-
1
-
-
84896284039
-
The present and future role of microfluidics in biomedical research
-
Sackmann, E.K., et al. The present and future role of microfluidics in biomedical research. Nature 507 (2014), 181–189.
-
(2014)
Nature
, vol.507
, pp. 181-189
-
-
Sackmann, E.K.1
-
2
-
-
85024409143
-
Single-cell genome sequencing at ultra-high-throughput with microfluidic droplet barcoding
-
Lan, F., et al. Single-cell genome sequencing at ultra-high-throughput with microfluidic droplet barcoding. Nat. Biotechnol. 35 (2017), 640–646.
-
(2017)
Nat. Biotechnol.
, vol.35
, pp. 640-646
-
-
Lan, F.1
-
3
-
-
85010710363
-
Single-cell barcoding and sequencing using droplet microfluidics
-
Zilionis, R., et al. Single-cell barcoding and sequencing using droplet microfluidics. Nat. Protoc. 12 (2016), 44–73.
-
(2016)
Nat. Protoc.
, vol.12
, pp. 44-73
-
-
Zilionis, R.1
-
4
-
-
85012906748
-
Single cell proteomics in biomedicine: high-dimensional data acquisition, visualization, and analysis
-
Su, Y., et al. Single cell proteomics in biomedicine: high-dimensional data acquisition, visualization, and analysis. Proteomics, 17, 2017, 1600267.
-
(2017)
Proteomics
, vol.17
-
-
Su, Y.1
-
5
-
-
85020532249
-
Microfluidics as a strategic player to decipher single-cell omics?
-
Caen, O., et al. Microfluidics as a strategic player to decipher single-cell omics?. Trends Biotechnol. 35 (2017), 713–727.
-
(2017)
Trends Biotechnol.
, vol.35
, pp. 713-727
-
-
Caen, O.1
-
6
-
-
85033670876
-
Microfluidics for combating antimicrobial resistance
-
Liu, Z., et al. Microfluidics for combating antimicrobial resistance. Trends Biotechnol. 35 (2017), 1129–1139.
-
(2017)
Trends Biotechnol.
, vol.35
, pp. 1129-1139
-
-
Liu, Z.1
-
7
-
-
33644777646
-
Lab-on-a-chip: microfluidics in drug discovery
-
Dittrich, P.S., Manz, A., Lab-on-a-chip: microfluidics in drug discovery. Nat. Rev. Drug Discov. 5 (2006), 210–218.
-
(2006)
Nat. Rev. Drug Discov.
, vol.5
, pp. 210-218
-
-
Dittrich, P.S.1
Manz, A.2
-
8
-
-
1242346127
-
Integrating advanced functionality in a microfabricated high-throughput fluorescent-activated cell sorter
-
Wolff, A., et al. Integrating advanced functionality in a microfabricated high-throughput fluorescent-activated cell sorter. Lab Chip 3 (2003), 22–27.
-
(2003)
Lab Chip
, vol.3
, pp. 22-27
-
-
Wolff, A.1
-
9
-
-
84861216623
-
Hydrodynamic stretching of single cells for large population mechanical phenotyping
-
Gossett, D.R., et al. Hydrodynamic stretching of single cells for large population mechanical phenotyping. Proc. Natl. Acad. Sci. U. S. A. 109 (2012), 7630–7635.
-
(2012)
Proc. Natl. Acad. Sci. U. S. A.
, vol.109
, pp. 7630-7635
-
-
Gossett, D.R.1
-
10
-
-
84877057770
-
Single-cell analysis and sorting using droplet-based microfluidics
-
Mazutis, L., et al. Single-cell analysis and sorting using droplet-based microfluidics. Nat. Protoc. 8 (2013), 870–891.
-
(2013)
Nat. Protoc.
, vol.8
, pp. 870-891
-
-
Mazutis, L.1
-
11
-
-
84992388359
-
High-throughput measurement of single-cell growth rates using serial microfluidic mass sensor arrays
-
Cermak, N., et al. High-throughput measurement of single-cell growth rates using serial microfluidic mass sensor arrays. Nat. Biotechnol. 34 (2016), 1052–1059.
-
(2016)
Nat. Biotechnol.
, vol.34
, pp. 1052-1059
-
-
Cermak, N.1
-
12
-
-
84929573436
-
Quantifying the volume of single cells continuously using a microfluidic pressure-driven trap with media exchange
-
Riordon, J., et al. Quantifying the volume of single cells continuously using a microfluidic pressure-driven trap with media exchange. Biomicrofluidics, 8, 2014, 011101.
