & Arieli, A. (2003) · Nature · doi:10.1038/nature02078 · Cited by 924

Bibliometric cartography of information retrieval research by using co-word analysis

Ding, Y., Chowdhury, G. G., & Foo, S. (2001) · Information Processing & Management · doi:10.1016/s0306-4573(00)00051-0 · Cited by 882

Machine learning in wireless sensor networks: Algorithms, strategies, and applications

Alsheikh, M. A., Lin, S., Niyato, D., & Tan, H.-P. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2014.2320099 · Cited by 965

Machine Learning Methods Without Tears: A Primer for Ecologists

Olden, J. D., Lawler, J. J., & Poff, N. L. (2008) · The Quarterly Review of Biology · doi:10.1086/587826 · Cited by 823

State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems

Fadlullah, Z. M., Tang, F., Mao, B., Kato, N., Akashi, O., Inoue, T., & Mizutani, K. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2707140 · Cited by 810

Decoding Neural Representational Spaces Using Multivariate Pattern Analysis

Haxby, J. V., Connolly, A. C., & Guntupalli, J. S. (2014) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-062012-170325 · Cited by 717

Machine learning and its applications to biology

Tarca, A. L., Carey, V. J., Chen, X.-W., Romero, R., & Drăghici, S. (2007) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030116 · Cited by 656

Enhanced and diminished visuo-spatial information processing in autism depends on stimulus complexity

Bertone, A., Mottron, L., Jelenic, P., & Faubert, J. (2005) · Brain · doi:10.1093/brain/awh561 · Cited by 536

Urine steroid metabolomics as a biomarker tool for detecting malignancy in adrenal tumors

Arlt, W., Biehl, M., Taylor, A. E., Hahner, S., Libé, R., Hughes, B. A., Schneider, P., Smith, D. J., Stiekema, H., Krone, N., et al. (2011) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2011-1565 · Cited by 458

Riemannian Manifold Learning

Lin, T., & Zha, H. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.70735 · Cited by 446

Oncogenic Ras and Akt signaling contribute to glioblastoma formation by differential recruitment of existing mRNAs to polysomes.

Rajasekhar, V. K., Viale, A., Socci, N. D., Wiedmann, M., Hu, X., & Holland, E. C. (2003) · Molecular Cell · doi:10.1016/s1097-2765(03)00395-2 · Cited by 420

The temporal binding deficit hypothesis of autism

BROCK, J., BROWN, C. C., BOUCHER, J., & RIPPON, G. (2002) · Development and Psychopathology · doi:10.1017/s0954579402002018 · Cited by 419

Large Scale Gene Expression Analysis of Osteoclastogenesisin Vitro and Elucidation of NFAT2 as a Key Regulator

Ishida, N., Hayashi, K., Hoshijima, M., Ogawa, T., Koga, S., Miyatake, Y., Kumegawa, M., Kimura, T., & Takeya, T. (2002) · Journal of Biological Chemistry · doi:10.1074/jbc.m205063200 · Cited by 377

High-dimensional data clustering

Bouveyron, C., Girard, S., & Schmid, C. (2007) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2007.02.009 · Cited by 382

Twisted light transmission over 143 km

Krenn, M., Handsteiner, J., Fink, M., Fickler, R., Ursin, R., Malik, M., & Zeilinger, A. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1612023113 · Cited by 382

The emergence of competing modules in bilingualism

HERNANDEZ, A., LI, P., & MACWHINNEY, B. (2005) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2005.03.003 · Cited by 366

A Survey of Outlier Detection Methodologies

Hodge, V., & Austin, J. (2004) · Artificial Intelligence Review · doi:10.1007/s10462-004-4304-y · Cited by 3,596

Tutorial on practical tips of the most influential data preprocessing algorithms in data mining

García, S., Luengo, J., & Herrera, F. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.12.006 · Cited by 310

Explainable artificial intelligence for breast cancer: A visual case-based reasoning approach

