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Items: 1 to 50 of 110

1.

Bit-parallel sequence-to-graph alignment.

Rautiainen M, Mäkinen V, Marschall T.

Bioinformatics. 2019 Mar 9. pii: btz162. doi: 10.1093/bioinformatics/btz162. [Epub ahead of print]

PMID:
30851095
2.

Body Mass Index in Children Validated by Metabolic and Fat Mass Profiling.

Mäkinen VP.

J Am Coll Cardiol. 2018 Dec 18;72(24):3155-3157. doi: 10.1016/j.jacc.2018.10.016. No abstract available.

PMID:
30545454
3.

Improving outgroup attitudes in schools: First steps toward a teacher-led vicarious contact intervention.

Liebkind K, Mäkinen V, Jasinskaja-Lahti I, Renvik TA, Solheim EF.

Scand J Psychol. 2019 Feb;60(1):77-86. doi: 10.1111/sjop.12505. Epub 2018 Nov 29.

PMID:
30497107
4.

Discovery of potential causative mutations in human coding and noncoding genome with the interactive software BasePlayer.

Katainen R, Donner I, Cajuso T, Kaasinen E, Palin K, Mäkinen V, Aaltonen LA, Pitkänen E.

Nat Protoc. 2018 Nov;13(11):2580-2600. doi: 10.1038/s41596-018-0052-3.

PMID:
30323186
5.

Genetic variation within endolysosomal system is associated with late-onset Alzheimer's disease.

Gao S, Casey AE, Sargeant TJ, Mäkinen VP.

Brain. 2018 Sep 1;141(9):2711-2720. doi: 10.1093/brain/awy197.

PMID:
30124770
6.

MIPUP: minimum perfect unmixed phylogenies for multi-sampled tumors via branchings and ILP.

Husić E, Li X, Hujdurović A, Mehine M, Rizzi R, Mäkinen V, Milanič M, Tomescu AI.

Bioinformatics. 2019 Mar 1;35(5):769-777. doi: 10.1093/bioinformatics/bty683.

7.

Hardness of Covering Alignment: Phase Transition in Post-Sequence Genomics.

Rizzi R, Cairo M, Makinen V, Tomescu AI, Valenzuela D.

IEEE/ACM Trans Comput Biol Bioinform. 2019 Jan-Feb;16(1):23-30. doi: 10.1109/TCBB.2018.2831691. Epub 2018 Apr 30.

PMID:
29994032
8.

Numero: a statistical framework to define multivariable subgroups in complex population-based datasets.

Gao S, Mutter S, Casey A, Mäkinen VP.

Int J Epidemiol. 2018 Jun 26. doi: 10.1093/ije/dyy113. [Epub ahead of print]

PMID:
29947762
9.

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.

10.

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.

11.

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.

12.

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.

13.

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.

14.

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
15.

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.

16.

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.

17.

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.

18.

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.

19.

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
20.

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.

21.

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.

22.

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.

23.

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
24.

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.

25.

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
26.

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
27.

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
28.

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.

29.

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; EUGENE2 (European Network on Functional Genomics of Type 2 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. 2015 Apr;125(4):1739-51. doi: 10.1172/JCI74692. Epub 2015 Mar 23. Erratum in: J Clin Invest. 2016 Jan;126(1):403. Chennemsetty, Indumathi [corrected to Chennamsetty, Indumathi].

30.

Recombination-aware alignment of diploid individuals.

Mäkinen V, Valenzuela D.

BMC Genomics. 2014;15 Suppl 6:S15. doi: 10.1186/1471-2164-15-S6-S15. Epub 2014 Oct 17.

31.

SNV-PPILP: refined SNV calling for tumor data using perfect phylogenies and ILP.

van Rens KE, Mäkinen V, Tomescu AI.

Bioinformatics. 2015 Apr 1;31(7):1133-5. doi: 10.1093/bioinformatics/btu755. Epub 2014 Nov 13.

PMID:
25398608
32.

On the complexity of Minimum Path Cover with Subpath Constraints for multi-assembly.

