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Items: 1 to 20 of 34

1.
2.

Non-invasive prediction of non-alcoholic steatohepatitis in Japanese patients with morbid obesity by artificial intelligence using rule extraction technology.

Uehara D, Hayashi Y, Seki Y, Kakizaki S, Horiguchi N, Tojima H, Yamazaki Y, Sato K, Yasuda K, Yamada M, Uraoka T, Kasama K.

World J Hepatol. 2018 Dec 27;10(12):934-943. doi: 10.4254/wjh.v10.i12.934.

3.

Use of a Deep Belief Network for Small High-Level Abstraction Data Sets Using Artificial Intelligence with Rule Extraction.

Hayashi Y.

Neural Comput. 2018 Oct 12:1-18. doi: 10.1162/neco_a_01139. [Epub ahead of print]

PMID:
30314421
4.

Volumetric brain tumour detection from MRI using visual saliency.

Mitra S, Banerjee S, Hayashi Y.

PLoS One. 2017 Nov 2;12(11):e0187209. doi: 10.1371/journal.pone.0187209. eCollection 2017.

5.

Effect of an intensified multifactorial intervention on cardiovascular outcomes and mortality in type 2 diabetes (J-DOIT3): an open-label, randomised controlled trial.

Ueki K, Sasako T, Okazaki Y, Kato M, Okahata S, Katsuyama H, Haraguchi M, Morita A, Ohashi K, Hara K, Morise A, Izumi K, Ishizuka N, Ohashi Y, Noda M, Kadowaki T; J-DOIT3 Study Group.

Lancet Diabetes Endocrinol. 2017 Dec;5(12):951-964. doi: 10.1016/S2213-8587(17)30327-3. Epub 2017 Oct 24.

PMID:
29079252
6.

The number of microvascular complications is associated with an increased risk for severity of periodontitis in type 2 diabetes patients: Results of a multicenter hospital-based cross-sectional study.

Nitta H, Katagiri S, Nagasawa T, Izumi Y, Ishikawa I, Izumiyama H, Uchimura I, Kanazawa M, Chiba H, Matsuo A, Utsunomiya K, Tanabe H, Takei I, Asanami S, Kajio H, Ono T, Hayashi Y, Ueki K, Tsuji M, Kurachi Y, Yamanouchi T, Ichinokawa Y, Inokuchi T, Fukui A, Miyazaki S, Miyauchi T, Kawahara R, Ogiuchi H, Yoshioka N, Negishi J, Mori M, Mogi K, Saito Y, Tanzawa H, Nishikawa T, Takada N, Nanjo K, Morita N, Nakamura N, Kanamura N, Makino H, Nishimura F, Kobayashi K, Higuchi Y, Sakata T, Yanagisawa S, Tei C, Ando Y, Hanada N, Inoue S.

J Diabetes Investig. 2017 Sep;8(5):677-686. doi: 10.1111/jdi.12633. Epub 2017 May 8.

7.

A Novel GBM Saliency Detection Model Using Multi-Channel MRI.

Banerjee S, Mitra S, Shankar BU, Hayashi Y.

PLoS One. 2016 Jan 11;11(1):e0146388. doi: 10.1371/journal.pone.0146388. eCollection 2016.

8.

Differences in physician and patient perceptions about insulin therapy for management of type 2 diabetes: the DAWN Japan study.

Yoshioka N, Ishii H, Tajima N, Iwamoto Y; DAWN Japan group.

Curr Med Res Opin. 2014 Feb;30(2):177-83. doi: 10.1185/03007995.2013.855187. Epub 2013 Nov 5.

PMID:
24128339
9.

Evaluation of effects of multiple candidate genes (LEP, LEPR, MC4R, PIK3C3, and VRTN) on production traits in Duroc pigs.

Hirose K, Ito T, Fukawa K, Arakawa A, Mikawa S, Hayashi Y, Tanaka K.

Anim Sci J. 2014 Mar;85(3):198-206. doi: 10.1111/asj.12134. Epub 2013 Oct 15.

PMID:
24128088
10.

Association of swine vertnin (VRTN) gene with production traits in Duroc pigs improved using a closed nucleus breeding system.

Hirose K, Mikawa S, Okumura N, Noguchi G, Fukawa K, Kanaya N, Mikawa A, Arakawa A, Ito T, Hayashi Y, Tachibana F, Awata T.