Lamy, J.-B., Sekar, B., Guezennec, G., Bouaud, J., & Séroussi, B. (2019) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2019.01.001 · Cited by 282

Genome-wide map of regulatory interactions in the human genome

Heidari, N., Phanstiel, D. H., He, C., Grubert, F., Jahanbani, F., Kasowski, M., Zhang, M. Q., & Snyder, M. P. (2014) · Genome Research · doi:10.1101/gr.176586.114 · Cited by 278

Studying Complex Discursive Systems..: Centering Resonance Analysis of Communication

Corman, S. R., Kuhn, T., Mcphee, R. D., & Dooley, K. J. (2002) · Human Communication Research · doi:10.1111/j.1468-2958.2002.tb00802.x · Cited by 274

Finding approximate POMDP solutions through belief compression

Roy, N., Gordon, G., & Thrun, S. (2005) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1496 · Cited by 254

Untitled work

(2012) · Cited by 253

A renaissance of neural networks in drug discovery

Baskin, I. I., Winkler, D., & Tetko, I. V. (2016) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2016.1201262 · Cited by 235

Chemistry42: An AI-Driven Platform for Molecular Design and Optimization

Ivanenkov, Y. A., Polykovskiy, D., Bezrukov, D., Zagribelnyy, B., Aladinskiy, V., Kamya, P., Aliper, A., Ren, F., & Zhavoronkov, A. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01191 · Cited by 231

Quantum Speedup for Active Learning Agents

Paparo, G. D., Dunjko, V., Makmal, A., Martin-Delgado, M. A., & Briegel, H. J. (2014) · Physical Review X · doi:10.1103/physrevx.4.031002 · Cited by 192

Reconciling intuitive physics and Newtonian mechanics for colliding objects

Sanborn, A. N., Mansinghka, V. K., & Griffiths, T. L. (2013) · Psychological Review · doi:10.1037/a0031912 · Cited by 189

Analysis of physiological signals for recognition of boredom, pain, and surprise emotions

Jang, E.-H., Park, B.-J., Park, M.-S., Kim, S.-H., & Sohn, J.-H. (2015) · Journal of Physiological Anthropology · doi:10.1186/s40101-015-0063-5 · Cited by 159

Peripheral and central inputs shape network dynamics in the developing visual cortex in vivo

Siegel, F., Heimel, J. A., Peters, J., & Lohmann, C. (2012) · Current Biology · doi:10.1016/j.cub.2011.12.026 · Cited by 142

Nonlinear multivariate and time series analysis by neural network methods

Hsieh, W. W. (2004) · Reviews of Geophysics · doi:10.1029/2002rg000112 · Cited by 148

Chemometrics in bioprocess engineering: Process analytical technology (PAT) applications

Lopes, J. A., Costa, P. F., Alves, T. P., & Menezes, J. C. (2004) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2004.07.006 · Cited by 137

Identification and validation of novel androgen-regulated genes in prostate cancer

Velasco, A. M., Gillis, K. A., Li, Y., Brown, E. L., Sadler, T. M., Achilleos, M., Greenberger, L. M., Frost, P., Bai, W., & Zhang, Y. (2004) · Endocrinology · doi:10.1210/en.2004-0311 · Cited by 131

Transcriptomic and proteomic analyses of the Aspergillus fumigatus hypoxia response using an oxygen-controlled fermenter

Barker, B. M., Kroll, K., Vödisch, M., Mazurie, A., Kniemeyer, O., & Cramer, R. A. (2012) · BMC Genomics · doi:10.1186/1471-2164-13-62 · Cited by 127

Time-scale analysis of motor unit action potentials

Pattichis, C. S., & Pattichis, M. S. (1999) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.797992 · Cited by 105

Biological mechanism profiling using an annotated compound library

Root, D. E., Flaherty, S. P., Kelley, B. P., & Stockwell, B. R. (2003) · Chemistry & Biology · doi:10.1016/j.chembiol.2003.08.009 · Cited by 95

