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Items: 1 to 50 of 59

1.

Accounting for proximal variants improves neoantigen prediction.

Hundal J, Kiwala S, Feng YY, Liu CJ, Govindan R, Chapman WC, Uppaluri R, Swamidass SJ, Griffith OL, Mardis ER, Griffith M.

Nat Genet. 2019 Jan;51(1):175-179. doi: 10.1038/s41588-018-0283-9. Epub 2018 Dec 3.

PMID:
30510237
2.

A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data.

Ainscough BJ, Barnell EK, Ronning P, Campbell KM, Wagner AH, Fehniger TA, Dunn GP, Uppaluri R, Govindan R, Rohan TE, Griffith M, Mardis ER, Swamidass SJ, Griffith OL.

Nat Genet. 2018 Dec;50(12):1735-1743. doi: 10.1038/s41588-018-0257-y. Epub 2018 Nov 5.

PMID:
30397337
3.

Standard operating procedure for somatic variant refinement of sequencing data with paired tumor and normal samples.

Barnell EK, Ronning P, Campbell KM, Krysiak K, Ainscough BJ, Sheta LM, Pema SP, Schmidt AD, Richters M, Cotto KC, Danos AM, Ramirez C, Skidmore ZL, Spies NC, Hundal J, Sediqzad MS, Kunisaki J, Gomez F, Trani L, Matlock M, Wagner AH, Swamidass SJ, Griffith M, Griffith OL.

Genet Med. 2018 Oct 5. doi: 10.1038/s41436-018-0278-z. [Epub ahead of print]

PMID:
30287923
4.

Lamisil (terbinafine) toxicity: Determining pathways to bioactivation through computational and experimental approaches.

Barnette DA, Davis MA, Dang NL, Pidugu AS, Hughes T, Swamidass SJ, Boysen G, Miller GP.

Biochem Pharmacol. 2018 Oct;156:10-21. doi: 10.1016/j.bcp.2018.07.043. Epub 2018 Aug 2.

PMID:
30076845
5.

Deep Learning Global Glomerulosclerosis in Transplant Kidney Frozen Sections.

Marsh JN, Matlock MK, Kudose S, Liu TC, Stappenbeck TS, Gaut JP, Swamidass SJ.

IEEE Trans Med Imaging. 2018 Dec;37(12):2718-2728. doi: 10.1109/TMI.2018.2851150. Epub 2018 Jun 27.

PMID:
29994669
6.

Modeling Small-Molecule Reactivity Identifies Promiscuous Bioactive Compounds.

Matlock MK, Hughes TB, Dahlin JL, Swamidass SJ.

J Chem Inf Model. 2018 Aug 27;58(8):1483-1500. doi: 10.1021/acs.jcim.8b00104. Epub 2018 Jul 23.

PMID:
29990427
7.

Opportunities and obstacles for deep learning in biology and medicine.

Ching T, Himmelstein DS, Beaulieu-Jones BK, Kalinin AA, Do BT, Way GP, Ferrero E, Agapow PM, Zietz M, Hoffman MM, Xie W, Rosen GL, Lengerich BJ, Israeli J, Lanchantin J, Woloszynek S, Carpenter AE, Shrikumar A, Xu J, Cofer EM, Lavender CA, Turaga SC, Alexandari AM, Lu Z, Harris DJ, DeCaprio D, Qi Y, Kundaje A, Peng Y, Wiley LK, Segler MHS, Boca SM, Swamidass SJ, Huang A, Gitter A, Greene CS.

J R Soc Interface. 2018 Apr;15(141). pii: 20170387. doi: 10.1098/rsif.2017.0387. Review.

8.

Learning a Local-Variable Model of Aromatic and Conjugated Systems.

Matlock MK, Dang NL, Swamidass SJ.

ACS Cent Sci. 2018 Jan 24;4(1):52-62. doi: 10.1021/acscentsci.7b00405. Epub 2018 Jan 3.

9.

Computationally Assessing the Bioactivation of Drugs by N-Dealkylation.