Rizzi R, Tomescu AI, Mäkinen V.

BMC Bioinformatics. 2014;15 Suppl 9:S5. doi: 10.1186/1471-2105-15-S9-S5. Epub 2014 Sep 10.

33.

The Glanville fritillary genome retains an ancient karyotype and reveals selective chromosomal fusions in Lepidoptera.

Ahola V, Lehtonen R, Somervuo P, Salmela L, Koskinen P, Rastas P, Välimäki N, Paulin L, Kvist J, Wahlberg N, Tanskanen J, Hornett EA, Ferguson LC, Luo S, Cao Z, de Jong MA, Duplouy A, Smolander OP, Vogel H, McCoy RC, Qian K, Chong WS, Zhang Q, Ahmad F, Haukka JK, Joshi A, Salojärvi J, Wheat CW, Grosse-Wilde E, Hughes D, Katainen R, Pitkänen E, Ylinen J, Waterhouse RM, Turunen M, Vähärautio A, Ojanen SP, Schulman AH, Taipale M, Lawson D, Ukkonen E, Mäkinen V, Goldsmith MR, Holm L, Auvinen P, Frilander MJ, Hanski I.

Nat Commun. 2014 Sep 5;5:4737. doi: 10.1038/ncomms5737.

34.

High-fat meals induce systemic cytokine release without evidence of endotoxemia-mediated cytokine production from circulating monocytes or myeloid dendritic cells.

Fogarty CL, Nieminen JK, Peräneva L, Lassenius MI, Ahola AJ, Taskinen MR, Jauhiainen M, Kirveskari J, Pussinen P, Hörkkö S, Mäkinen VP, Gordin D, Forsblom C, Groop PH, Vaarala O, Lehto M.

Acta Diabetol. 2015 Apr;52(2):315-22. doi: 10.1007/s00592-014-0641-8. Epub 2014 Sep 3.

PMID:
25182144
35.

Integrative genomics reveals novel molecular pathways and gene networks for coronary artery disease.

Mäkinen VP, Civelek M, Meng Q, Zhang B, Zhu J, Levian C, Huan T, Segrè AV, Ghosh S, Vivar J, Nikpay M, Stewart AF, Nelson CP, Willenborg C, Erdmann J, Blakenberg S, O'Donnell CJ, März W, Laaksonen R, Epstein SE, Kathiresan S, Shah SH, Hazen SL, Reilly MP; Coronary ARtery DIsease Genome-Wide Replication And Meta-Analysis (CARDIoGRAM) Consortium, Lusis AJ, Samani NJ, Schunkert H, Quertermous T, McPherson R, Yang X, Assimes TL.

PLoS Genet. 2014 Jul 17;10(7):e1004502. doi: 10.1371/journal.pgen.1004502. eCollection 2014 Jul.

36.

Patients with type 1 diabetes show signs of vascular dysfunction in response to multiple high-fat meals.

Lassenius MI, Mäkinen VP, Fogarty CL, Peräneva L, Jauhiainen M, Pussinen PJ, Taskinen MR, Kirveskari J, Vaarala O, Nieminen JK, Hörkkö S, Kangas AJ, Soininen P, Ala-Korpela M, Gordin D, Ahola AJ, Forsblom C, Groop PH, Lehto M.

Nutr Metab (Lond). 2014 Jun 13;11:28. doi: 10.1186/1743-7075-11-28. eCollection 2014.

37.

Different lipid variables predict incident coronary artery disease in patients with type 1 diabetes with or without diabetic nephropathy: the FinnDiane study.

Tolonen N, Forsblom C, Mäkinen VP, Harjutsalo V, Gordin D, Feodoroff M, Sandholm N, Thorn LM, Wadén J, Taskinen MR, Groop PH; FinnDiane Study Group.

Diabetes Care. 2014 Aug;37(8):2374-82. doi: 10.2337/dc13-2873. Epub 2014 May 30.

PMID:
24879842
38.