Exchange rate prediction using hybrid neural networks and trading indicators

Ni, H., & Yin, H. (2009) · Neurocomputing · doi:10.1016/j.neucom.2008.09.023 · Cited by 94

Bcl11b and combinatorial resolution of cell fate in the T-cell gene regulatory network

Longabaugh, W. J. R., Zeng, W., Zhang, J. A., Hosokawa, H., Jansen, C. S., Li, L., Romero-Wolf, M., Liu, P., Kueh, H. Y., Mortazavi, A., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1610617114 · Cited by 89

Expert System for Predicting Reaction Conditions: The Michael Reaction Case

Marcou, G., Aires de Sousa, J., Latino, D. A. R. S., de Luca, A., Horvath, D., Rietsch, V., & Varnek, A. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/ci500698a · Cited by 86

somoclu : An Efficient Parallel Library for Self-Organizing Maps

Wittek, P., Gao, S. C., Lim, I. S., & Zhao, L. (2017) · Journal of Statistical Software · doi:10.18637/jss.v078.i09 · Cited by 80

Heritage Interpretation and Sustainable Development: A Systematic Literature Review

Nowacki, M. (2021) · Sustainability · doi:10.3390/su13084383 · Cited by 73

Machine Learning in Human Olfactory Research

Lötsch, J., Kringel, D., & Hummel, T. (2019) · Chemical Senses · doi:10.1093/chemse/bjy067 · Cited by 71

Meta-analysis of primary target genes of peroxisome proliferator-activated receptors

Heinäniemi, M., Uski, J. O., Degenhardt, T., & Carlberg, C. (2007) · Genome Biology · doi:10.1186/gb-2007-8-7-r147 · Cited by 71

Fingerprinting Heatwaves and Cold Spells and Assessing Their Response to Climate Change Using Large Deviation Theory

Galfi, V. M., & Lucarini, V. (2021) · Physical Review Letters · doi:10.1103/physrevlett.127.058701 · Cited by 62

Selective regulation of biological processes by vitamin D based on the spatio-temporal cistrome of its receptor

Neme, A., Seuter, S., & Carlberg, C. (2017) · Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms · doi:10.1016/j.bbagrm.2017.07.002 · Cited by 64

Enhancing discrete-event simulation with big data analytics: A review

Greasley, A., & Edwards, J. S. (2021) · Journal of the Operational Research Society · doi:10.1080/01605682.2019.1678406 · Cited by 63

Stress wave evaluation by accelerometer and acoustic emission sensor for thermally modified wood classification using three types of neural networks

Nasir, V., Nourian, S., Avramidis, S., & Cool, J. (2019) · European Journal of Wood and Wood Products · doi:10.1007/s00107-018-1373-1 · Cited by 47

Untitled work

(2000) · doi:10.1109/kes.2000.885769 · Cited by 45

A Probabilistic Model for Information and Sensor Validation

Ibargüengoytia, P. H., Vadera, S., & Sucar, L. E. (2006) · The Computer Journal · doi:10.1093/comjnl/bxh142 · Cited by 43

Nicotine causes age-dependent changes in gene expression in the adolescent female rat brain

Polesskaya, O. O., Fryxell, K. J., Merchant, A. D., Locklear, L. L., Ker, K.-F., McDonald, C. G., Eppolito, A. K., Smith, L. N., Wheeler, T. L., & Smith, R. F. (2007) · Neurotoxicology and Teratology · doi:10.1016/j.ntt.2006.11.005 · Cited by 36

SimplePPT: A Simple Principal Tree Algorithm

Mao, Q., Yang, L., Wang, L., Goodison, S., & Sun, Y. (2015) · Proceedings of the 2015 SIAM International Conference on Data Mining · doi:10.1137/1.9781611974010.89 · Cited by 33

3D reconstruction of medical images from slices automatically landmarked with growing neural models

Angelopoulou, A., Psarrou, A., Garcia-Rodriguez, J., Orts-Escolano, S., Azorin-Lopez, J., & Revett, K. (2015) · Neurocomputing · doi:10.1016/j.neucom.2014.03.078 · Cited by 31