Dang NL, Hughes TB, Miller GP, Swamidass SJ.

Chem Res Toxicol. 2018 Feb 19;31(2):68-80. doi: 10.1021/acs.chemrestox.7b00191. Epub 2018 Feb 6.

PMID:
29355304
10.

The diversity and disparity in biomedical informatics (DDBI) workshop.

Southerland WM, Swamidass SJ, Payne PRO, Wiley L, Williams-DeVane C.

Pac Symp Biocomput. 2018;23:614-617.

11.

BEESEM: estimation of binding energy models using HT-SELEX data.

Ruan S, Swamidass SJ, Stormo GD.

Bioinformatics. 2017 Aug 1;33(15):2288-2295. doi: 10.1093/bioinformatics/btx191.

12.

Computational Approach to Structural Alerts: Furans, Phenols, Nitroaromatics, and Thiophenes.

Dang NL, Hughes TB, Miller GP, Swamidass SJ.

Chem Res Toxicol. 2017 Apr 17;30(4):1046-1059. doi: 10.1021/acs.chemrestox.6b00336. Epub 2017 Mar 14.

13.

Deep Learning to Predict the Formation of Quinone Species in Drug Metabolism.

Hughes TB, Swamidass SJ.

Chem Res Toxicol. 2017 Feb 20;30(2):642-656. doi: 10.1021/acs.chemrestox.6b00385. Epub 2017 Feb 2.

14.

Erratum: Inhibition of DNA Methyltransferases Blocks Mutant Huntingtin-Induced Neurotoxicity.

Pan Y, Daito T, Sasaki Y, Chung YH, Xing X, Pondugula S, Swamidass SJ, Wang T, Kim AH, Yano H.

Sci Rep. 2016 Sep 21;6:33766. doi: 10.1038/srep33766. No abstract available.

15.

Unsupervised detection of cancer driver mutations with parsimony-guided learning.

Kumar RD, Swamidass SJ, Bose R.

Nat Genet. 2016 Oct;48(10):1288-94. doi: 10.1038/ng.3658. Epub 2016 Sep 12.

16.

Modeling Reactivity to Biological Macromolecules with a Deep Multitask Network.

Hughes TB, Dang NL, Miller GP, Swamidass SJ.

ACS Cent Sci. 2016 Aug 24;2(8):529-37. doi: 10.1021/acscentsci.6b00162. Epub 2016 Jul 29.

17.

Inhibition of DNA Methyltransferases Blocks Mutant Huntingtin-Induced Neurotoxicity.

Pan Y, Daito T, Sasaki Y, Chung YH, Xing X, Pondugula S, Swamidass SJ, Wang T, Kim AH, Yano H.

Sci Rep. 2016 Aug 12;6:31022. doi: 10.1038/srep31022. Erratum in: Sci Rep. 2016 Sep 21;6:33766.

18.

Open Source Drug Discovery with the Malaria Box Compound Collection for Neglected Diseases and Beyond.

