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Items: 1 to 20 of 101

1.

Towards pan-genome read alignment to improve variation calling.

Valenzuela D, Norri T, Välimäki N, Pitkänen E, Mäkinen V.

BMC Genomics. 2018 May 9;19(Suppl 2):87. doi: 10.1186/s12864-018-4465-8.

2.

A safe and complete algorithm for metagenomic assembly.

Obscura Acosta N, Mäkinen V, Tomescu AI.

Algorithms Mol Biol. 2018 Feb 7;13:3. doi: 10.1186/s13015-018-0122-7. eCollection 2018.

3.

Multivariable Analysis of Nutritional and Socio-Economic Profiles Shows Differences in Incident Anemia for Northern and Southern Jiangsu in China.

Mutter S, Casey AE, Zhen S, Shi Z, Mäkinen VP.

Nutrients. 2017 Oct 21;9(10). pii: E1153. doi: 10.3390/nu9101153.

4.

Variant genotyping with gap filling.

Walve R, Salmela L, Mäkinen V.

PLoS One. 2017 Sep 8;12(9):e0184608. doi: 10.1371/journal.pone.0184608. eCollection 2017.

5.

Metabolic profiling of polycystic ovary syndrome reveals interactions with abdominal obesity.

Couto Alves A, Valcarcel B, Mäkinen VP, Morin-Papunen L, Sebert S, Kangas AJ, Soininen P, Das S, De Iorio M, Coin L, Ala-Korpela M, Järvelin MR, Franks S.

Int J Obes (Lond). 2017 Sep;41(9):1331-1340. doi: 10.1038/ijo.2017.126. Epub 2017 May 26.

6.

Data-driven metabolic subtypes predict future adverse events in individuals with type 1 diabetes.

Lithovius R, Toppila I, Harjutsalo V, Forsblom C, Groop PH, Mäkinen VP; FinnDiane Study Group.

Diabetologia. 2017 Jul;60(7):1234-1243. doi: 10.1007/s00125-017-4273-8. Epub 2017 Apr 24.

PMID:
28439641
7.

A framework for space-efficient read clustering in metagenomic samples.

Alanko J, Cunial F, Belazzougui D, Mäkinen V.

BMC Bioinformatics. 2017 Mar 14;18(Suppl 3):59. doi: 10.1186/s12859-017-1466-6.

8.

Evaluating approaches to find exon chains based on long reads.

Kuosmanen A, Norri T, Mäkinen V.

Brief Bioinform. 2018 May 1;19(3):404-414. doi: 10.1093/bib/bbw137.

9.

Metabolic profiling of pregnancy: cross-sectional and longitudinal evidence.

Wang Q, Würtz P, Auro K, Mäkinen VP, Kangas AJ, Soininen P, Tiainen M, Tynkkynen T, Jokelainen J, Santalahti K, Salmi M, Blankenberg S, Zeller T, Viikari J, Kähönen M, Lehtimäki T, Salomaa V, Perola M, Jalkanen S, Järvelin MR, Raitakari OT, Kettunen J, Lawlor DA, Ala-Korpela M.

BMC Med. 2016 Dec 13;14(1):205.

10.

Digital footprints: facilitating large-scale environmental psychiatric research in naturalistic settings through data from everyday technologies.

Bidargaddi N, Musiat P, Makinen VP, Ermes M, Schrader G, Licinio J.

Mol Psychiatry. 2017 Feb;22(2):164-169. doi: 10.1038/mp.2016.224. Epub 2016 Dec 6.

11.

Challenges in conducting genetic analyses based on data-driven classification of major depressive disorder.

Mäkinen VP.

Mol Psychiatry. 2018 Mar;23(3):494. doi: 10.1038/mp.2016.205. Epub 2016 Nov 8. No abstract available.

PMID:
27821869
12.

Mergeomics: multidimensional data integration to identify pathogenic perturbations to biological systems.

Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD, Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Mäkinen VP, Yang X.

BMC Genomics. 2016 Nov 4;17(1):874.

13.