-
(2014)
Biomicrofluidics
, vol.8
, pp. 011101
-
-
Riordon, J.1
-
13
-
-
84900315204
-
Microfluidic high-throughput culturing of single cells for selection based on extracellular metabolite production or consumption
-
Wang, B.L., et al. Microfluidic high-throughput culturing of single cells for selection based on extracellular metabolite production or consumption. Nat. Biotechnol. 32 (2014), 473–478.
-
(2014)
Nat. Biotechnol.
, vol.32
, pp. 473-478
-
-
Wang, B.L.1
-
14
-
-
59349111076
-
Microfluidic control of cell pairing and fusion
-
Skelley, A., et al. Microfluidic control of cell pairing and fusion. Nat. Methods 6 (2009), 147–152.
-
(2009)
Nat. Methods
, vol.6
, pp. 147-152
-
-
Skelley, A.1
-
15
-
-
37549002543
-
Isolation of rare circulating tumour cells in cancer patients by microchip technology
-
Nagrath, S., et al. Isolation of rare circulating tumour cells in cancer patients by microchip technology. Nature 450 (2007), 1235–1239.
-
(2007)
Nature
, vol.450
, pp. 1235-1239
-
-
Nagrath, S.1
-
16
-
-
84934437818
-
A microfluidic device for label-free, physical capture of circulating tumor cell clusters
-
Sarioglu, A.F., et al. A microfluidic device for label-free, physical capture of circulating tumor cell clusters. Nat. Methods 12 (2015), 685–691.
-
(2015)
Nat. Methods
, vol.12
, pp. 685-691
-
-
Sarioglu, A.F.1
-
17
-
-
0242417549
-
Passively driven integrated microfluidic system for separation of motile sperm
-
Cho, B.S., et al. Passively driven integrated microfluidic system for separation of motile sperm. Anal. Chem. 75 (2003), 1671–1675.
-
(2003)
Anal. Chem.
, vol.75
, pp. 1671-1675
-
-
Cho, B.S.1
-
18
-
-
84894259696
-
Rapid selection of sperm with high DNA integrity
-
Nosrati, R., et al. Rapid selection of sperm with high DNA integrity. Lab Chip 14 (2014), 1142–1150.
-
(2014)
Lab Chip
, vol.14
, pp. 1142-1150
-
-
Nosrati, R.1
-
19
-
-
84925291393
-
Microfluidics for sperm research
-
Knowlton, S.M., et al. Microfluidics for sperm research. Trends Biotechnol. 33 (2015), 221–229.
-
(2015)
Trends Biotechnol.
, vol.33
, pp. 221-229
-
-
Knowlton, S.M.1
-
20
-
-
85035334552
-
Microfluidics for sperm analysis and selection
-
Nosrati, R., et al. Microfluidics for sperm analysis and selection. Nat. Rev. Urol. 14 (2017), 707–730.
-
(2017)
Nat. Rev. Urol.
, vol.14
, pp. 707-730
-
-
Nosrati, R.1
-
21
-
-
85023177328
-
Droplet control technologies for microfluidic high throughput screening (μHTS)
-
Sesen, M., et al. Droplet control technologies for microfluidic high throughput screening (μHTS). Lab Chip 17 (2017), 2372–2394.
-
(2017)
Lab Chip
, vol.17
, pp. 2372-2394
-
-
Sesen, M.1
-
22
-
-
79955457407
-
Zebrafish embryo development in a microfluidic flow-through system
-
Wielhouwer, E.M., et al. Zebrafish embryo development in a microfluidic flow-through system. Lab Chip 11 (2011), 1815–1824.
-
(2011)
Lab Chip
, vol.11
, pp. 1815-1824
-
-
Wielhouwer, E.M.1
-
23
-
-
84857162647
-
Fish and Chips: a microfluidic perfusion platform for monitoring zebrafish development
-
Choudhury, D., et al. Fish and Chips: a microfluidic perfusion platform for monitoring zebrafish development. Lab Chip 12 (2012), 892–900.
-
(2012)
Lab Chip
, vol.12
, pp. 892-900
-
-
Choudhury, D.1
-
24
-
-
46249092235
-
Automated on-chip rapid microscopy, phenotyping and sorting of C. elegans
-
Chung, K., et al. Automated on-chip rapid microscopy, phenotyping and sorting of C. elegans. Nat. Methods 5 (2008), 637–643.
-
(2008)
Nat. Methods
, vol.5
, pp. 637-643
-
-
Chung, K.1
-
25
-
-
79551572775
-
A microfluidic array for large-scale ordering and orientation of embryos
-
Chung, K., et al. A microfluidic array for large-scale ordering and orientation of embryos. Nat. Methods 8 (2011), 171–176.
-
(2011)
Nat. Methods
, vol.8
, pp. 171-176
-
-
Chung, K.1
-
26
-
-
84930630277
-
Deep learning
-
LeCun, Y., et al. Deep learning. Nature 521 (2015), 436–444.