Van Voorhis WC, Adams JH, Adelfio R, Ahyong V, Akabas MH, Alano P, Alday A, Alemán Resto Y, Alsibaee A, Alzualde A, Andrews KT, Avery SV, Avery VM, Ayong L, Baker M, Baker S, Ben Mamoun C, Bhatia S, Bickle Q, Bounaadja L, Bowling T, Bosch J, Boucher LE, Boyom FF, Brea J, Brennan M, Burton A, Caffrey CR, Camarda G, Carrasquilla M, Carter D, Belen Cassera M, Chih-Chien Cheng K, Chindaudomsate W, Chubb A, Colon BL, Colón-López DD, Corbett Y, Crowther GJ, Cowan N, D'Alessandro S, Le Dang N, Delves M, DeRisi JL, Du AY, Duffy S, Abd El-Salam El-Sayed S, Ferdig MT, Fernández Robledo JA, Fidock DA, Florent I, Fokou PV, Galstian A, Gamo FJ, Gokool S, Gold B, Golub T, Goldgof GM, Guha R, Guiguemde WA, Gural N, Guy RK, Hansen MA, Hanson KK, Hemphill A, Hooft van Huijsduijnen R, Horii T, Horrocks P, Hughes TB, Huston C, Igarashi I, Ingram-Sieber K, Itoe MA, Jadhav A, Naranuntarat Jensen A, Jensen LT, Jiang RH, Kaiser A, Keiser J, Ketas T, Kicka S, Kim S, Kirk K, Kumar VP, Kyle DE, Lafuente MJ, Landfear S, Lee N, Lee S, Lehane AM, Li F, Little D, Liu L, Llinás M, Loza MI, Lubar A, Lucantoni L, Lucet I, Maes L, Mancama D, Mansour NR, March S, McGowan S, Medina Vera I, Meister S, Mercer L, Mestres J, Mfopa AN, Misra RN, Moon S, Moore JP, Morais Rodrigues da Costa F, Müller J, Muriana A, Nakazawa Hewitt S, Nare B, Nathan C, Narraidoo N, Nawaratna S, Ojo KK, Ortiz D, Panic G, Papadatos G, Parapini S, Patra K, Pham N, Prats S, Plouffe DM, Poulsen SA, Pradhan A, Quevedo C, Quinn RJ, Rice CA, Abdo Rizk M, Ruecker A, St Onge R, Salgado Ferreira R, Samra J, Robinett NG, Schlecht U, Schmitt M, Silva Villela F, Silvestrini F, Sinden R, Smith DA, Soldati T, Spitzmüller A, Stamm SM, Sullivan DJ, Sullivan W, Suresh S, Suzuki BM, Suzuki Y, Swamidass SJ, Taramelli D, Tchokouaha LR, Theron A, Thomas D, Tonissen KF, Townson S, Tripathi AK, Trofimov V, Udenze KO, Ullah I, Vallieres C, Vigil E, Vinetz JM, Voong Vinh P, Vu H, Watanabe NA, Weatherby K, White PM, Wilks AF, Winzeler EA, Wojcik E, Wree M, Wu W, Yokoyama N, Zollo PH, Abla N, Blasco B, Burrows J, Laleu B, Leroy D, Spangenberg T, Wells T, Willis PA.

PLoS Pathog. 2016 Jul 28;12(7):e1005763. doi: 10.1371/journal.ppat.1005763. eCollection 2016 Jul.

19.

A simple model predicts UGT-mediated metabolism.

Dang NL, Hughes TB, Krishnamurthy V, Swamidass SJ.

Bioinformatics. 2016 Oct 15;32(20):3183-3189. Epub 2016 Jun 20.

20.

Modeling Epoxidation of Drug-like Molecules with a Deep Machine Learning Network.

Hughes TB, Miller GP, Swamidass SJ.

ACS Cent Sci. 2015 Jul 22;1(4):168-80. doi: 10.1021/acscentsci.5b00131. Epub 2015 Jun 9.

21.

Education: Initiatives to bridge faith and science.

Swamidass SJ.

Nature. 2015 Jul 30;523(7562):531. doi: 10.1038/523531b. No abstract available.

PMID:
26223616
22.

Statistically identifying tumor suppressors and oncogenes from pan-cancer genome-sequencing data.

Kumar RD, Searleman AC, Swamidass SJ, Griffith OL, Bose R.

Bioinformatics. 2015 Nov 15;31(22):3561-8. doi: 10.1093/bioinformatics/btv430. Epub 2015 Jul 25.

23.

Improved Prediction of CYP-Mediated Metabolism with Chemical Fingerprints.

Zaretzki J, Boehm KM, Swamidass SJ.

J Chem Inf Model. 2015 May 26;55(5):972-82. doi: 10.1021/ci5005652. Epub 2015 May 8.

PMID:
25871613
24.

A survey of current trends in computational drug repositioning.

Li J, Zheng S, Chen B, Butte AJ, Swamidass SJ, Lu Z.