Anim Sci J. 2013 Mar;84(3):213-21. doi: 10.1111/j.1740-0929.2012.01066.x. Epub 2012 Nov 2.

PMID:
23480701
11.

Comparison of various lipid variables as predictors of coronary heart disease in Japanese men and women with type 2 diabetes: subanalysis of the Japan Diabetes Complications Study.

Sone H, Tanaka S, Tanaka S, Iimuro S, Ishibashi S, Oikawa S, Shimano H, Katayama S, Ohashi Y, Akanuma Y, Yamada N; Japan Diabetes Complications Study Group.

Diabetes Care. 2012 May;35(5):1150-7. doi: 10.2337/dc11-1412. Epub 2012 Feb 14.

12.

Serum level of triglycerides is a potent risk factor comparable to LDL cholesterol for coronary heart disease in Japanese patients with type 2 diabetes: subanalysis of the Japan Diabetes Complications Study (JDCS).

Sone H, Tanaka S, Tanaka S, Iimuro S, Oida K, Yamasaki Y, Oikawa S, Ishibashi S, Katayama S, Ohashi Y, Akanuma Y, Yamada N; Japan Diabetes Complications Study Group.

J Clin Endocrinol Metab. 2011 Nov;96(11):3448-56. doi: 10.1210/jc.2011-0622. Epub 2011 Aug 24.

PMID:
21865372
13.

Association of an SNP marker in exon 24 of a class 3 phosphoinositide-3-kinase (PIK3C3) gene with production traits in Duroc pigs.

Hirose K, Takizawa T, Fukawa K, Ito T, Ueda M, Hayashi Y, Tanaka K.

Anim Sci J. 2011 Feb;82(1):46-51. doi: 10.1111/j.1740-0929.2010.00816.x. Epub 2010 Oct 4.

PMID:
21269358
14.

Genetic networks and soft computing.

Mitra S, Das R, Hayashi Y.

IEEE/ACM Trans Comput Biol Bioinform. 2011 Jan-Mar;8(1):94-107. doi: 10.1109/TCBB.2009.39. Review.

PMID:
21071800
15.

Influence of acute aerobic exercise on adiponectin oligomer concentrations in middle-aged abdominally obese men.

Numao S, Katayama Y, Hayashi Y, Matsuo T, Tanaka K.

Metabolism. 2011 Feb;60(2):186-94. doi: 10.1016/j.metabol.2009.12.011. Epub 2010 Jan 27.

PMID:
20102772
16.

Sex differences in substrate oxidation during aerobic exercise in obese men and postmenopausal obese women.

Numao S, Hayashi Y, Katayama Y, Matsuo T, Tanaka K.

Metabolism. 2009 Sep;58(9):1312-9. doi: 10.1016/j.metabol.2009.04.015. Epub 2009 Jun 18.

PMID:
19501865
17.

Acute effects of aerobic exercise on cognitive function in older adults.

Kamijo K, Hayashi Y, Sakai T, Yahiro T, Tanaka K, Nishihira Y.

J Gerontol B Psychol Sci Soc Sci. 2009 May;64(3):356-63. doi: 10.1093/geronb/gbp030. Epub 2009 Apr 10.

PMID:
19363089
18.

Greedy rule generation from discrete data and its use in neural network rule extraction.

Odajima K, Hayashi Y, Tianxia G, Setiono R.

Neural Netw. 2008 Sep;21(7):1020-8. doi: 10.1016/j.neunet.2008.01.003. Epub 2008 Mar 23.

PMID:
18442894
19.

Plasma fat concentration increases in visceral fat obese men during high-intensity endurance exercise.

Numao S, Hayashi Y, Katayama Y, Matsuo T, Tomita T, Ohkawara K, Nakata Y, Okura T, Tanaka K.

Obes Res Clin Pract. 2007 Dec;1(4):223-90. doi: 10.1016/j.orcp.2007.10.004.

PMID:
24351587
20.

Knowledge-based generation of diagnostic hypotheses and therapy recommendations for toxoplasma infections in pregnancy.

Kopecky D, Adlassnig KP, Prusa AR, Hayde M, Hayashi Y, Panzenböck B, Rappelsberger A, Pollak A.

Med Inform Internet Med. 2007 Sep;32(3):199-214.

PMID:
17701826

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