-
(2015)
Nature
, vol.521
, pp. 436-444
-
-
LeCun, Y.1
-
27
-
-
84879854889
-
Representation learning: a review and new perspectives
-
Bengio, Y., et al. Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35 (2013), 1798–1828.
-
(2013)
IEEE Trans. Pattern Anal. Mach. Intell.
, vol.35
, pp. 1798-1828
-
-
Bengio, Y.1
-
28
-
-
84876231242
-
ImageNet classification with deep convolutional neural networks
-
Krizhevsky, A., et al. ImageNet classification with deep convolutional neural networks. Adv. Neural Inform. Process. Syst. 25 (2012), 1097–1105.
-
(2012)
Adv. Neural Inform. Process. Syst.
, vol.25
, pp. 1097-1105
-
-
Krizhevsky, A.1
-
29
-
-
84933585162
-
Very deep convolutional networks for large-scale image recognition
-
Published online September 4, 2014.
-
Simonyan, K., Zisserman, A., Very deep convolutional networks for large-scale image recognition. arXiv, 2014 Published online September 4, 2014. https://arxiv.org/abs/1409.1556v6.
-
(2014)
arXiv
-
-
Simonyan, K.1
Zisserman, A.2
-
32
-
-
85039172448
-
Effective approaches to attention-based neural machine translation
-
Published online August 17, 2015.
-
Luong, M.-T., et al. Effective approaches to attention-based neural machine translation. arXiv, 2015 Published online August 17, 2015. https://arxiv.org/abs/1508.04025.
-
(2015)
arXiv
-
-
Luong, M.-T.1
-
33
-
-
84928547704
-
Sequence to sequence learning with neural networks
-
Sutskever, I., et al. Sequence to sequence learning with neural networks. Adv. Neural Inform. Process. Syst. 4 (2014), 3104–3112.
-
(2014)
Adv. Neural Inform. Process. Syst.
, vol.4
, pp. 3104-3112
-
-
Sutskever, I.1
-
34
-
-
84921940378
-
Learning phrase representations using RNN encoder–decoder for statistical machine translation
-
Published online June 3, 2014.
-
Cho, K., et al. Learning phrase representations using RNN encoder–decoder for statistical machine translation. arXiv, 2014 Published online June 3, 2014. https://arxiv.org/abs/1406.1078.
-
(2014)
arXiv
-
-
Cho, K.1
-
37
-
-
85050637249
-
Bag of tricks for efficient text classification
-
Published online July 6, 2016.
-
Joulin, A., et al. Bag of tricks for efficient text classification. arXiv, 2016 Published online July 6, 2016. https://arxiv.org/abs/1607.01759.
-
(2016)
arXiv
-
-
Joulin, A.1
-
38
-
-
84943153743
-
Reading text in the wild with convolutional neural networks
-
Jaderberg, M., et al. Reading text in the wild with convolutional neural networks. Int. J. Comput. Vis. 116 (2016), 1–20.
-
(2016)
Int. J. Comput. Vis.
, vol.116
, pp. 1-20
-
-
Jaderberg, M.1
-
39
-
-
84958257565
-
Predicting effects of noncoding variants with deep learning-based sequence model
-
Zhou, J., Troyanskaya, O.G., Predicting effects of noncoding variants with deep learning-based sequence model. Nat. Methods 12 (2015), 931–934.
-
(2015)
Nat. Methods
, vol.12
, pp. 931-934
-
-
Zhou, J.1
Troyanskaya, O.G.2
-
40
-
-
84980022857
-
Deep learning for computational biology
-
Angermueller, C., et al. Deep learning for computational biology. Mol. Syst. Biol., 12, 2016, 878.
-
(2016)
Mol. Syst. Biol.
, vol.12
, pp. 878
-
-
Angermueller, C.1
-
41
-
-
85041308556
-
Machine learning to detect signatures of disease in liquid biopsies – a user's guide
-
Ko, J., et al. Machine learning to detect signatures of disease in liquid biopsies – a user's guide. Lab Chip 18 (2018), 395–405.
-
(2018)
Lab Chip
, vol.18
, pp. 395-405
-
-
Ko, J.1
-
42
-
-
85017546094
-
High-throughput, label-free, single-cell, microalgal lipid screening by machine-learning-equipped optofluidic time-stretch quantitative phase microscopy
-
Guo, B., et al. High-throughput, label-free, single-cell, microalgal lipid screening by machine-learning-equipped optofluidic time-stretch quantitative phase microscopy. Cytometry A 91 (2017), 494–502.