Brief Bioinform. 2016 Jan;17(1):2-12. doi: 10.1093/bib/bbv020. Epub 2015 Mar 31. Review.

25.

Site of reactivity models predict molecular reactivity of diverse chemicals with glutathione.

Hughes TB, Miller GP, Swamidass SJ.

Chem Res Toxicol. 2015 Apr 20;28(4):797-809. doi: 10.1021/acs.chemrestox.5b00017. Epub 2015 Mar 16.

26.

Securely measuring the overlap between private datasets with cryptosets.

Swamidass SJ, Matlock M, Rozenblit L.

PLoS One. 2015 Feb 25;10(2):e0117898. doi: 10.1371/journal.pone.0117898. eCollection 2015.

27.

Extending P450 site-of-metabolism models with region-resolution data.

Zaretzki JM, Browning MR, Hughes TB, Swamidass SJ.

Bioinformatics. 2015 Jun 15;31(12):1966-73. doi: 10.1093/bioinformatics/btv100. Epub 2015 Feb 19.

PMID:
25697821
28.

Subcellular localization and Ser-137 phosphorylation regulate tumor-suppressive activity of profilin-1.

Diamond MI, Cai S, Boudreau A, Carey CJ Jr, Lyle N, Pappu RV, Swamidass SJ, Bissell M, Piwnica-Worms H, Shao J.

J Biol Chem. 2015 Apr 3;290(14):9075-86. doi: 10.1074/jbc.M114.619874. Epub 2015 Feb 13.

29.

XenoSite server: a web-available site of metabolism prediction tool.

Matlock MK, Hughes TB, Swamidass SJ.

Bioinformatics. 2015 Apr 1;31(7):1136-7. doi: 10.1093/bioinformatics/btu761. Epub 2014 Nov 18.

PMID:
25411327
30.

Bigger data, collaborative tools and the future of predictive drug discovery.

Ekins S, Clark AM, Swamidass SJ, Litterman N, Williams AJ.

J Comput Aided Mol Des. 2014 Oct;28(10):997-1008. doi: 10.1007/s10822-014-9762-y. Epub 2014 Jun 19.

31.

Combined Analysis of Phenotypic and Target-Based Screening in Assay Networks.

Swamidass SJ, Schillebeeckx CN, Matlock M, Hurle MR, Agarwal P.

J Biomol Screen. 2014 Jun;19(5):782-90. doi: 10.1177/1087057114523068. Epub 2014 Feb 21.

PMID:
24563424
32.

Drug repositioning from the combined evaluation of phenotypic and target-based screening.

Swamidass SJ, Agarwal P.

AMIA Jt Summits Transl Sci Proc. 2013 Mar 18;2013:161. eCollection 2013. No abstract available.

PMID:
24303328
33.

Sharing chemical relationships does not reveal structures.

Matlock M, Swamidass SJ.

J Chem Inf Model. 2014 Jan 27;54(1):37-48. doi: 10.1021/ci400399a. Epub 2013 Dec 16.

PMID:
24289228
34.

XenoSite: accurately predicting CYP-mediated sites of metabolism with neural networks.

Zaretzki J, Matlock M, Swamidass SJ.

J Chem Inf Model. 2013 Dec 23;53(12):3373-83. doi: 10.1021/ci400518g. Epub 2013 Nov 23.

PMID:
24224933
35.

Scaffold network generator: a tool for mining molecular structures.

Matlock MK, Zaretzki JM, Swamidass SJ.

Bioinformatics. 2013 Oct 15;29(20):2655-6. doi: 10.1093/bioinformatics/btt448. Epub 2013 Aug 5.

PMID:
23918250
36.

Managing missing measurements in small-molecule screens.

Browning MR, Calhoun BT, Swamidass SJ.

J Comput Aided Mol Des. 2013 May;27(5):469-78. doi: 10.1007/s10822-013-9642-x. Epub 2013 Apr 13.

PMID:
23585219
37.

Using economic optimization to design high-throughput screens.

Swamidass SJ.