Mergeomics: a web server for identifying pathological pathways, networks, and key regulators via multidimensional data integration.

Arneson D, Bhattacharya A, Shu L, Mäkinen VP, Yang X.

BMC Genomics. 2016 Sep 9;17(1):722. doi: 10.1186/s12864-016-3057-8.

14.

Metabolomics of aging requires large-scale longitudinal studies with replication.

Mäkinen VP, Ala-Korpela M.

Proc Natl Acad Sci U S A. 2016 Jun 21;113(25):E3470. doi: 10.1073/pnas.1607062113. Epub 2016 Jun 14. No abstract available.

15.

Gap Filling as Exact Path Length Problem.

Salmela L, Sahlin K, Mäkinen V, Tomescu AI.

J Comput Biol. 2016 May;23(5):347-61. doi: 10.1089/cmb.2015.0197. Epub 2016 Mar 9.

PMID:
26959081
16.

Identification and validation of N-acetyltransferase 2 as an insulin sensitivity gene.

Knowles JW, Xie W, Zhang Z, Chennamsetty I, Assimes TL, Paananen J, Hansson O, Pankow J, Goodarzi MO, Carcamo-Orive I, Morris AP, Chen YD, Mäkinen VP, Ganna A, Mahajan A, Guo X, Abbasi F, Greenawalt DM, Lum P, Molony C, Lind L, Lindgren C, Raffel LJ, Tsao PS; RISC (Relationship between Insulin Sensitivity and Cardiovascular Disease) Consortium; EUGENE (European Network on Functional Genomics of Type Diabetes) Study; GUARDIAN (Genetics UndeRlying DIAbetes in HispaNics) Consortium; SAPPHIRe (Stanford Asian and Pacific Program for Hypertension and Insulin Resistance) Study, Schadt EE, Rotter JI, Sinaiko A, Reaven G, Yang X, Hsiung CA, Groop L, Cordell HJ, Laakso M, Hao K, Ingelsson E, Frayling TM, Weedon MN, Walker M, Quertermous T.

J Clin Invest. 2016 Jan;126(1):403. doi: 10.1172/JCI85921. Epub 2016 Jan 4. No abstract available.

17.

Explaining a Weighted DAG with Few Paths for Solving Genome-Guided Multi-Assembly.

Tomescu AI, Gagie T, Popa A, Rizzi R, Kuosmanen A, Mäkinen V.

IEEE/ACM Trans Comput Biol Bioinform. 2015 Nov-Dec;12(6):1345-54. doi: 10.1109/TCBB.2015.2418753.

PMID:
26671806
18.

Oxygen deteriorates arterial function in type 1 diabetes.

Gordin D, Bernardi L, Rosengård-Bärlund M, Mäkinen VP, Soro-Paavonen A, Forsblom C, Sandelin A, Groop PH.

Acta Diabetol. 2016 Jun;53(3):349-57. doi: 10.1007/s00592-015-0775-3. Epub 2015 Jul 11.

PMID:
26159114
19.

Repeat- and error-aware comparison of deletions.

Wittler R, Marschall T, Schönhuth A, Mäkinen V.

Bioinformatics. 2015 Sep 15;31(18):2947-54. doi: 10.1093/bioinformatics/btv304. Epub 2015 May 15.

PMID:
25979471
20.

Systems Genetics Analysis of Genome-Wide Association Study Reveals Novel Associations Between Key Biological Processes and Coronary Artery Disease.

Ghosh S, Vivar J, Nelson CP, Willenborg C, Segrè AV, Mäkinen VP, Nikpay M, Erdmann J, Blankenberg S, O'Donnell C, März W, Laaksonen R, Stewart AF, Epstein SE, Shah SH, Granger CB, Hazen SL, Kathiresan S, Reilly MP, Yang X, Quertermous T, Samani NJ, Schunkert H, Assimes TL, McPherson R.

Arterioscler Thromb Vasc Biol. 2015 Jul;35(7):1712-22. doi: 10.1161/ATVBAHA.115.305513. Epub 2015 May 14.

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