-
(2017)
Cytometry A
, vol.91
, pp. 494-502
-
-
Guo, B.1
-
43
-
-
85035364200
-
Combining machine learning and nanofluidic technology to diagnose pancreatic cancer using exosomes
-
Ko, J., et al. Combining machine learning and nanofluidic technology to diagnose pancreatic cancer using exosomes. ACS Nano 11 (2017), 11182–11193.
-
(2017)
ACS Nano
, vol.11
, pp. 11182-11193
-
-
Ko, J.1
-
44
-
-
85028336483
-
Label-free, high-throughput holographic screening and enumeration of tumor cells in blood
-
Singh, D.K., et al. Label-free, high-throughput holographic screening and enumeration of tumor cells in blood. Lab Chip 17 (2017), 2920–2932.
-
(2017)
Lab Chip
, vol.17
, pp. 2920-2932
-
-
Singh, D.K.1
-
45
-
-
84994756552
-
Machine learning based single-frame super-resolution processing for lensless blood cell counting
-
Huang, X., et al. Machine learning based single-frame super-resolution processing for lensless blood cell counting. Sensors, 16, 2016, 1836.
-
(2016)
Sensors
, vol.16
, pp. 1836
-
-
Huang, X.1
-
46
-
-
85015332197
-
Microdroplet size prediction in microfluidic systems via artificial neural network modeling for water-in-oil emulsion formulation
-
Mahdi, Y., Daoud, K., Microdroplet size prediction in microfluidic systems via artificial neural network modeling for water-in-oil emulsion formulation. J. Dispers. Sci. Technol. 38 (2017), 1501–1508.
-
(2017)
J. Dispers. Sci. Technol.
, vol.38
, pp. 1501-1508
-
-
Mahdi, Y.1
Daoud, K.2
-
47
-
-
84960984309
-
Deep learning in label-free cell classification
-
Chen, C.L., et al. Deep learning in label-free cell classification. Sci. Rep., 6, 2016, 21471.
-
(2016)
Sci. Rep.
, vol.6
, pp. 21471
-
-
Chen, C.L.1
-
48
-
-
85029505529
-
Real-time image processing for microscopy-based label-free imaging flow cytometry in a microfluidic chip
-
Heo, Y.J., et al. Real-time image processing for microscopy-based label-free imaging flow cytometry in a microfluidic chip. Sci. Rep., 7, 2017, 21471.
-
(2017)
Sci. Rep.
, vol.7
, pp. 21471
-
-
Heo, Y.J.1
-
49
-
-
84999836246
-
Deep learning automates the quantitative analysis of individual cells in live-cell imaging experiments
-
Van Valen, D.A., et al. Deep learning automates the quantitative analysis of individual cells in live-cell imaging experiments. PLoS Comput. Biol., 12, 2016, e1005177.
-
(2016)
PLoS Comput. Biol.
, vol.12
-
-
Van Valen, D.A.1
-
50
-
-
85009237688
-
Cytopathological image analysis using deep-learning networks in microfluidic microscopy
-
Gopakumar, G., et al. Cytopathological image analysis using deep-learning networks in microfluidic microscopy. J. Opt. Soc. Am. A Opt. Image Sci. Vis., 34, 2017, 111.
-
(2017)
J. Opt. Soc. Am. A Opt. Image Sci. Vis.
, vol.34
, pp. 111
-
-
Gopakumar, G.1
-
51
-
-
85017419380
-
Deep learning for flow sculpting: insights into efficient learning using scientific simulation data
-
Stoecklein, D., et al. Deep learning for flow sculpting: insights into efficient learning using scientific simulation data. Sci. Rep, 7, 2017, 46368.
-
(2017)
Sci. Rep
, vol.7
, pp. 46368
-
-
Stoecklein, D.1
-
52
-
-
85030178612
-
Deep learning for single-molecule science
-
Albrecht, T., et al. Deep learning for single-molecule science. Nanotechnology, 28, 2017, 423001.
-
(2017)
Nanotechnology
, vol.28
, pp. 423001
-
-
Albrecht, T.1
-
53
-
-
85032037004
-
Opportunities and obstacles for deep learning in biology and medicine
-
Published online May 28, 2017
-
Ching, T., et al. Opportunities and obstacles for deep learning in biology and medicine. bioRxiv, 2017, 10.1101/142760 Published online May 28, 2017.
-
(2017)
bioRxiv
-
-
Ching, T.1
-
54
-
-
84968861400
-
Applications of deep learning in biomedicine
-
Mamoshina, P., et al. Applications of deep learning in biomedicine. Mol. Pharm. 13 (2016), 1445–1454.
-
(2016)
Mol. Pharm.
, vol.13
, pp. 1445-1454
-
-
Mamoshina, P.1
-
55
-
-
0025503558
-
Backpropagation through time: what it does and how to do it
-
Werbos, P.J., Backpropagation through time: what it does and how to do it. Proc. IEEE 78 (1990), 1550–1560.