An approach to the analysis of SDSS spectroscopic outliers based on self-organizing maps: Designing the outlier analysis software package for the next Gaia survey

Fustes, D., Manteiga, M., Dafonte, C., Arcay, B., Ulla, A., Smith, K., Borrachero, R., & Sordo, R. (2013) · Astronomy & Astrophysics · doi:10.1051/0004-6361/201321445 · Cited by 31

Implementation and applications of tri-state self-organizing maps on FPGA

Appiah, K., Hunter, A., Dickinson, P., & Meng, H. (2012) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2012.2197077 · Cited by 28

Use of machine learning for classification of magnetocardiograms

Embrechts, M., Szymanski, B., Sternickel, K., Naenna, T., & Bragaspathi, R. (2004) · SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483) · doi:10.1109/icsmc.2003.1244608 · Cited by 27

Predicting language treatment response in bilingual aphasia using neural network-based patient models

Grasemann, U., Peñaloza, C., Dekhtyar, M., Miikkulainen, R., & Kiran, S. (2021) · Scientific Reports · doi:10.1038/s41598-021-89443-6 · Cited by 24

Introduction to Scientific Data Mining: Direct Kernel Methods and Applications

Embrechts, M. J., Szymanski, B., & Sternickel, K. (2004) · Computationally Intelligent Hybrid Systems · doi:10.1002/9780471683407.ch10 · Cited by 21

Occurrence and fate of perfluoroalkyl and polyfluoroalkyl substances (PFAS) in an urban aquifer located at the Besòs River Delta (Spain)

Sáez, C., Bautista, A., Nikolenko, O., Scheiber, L., Llorca, M., Jurado, A., Farré, M., & Pujades-Garnes, E. (2024) · Environmental Pollution · doi:10.1016/j.envpol.2024.124468 · Cited by 19

A developmental perspective on visual word recognition: New evidence and a self-organising model

Dufau, S., Lété, B., Touzet, C., Glotin, H., Ziegler, J. C., & Grainger, J. (2010) · European Journal of Cognitive Psychology · doi:10.1080/09541440903031230 · Cited by 19

Comparison of machine learning methods for classifying aphasic and non-aphasic speakers

Järvelin, A., & Juhola, M. (2011) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2011.02.015 · Cited by 18

Untitled work

(2014) · Cited by 17

Utilizing Artificial Intelligence to achieve a robust architecture for future robotic spacecraft

Jaekel, S., & Scholz, B. (2015) · 2015 IEEE Aerospace Conference · doi:10.1109/aero.2015.7119180 · Cited by 13

Exploring the orientations which characterise the likely public acceptance of low emission energy technologies

Carr-Cornish, S., Ashworth, P., Gardner, J., & Fraser, S. J. (2011) · Climatic Change · doi:10.1007/s10584-010-9964-9 · Cited by 12

Effectiveness Analysis of Rolling Bearing Fault Detectors Based On Self-Organising Kohonen Neural Network – A Case Study of PMSM Drive

Jankowska, K., & Ewert, P. (2021) · Power Electronics and Drives · doi:10.2478/pead-2021-0008 · Cited by 9

A Waterbody Typology Derived from Catchment Controls Using Self-Organising Maps

Heasley, E. L., Millington, J. D. A., Clifford, N. J., & Chadwick, M. A. (2019) · Water · doi:10.3390/w12010078 · Cited by 8

Brain Evolution as an Information Flow Designer: The Ground Architecture for Biological and Artificial General Intelligence

Shigeno, S. (2017) · Diversity and Commonality in Animals · doi:10.1007/978-4-431-56469-0_19 · Cited by 6

Self-organizing maps for multi-scale morphometric feature identification using shuttle radar topography mission data

Ehsani, A. H., & Quiel, F. (2009) · Geocarto International · doi:10.1080/10106040802642577 · Cited by 6