Future Med Chem. 2013 Jan;5(1):9-11. doi: 10.4155/fmc.12.186. No abstract available.

PMID:
23256806
38.

RS-WebPredictor: a server for predicting CYP-mediated sites of metabolism on drug-like molecules.

Zaretzki J, Bergeron C, Huang TW, Rydberg P, Swamidass SJ, Breneman CM.

Bioinformatics. 2013 Feb 15;29(4):497-8. doi: 10.1093/bioinformatics/bts705. Epub 2012 Dec 14.

39.

Accounting for noise when clustering biological data.

Sloutsky R, Jimenez N, Swamidass SJ, Naegle KM.

Brief Bioinform. 2013 Jul;14(4):423-36. doi: 10.1093/bib/bbs057. Epub 2012 Oct 14.

PMID:
23063929
40.

Automatically detecting workflows in PubChem.

Calhoun BT, Browning MR, Chen BR, Bittker JA, Swamidass SJ.

J Biomol Screen. 2012 Sep;17(8):1071-9. doi: 10.1177/1087057112449054. Epub 2012 Jun 12.

PMID:
22693105
41.

Utility-aware screening with clique-oriented prioritization.

Swamidass SJ, Calhoun BT, Bittker JA, Bodycombe NE, Clemons PA.

J Chem Inf Model. 2012 Jan 23;52(1):29-37. doi: 10.1021/ci2003285. Epub 2011 Dec 20.

42.

Probabilistic Substructure Mining From Small-Molecule Screens.

Ranu S, Calhoun BT, Singh AK, Swamidass SJ.

Mol Inform. 2011 Sep;30(9):809-15. doi: 10.1002/minf.201100058. Epub 2011 Aug 4.

PMID:
27467413
43.

Mining small-molecule screens to repurpose drugs.

Swamidass SJ.

Brief Bioinform. 2011 Jul;12(4):327-35. doi: 10.1093/bib/bbr028. Epub 2011 Jun 29.

PMID:
21715466
44.

Enhancing the rate of scaffold discovery with diversity-oriented prioritization.

Swamidass SJ, Calhoun BT, Bittker JA, Bodycombe NE, Clemons PA.

Bioinformatics. 2011 Aug 15;27(16):2271-8. doi: 10.1093/bioinformatics/btr369. Epub 2011 Jun 17.

45.

An economic framework to prioritize confirmatory tests after a high-throughput screen.

Swamidass SJ, Bittker JA, Bodycombe NE, Ryder SP, Clemons PA.

J Biomol Screen. 2010 Jul;15(6):680-6. doi: 10.1177/1087057110372803. Epub 2010 Jun 14.

46.

A CROC stronger than ROC: measuring, visualizing and optimizing early retrieval.

Swamidass SJ, Azencott CA, Daily K, Baldi P.

Bioinformatics. 2010 May 15;26(10):1348-56. doi: 10.1093/bioinformatics/btq140. Epub 2010 Apr 7.

47.

Influence relevance voting: an accurate and interpretable virtual high throughput screening method.

Swamidass SJ, Azencott CA, Lin TW, Gramajo H, Tsai SC, Baldi P.

J Chem Inf Model. 2009 Apr;49(4):756-66. doi: 10.1021/ci8004379.

48.

Large scale study of multiple-molecule queries.

Nasr RJ, Swamidass SJ, Baldi PF.

J Cheminform. 2009 Jun 4;1(1):7. doi: 10.1186/1758-2946-1-7.

49.

Discovery of power-laws in chemical space.

Benz RW, Swamidass SJ, Baldi P.

J Chem Inf Model. 2008 Jun;48(6):1138-51. doi: 10.1021/ci700353m. Epub 2008 Jun 4.

PMID:
18522387
50.

Lossless compression of chemical fingerprints using integer entropy codes improves storage and retrieval.

Baldi P, Benz RW, Hirschberg DS, Swamidass SJ.

J Chem Inf Model. 2007 Nov-Dec;47(6):2098-109. Epub 2007 Oct 30.

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