-
(1990)
Proc. IEEE
, vol.78
, pp. 1550-1560
-
-
Werbos, P.J.1
-
56
-
-
84872497956
-
Machine learning approach for automated screening of malaria parasite using light microscopic images
-
Das, D.K., et al. Machine learning approach for automated screening of malaria parasite using light microscopic images. Micron 45 (2013), 97–106.
-
(2013)
Micron
, vol.45
, pp. 97-106
-
-
Das, D.K.1
-
57
-
-
85029661023
-
Automated analysis of individual sperm cells using stain-free interferometric phase microscopy and machine learning
-
Mirsky, S.K., et al. Automated analysis of individual sperm cells using stain-free interferometric phase microscopy and machine learning. Cytometry A 91A (2017), 893–900.
-
(2017)
Cytometry A
, vol.91A
, pp. 893-900
-
-
Mirsky, S.K.1
-
58
-
-
85035364200
-
Combining machine learning and nanofluidic technology to diagnose pancreatic cancer using exosomes
-
Ko, J., et al. Combining machine learning and nanofluidic technology to diagnose pancreatic cancer using exosomes. ACS Nano 11 (2017), 11182–11193.
-
(2017)
ACS Nano
, vol.11
, pp. 11182-11193
-
-
Ko, J.1
-
59
-
-
84997174196
-
Deep phenotyping unveils hidden traits and genetic relations in subtle mutants
-
San-Miguel, A., et al. Deep phenotyping unveils hidden traits and genetic relations in subtle mutants. Nat. Commun., 7, 2016, 12990.
-
(2016)
Nat. Commun.
, vol.7
, pp. 12990
-
-
San-Miguel, A.1
-
60
-
-
85021745740
-
How not to drown in data: a guide for biomaterial engineers
-
Vasilevich, A.S., et al. How not to drown in data: a guide for biomaterial engineers. Trends Biotechnol. 35 (2017), 743–755.
-
(2017)
Trends Biotechnol.
, vol.35
, pp. 743-755
-
-
Vasilevich, A.S.1
-
61
-
-
85054455201
-
Use of deep learning for characterization of microfluidic soft sensors
-
Han, S., et al. Use of deep learning for characterization of microfluidic soft sensors. IEEE Robot. Autom. Lett. 3 (2018), 873–880.
-
(2018)
IEEE Robot. Autom. Lett.
, vol.3
, pp. 873-880
-
-
Han, S.1
-
62
-
-
85020469555
-
DeepNano: deep recurrent neural networks for base calling in MinION nanopore reads
-
Boža, V., et al. DeepNano: deep recurrent neural networks for base calling in MinION nanopore reads. PLoS One, 12, 2017, e0178751.
-
(2017)
PLoS One
, vol.12
-
-
Boža, V.1
-
63
-
-
77952112749
-
Using buoyant mass to measure the growth of single cells
-
Godin, M., et al. Using buoyant mass to measure the growth of single cells. Nat. Methods 7 (2010), 387–390.
-
(2010)
Nat. Methods
, vol.7
, pp. 387-390
-
-
Godin, M.1
-
64
-
-
85039174318
-
Exploring the limits of language modeling
-
Published online February 7, 2016.
-
Jozefowicz, R., et al. Exploring the limits of language modeling. arXiv, 2016 Published online February 7, 2016. https://arxiv.org/abs/1602.02410.
-
(2016)
arXiv
-
-
Jozefowicz, R.1
-
65
-
-
85039863846
-
On the prediction of DNA-binding proteins only from primary sequences: a deep learning approach
-
Qu, Y.-H., et al. On the prediction of DNA-binding proteins only from primary sequences: a deep learning approach. PLoS One, 12, 2017, e0188129.
-
(2017)
PLoS One
, vol.12
-
-
Qu, Y.-H.1
-
67
-
-
85041430720
-
Single cells make big data: new challenges and opportunities in transcriptomics
-
Angerer, P., et al. Single cells make big data: new challenges and opportunities in transcriptomics. Curr. Opin. Syst. Biol. 4 (2017), 85–91.
-
(2017)
Curr. Opin. Syst. Biol.
, vol.4
, pp. 85-91
-
-
Angerer, P.1
-
68
-
-
85054453053
-
Massive single-cell RNA-seq analysis and imputation via deep learning
-
Published online July 26 2018
-
Deng, Y., et al. Massive single-cell RNA-seq analysis and imputation via deep learning. bioRxiv, 2018, 10.1101/315556 Published online July 26 2018.
-
(2018)
bioRxiv
-
-
Deng, Y.1
-
69
-
-
85042487006
-
Deep learning for biology
-
Webb, S., Deep learning for biology. Nature 554 (2018), 555–557.
-
(2018)
Nature
, vol.554
, pp. 555-557
-
-
Webb, S.1
-
70
-
-
85047752833
-
Next-generation machine learning for biological networks
-
Camacho, D.M., et al. Next-generation machine learning for biological networks. Cell 173 (2018), 1581–1592.
-
(2018)
Cell
, vol.173
, pp. 1581-1592
-
-
Camacho, D.M.1
-
71
-
-
84969504939
-
Multi-omics of single cells: strategies and applications
-
Bock, C., et al. Multi-omics of single cells: strategies and applications. Trends Biotechnol. 34 (2016), 605–608.
-
(2016)
Trends Biotechnol.
, vol.34
, pp. 605-608
-
-
Bock, C.1
-
72
-
-
0002263996
-
Convolutional networks for images, speech, and time series
-
LeCun, Y., Bengio, Y., Convolutional networks for images, speech, and time series. Handb. Brain Theory Neural Netw., 3361, 1995, 1995.
-
(1995)
Handb. Brain Theory Neural Netw.
, vol.3361
, pp. 1995
-
-
LeCun, Y.1
Bengio, Y.2
-
73
-
-
84856686379
-
Adaptive deconvolutional networks for mid and high level feature learning
-
IEEE
-
Zeiler, M.D. et al. (2011) Adaptive deconvolutional networks for mid and high level feature learning. In 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2018–2025, IEEE.
-
(2011)
2011 IEEE International Conference on Computer Vision (ICCV)
, pp. 2018-2025
-
-
Zeiler, M.D.1
-
74
-
-
85041441983
-
Visual estimation of bacterial growth level in microfluidic culture systems
-
Kim, K., et al. Visual estimation of bacterial growth level in microfluidic culture systems. Sensors, 18, 2018, 447.
-
(2018)
Sensors
, vol.18
, pp. 447
-
-
Kim, K.1
-
75
-
-
85043275350
-
AxonDeepSeg: automatic axon and myelin segmentation from microscopy data using convolutional neural networks
-
Zaimi, A., et al. AxonDeepSeg: automatic axon and myelin segmentation from microscopy data using convolutional neural networks. Sci. Rep., 8, 2018, 3816.
-
(2018)
Sci. Rep.
, vol.8
, pp. 3816
-
-
Zaimi, A.1
-
78
-
-
85033697420
-
SegNet: a deep convolutional encoder–decoder architecture for image segmentation
-
Badrinarayanan, V., et al. SegNet: a deep convolutional encoder–decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39 (2017), 2481–2495.
-
(2017)
IEEE Trans. Pattern Anal. Mach. Intell.
, vol.39
, pp. 2481-2495
-
-
Badrinarayanan, V.1
-
79
-
-
84986261676
-
Efficient piecewise training of deep structured models for semantic segmentation
-
IEEE
-
Lin, G. et al. (2016) Efficient piecewise training of deep structured models for semantic segmentation. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3194–3203, IEEE.
-
(2016)
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
, pp. 3194-3203
-
-
Lin, G.1
-
81
-
-
84986250328
-
ParseNet: looking wider to see better
-
Published online June 15, 2015.
-
Liu, W., et al. ParseNet: looking wider to see better. arXiv, 2015 Published online June 15, 2015. https://arxiv.org/abs/1506.04579.
-
(2015)
arXiv
-
-
Liu, W.1
-
82
-
-
85024089027
-
DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs
-
Published online June 2, 2016.
-
Chen, L.-C., et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. arXiv, 2016 Published online June 2, 2016. https://arxiv.org/abs/1606.00915.
-
(2016)
arXiv
-
-
Chen, L.-C.1
-
83
-
-
85013149570
-
Prospective identification of hematopoietic lineage choice by deep learning
-
Buggenthin, F., et al. Prospective identification of hematopoietic lineage choice by deep learning. Nat. Methods 14 (2017), 403–406.
-
(2017)
Nat. Methods
, vol.14
, pp. 403-406
-
-
Buggenthin, F.1
-
84
-
-
85046485207
-
Phenotypic antimicrobial susceptibility testing with deep learning video microscopy
-
Yu, H., et al. Phenotypic antimicrobial susceptibility testing with deep learning video microscopy. Anal. Chem. 90 (2018), 6314–6322.
-
(2018)
Anal. Chem.
, vol.90
, pp. 6314-6322
-
-
Yu, H.1
-
85
-
-
84905754409
-
Microfluidic organs-on-chips
-
Bhatia, S.N., Ingber, D.E., Microfluidic organs-on-chips. Nat. Biotechnol. 32 (2014), 760–772.
-
(2014)
Nat. Biotechnol.
, vol.32
, pp. 760-772
-
-
Bhatia, S.N.1
Ingber, D.E.2
-
86
-
-
85031413199
-
Organ-on-a-chip platforms: a convergence of advanced materials, cells, and microscale technologies
-
Ahadian, S., et al. Organ-on-a-chip platforms: a convergence of advanced materials, cells, and microscale technologies. Adv. Healthc. Mater., 7, 2018, 1700506.
-
(2018)
Adv. Healthc. Mater.
, vol.7
-
-
Ahadian, S.1
-
87
-
-
85037041276
-
An oral cavity squamous cell carcinoma quantitative histomorphometric-based image classifier of nuclear morphology can risk stratify patients for disease-specific survival
-
Lu, C., et al. An oral cavity squamous cell carcinoma quantitative histomorphometric-based image classifier of nuclear morphology can risk stratify patients for disease-specific survival. Mod. Pathol. 30 (2017), 1655–1665.
-
(2017)
Mod. Pathol.
, vol.30
, pp. 1655-1665
-
-
Lu, C.1
-
88
-
-
85047079043
-
Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction
-
Faust, K., et al. Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction. BMC Bioinform., 19, 2018, 173.
-
(2018)
BMC Bioinform.
, vol.19
, pp. 173
-
-
Faust, K.1
-
89
-
-
85044195316
-
Interconnected microphysiological systems for quantitative biology and pharmacology studies
-
Edington, C.D., et al. Interconnected microphysiological systems for quantitative biology and pharmacology studies. Sci. Rep., 8, 2018, 4530.
-
(2018)
Sci. Rep.
, vol.8
, pp. 4530
-
-
Edington, C.D.1
-
90
-
-
84960154893
-
Biodegradable scaffold with built-in vasculature for organ-on-a-chip engineering and direct surgical anastomosis
-
Zhang, B., et al. Biodegradable scaffold with built-in vasculature for organ-on-a-chip engineering and direct surgical anastomosis. Nat. Mater. 15 (2016), 669–678.
-
(2016)
Nat. Mater.
, vol.15
, pp. 669-678
-
-
Zhang, B.1
-
91
-
-
84946222016
-
Synthetic biology lures Silicon Valley investors
-
Check Hayden, E., Synthetic biology lures Silicon Valley investors. Nature, 527, 2015, 19.
-
(2015)
Nature
, vol.527
, pp. 19
-
-
Check Hayden, E.1
-
92
-
-
85012967324
-
Neural networks for the prediction of organic chemistry reactions
-
Wei, J.N., et al. Neural networks for the prediction of organic chemistry reactions. ACS Cent. Sci. 2 (2016), 725–732.
-
(2016)
ACS Cent. Sci.
, vol.2
, pp. 725-732
-
-
Wei, J.N.1
-
93
-
-
85041012344
-
A platform for high-throughput assessments of environmental multistressors
-
Nguyen, B., et al. A platform for high-throughput assessments of environmental multistressors. Adv. Sci. (Weinh.), 5, 2018, 1700677.
-
(2018)
Adv. Sci. (Weinh.)
, vol.5
-
-
Nguyen, B.1
-
94
-
-
85028560598
-
A microfluidics-based in situ chemotaxis assay to study the behaviour of aquatic microbial communities
-
Lambert, B.S., et al. A microfluidics-based in situ chemotaxis assay to study the behaviour of aquatic microbial communities. Nat. Microbiol. 2 (2017), 1344–1349.
-
(2017)
Nat. Microbiol.
, vol.2
, pp. 1344-1349
-
-
Lambert, B.S.1
-
95
-
-
85021654643
-
Turning the page: advancing paper-based microfluidics for broad diagnostic application
-
Gong, M.M., Sinton, D., Turning the page: advancing paper-based microfluidics for broad diagnostic application. Chem. Rev. 117 (2017), 8447–8480.
-
(2017)
Chem. Rev.
, vol.117
, pp. 8447-8480
-
-
Gong, M.M.1
Sinton, D.2
-
96
-
-
84905482334
-
Smartphone technology can be transformative to the deployment of lab-on-chip diagnostics
-
Erickson, D., et al. Smartphone technology can be transformative to the deployment of lab-on-chip diagnostics. Lab Chip 14 (2014), 3159–3164.
-
(2014)
Lab Chip
, vol.14
, pp. 3159-3164
-
-
Erickson, D.1
-
97
-
-
85038104955
-
Microfluidics based point-of-care diagnostics
-
Pandey, C.M., et al. Microfluidics based point-of-care diagnostics. Biotechnol. J., 13, 2017, 1700047.
-
(2017)
Biotechnol. J.
, vol.13
-
-
Pandey, C.M.1
-
98
-
-
85019091951
-
Ensuring food safety: quality monitoring using microfluidics
-
Weng, X., Neethirajan, S., Ensuring food safety: quality monitoring using microfluidics. Trends Food Sci. Technol. 65 (2017), 10–22.
-
(2017)
Trends Food Sci. Technol.
, vol.65
, pp. 10-22
-
-
Weng, X.1
Neethirajan, S.2
-
99
-
-
84973486576
-
Rapid, low-cost detection of Zika virus using programmable biomolecular components
-
Pardee, K., et al. Rapid, low-cost detection of Zika virus using programmable biomolecular components. Cell 165 (2016), 1255–1266.
-
(2016)
Cell
, vol.165
, pp. 1255-1266
-
-
Pardee, K.1
-
100
-
-
84968649810
-
Convolutional neural networks for medical image analysis: full training or fine tuning?
-
Tajbakhsh, N., et al. Convolutional neural networks for medical image analysis: full training or fine tuning?. IEEE Trans. Med. Imaging 35 (2016), 1299–1312.
-
(2016)
IEEE Trans. Med. Imaging
, vol.35
, pp. 1299-1312
-
-
Tajbakhsh, N.1
-
101
-
-
85054461995
-
-
Texas Instruments, Inc. Miniaturized electronic circuits, US3138743A
-
Kilby, J.S. Texas Instruments, Inc. Miniaturized electronic circuits, US3138743A.
-
-
-
Kilby, J.S.1
-
102
-
-
0031173078
-
The Intel 4004 microprocessor: what constituted invention?
-
Aspray, W., The Intel 4004 microprocessor: what constituted invention?. IEEE Ann. Hist. Comput. 19 (1997), 4–15.
-
(1997)
IEEE Ann. Hist. Comput.
, vol.19
, pp. 4-15
-
-
Aspray, W.1
-
103
-
-
0018653907
-
A gas chromatographic air analyzer fabricated on a silicon wafer
-
Terry, S.C., et al. A gas chromatographic air analyzer fabricated on a silicon wafer. IEEE Trans. Electron Devices 26 (1979), 1880–1886.
-
(1979)
IEEE Trans. Electron Devices
, vol.26
, pp. 1880-1886
-
-
Terry, S.C.1
-
104
-
-
0032403465
-
Rapid prototyping of microfluidic systems in poly(dimethylsiloxane)
-
Duffy, D.C., et al. Rapid prototyping of microfluidic systems in poly(dimethylsiloxane). Anal. Chem. 70 (1998), 4974–4984.
-
(1998)
Anal. Chem.
, vol.70
, pp. 4974-4984
-
-
Duffy, D.C.1
-
105
-
-
11144273669
-
The perceptron: a probabilistic model for information storage and organization in the brain
-
Rosenblatt, F., The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev., 65, 1958, 386.
-
(1958)
Psychol. Rev.
, vol.65
, pp. 386
-
-
Rosenblatt, F.1
-
106
-
-
0022471098
-
Learning representations by back-propagating errors
-
Rumelhart, D.E., et al. Learning representations by back-propagating errors. Nature 323 (1986), 533–536.
-
(1986)
Nature
, vol.323
, pp. 533-536
-
-
Rumelhart, D.E.1
-
107
-
-
77956729294
-
Serial order: a parallel distributed processing approach
-
(Donahoe, J.W. and Dorsel, P., eds), Elsevier
-
Jordan, M.I. (1997) Serial order: a parallel distributed processing approach. In Advances in Psychology (Vol. 121) (Donahoe, J.W. and Dorsel, P., eds), pp. 471–495, Elsevier.
-
(1997)
Advances in Psychology
, vol.121
, pp. 471-495
-
-
Jordan, M.I.1
-
108
-
-
85021238093
-
TensorFlow: large-scale machine learning on heterogeneous distributed systems
-
Published online March 14, 2016.
-
Abadi, M., et al. TensorFlow: large-scale machine learning on heterogeneous distributed systems. arXiv, 2016 Published online March 14, 2016. https://arxiv.org/abs/1603.04467.
-
(2016)
arXiv
-
-
Abadi, M.1
-
109
-
-
84958291921
-
TensorFlow: biology's gateway to deep learning?
-
Rampasek, L., Goldenberg, A., TensorFlow: biology's gateway to deep learning?. Cell Syst. 2 (2016), 12–14.
-
(2016)
Cell Syst.
, vol.2
, pp. 12-14
-
-
Rampasek, L.1
Goldenberg, A.2
-
110
-
-
38949126980
-
What are artificial neural networks?
-
Krogh, A., What are artificial neural networks?. Nat. Biotechnol. 26 (2008), 195–197.
-
(2008)
Nat. Biotechnol.
, vol.26
, pp. 195-197
-
-
Krogh, A.1
|