Novel genetic susceptibility loci for diabetic end-stage renal disease identified through robust naive Bayes classification.

Sambo F, Malovini A, Sandholm N, Stavarachi M, Forsblom C, Mäkinen VP, Harjutsalo V, Lithovius R, Gordin D, Parkkonen M, Saraheimo M, Thorn LM, Tolonen N, Wadén J, He B, Osterholm AM, Tuomilehto J, Lajer M, Salem RM, McKnight AJ; GENIE Consortium, Tarnow L, Panduru NM, Barbarini N, Di Camillo B, Toffolo GM, Tryggvason K, Bellazzi R, Cobelli C; FinnDiane Study Group, Groop PH.

Diabetologia. 2014 Aug;57(8):1611-22. doi: 10.1007/s00125-014-3256-2. Epub 2014 May 29.

PMID:
24871321
39.

Arterial function can be obtained by noninvasive finger pressure waveform.

Bernardi L, Gordin D, Rosengård-Bärlund M, Mäkinen VP, Mereu R, DiToro A, Groop PH; FinnDiane Study Group.

Int J Cardiol. 2014 Jul 15;175(1):169-71. doi: 10.1016/j.ijcard.2014.03.179. Epub 2014 Apr 6. No abstract available.

PMID:
24856804
40.

Genome-wide association study of urinary albumin excretion rate in patients with type 1 diabetes.

Sandholm N, Forsblom C, Mäkinen VP, McKnight AJ, Osterholm AM, He B, Harjutsalo V, Lithovius R, Gordin D, Parkkonen M, Saraheimo M, Thorn LM, Tolonen N, Wadén J, Tuomilehto J, Lajer M, Ahlqvist E, Möllsten A, Marcovecchio ML, Cooper J, Dunger D, Paterson AD, Zerbini G, Groop L; SUMMIT Consortium, Tarnow L, Maxwell AP, Tryggvason K, Groop PH; FinnDiane Study Group.

Diabetologia. 2014 Jun;57(6):1143-53. doi: 10.1007/s00125-014-3202-3. Epub 2014 Mar 5.

PMID:
24595857
41.

Indexing Graphs for Path Queries with Applications in Genome Research.

Sirén J, Välimäki N, Mäkinen V.

IEEE/ACM Trans Comput Biol Bioinform. 2014 Mar-Apr;11(2):375-88. doi: 10.1109/TCBB.2013.2297101.

PMID:
26355784
42.

Haploid to diploid alignment for variation calling assessment.

Mäkinen V, Rahkola J.

BMC Bioinformatics. 2013;14 Suppl 15:S13. doi: 10.1186/1471-2105-14-S15-S13. Epub 2013 Oct 15.

43.

Chromosome 2q31.1 associates with ESRD in women with type 1 diabetes.

Sandholm N, McKnight AJ, Salem RM, Brennan EP, Forsblom C, Harjutsalo V, Mäkinen VP, McKay GJ, Sadlier DM, Williams WW, Martin F, Panduru NM, Tarnow L, Tuomilehto J, Tryggvason K, Zerbini G, Comeau ME, Langefeld CD; FIND Consortium, Godson C, Hirschhorn JN, Maxwell AP, Florez JC, Groop PH; FinnDiane Study Group and the GENIE Consortium.

J Am Soc Nephrol. 2013 Oct;24(10):1537-43. doi: 10.1681/ASN.2012111122. Epub 2013 Sep 12.

44.

Metabolic phenotyping of diabetic nephropathy.

Mäkinen VP, Kangas AJ, Soininen P, Würtz P, Groop PH, Ala-Korpela M.

Clin Pharmacol Ther. 2013 Nov;94(5):566-9. doi: 10.1038/clpt.2013.158. Epub 2013 Aug 9. Review.

PMID:
23933969
45.

Associations and interactions between lipid profiles, retinopathy and nephropathy in patients with type 1 diabetes: the FinnDiane Study.

Tolonen N, Hietala K, Forsblom C, Harjutsalo V, Mäkinen VP, Kytö J, Summanen PA, Thorn LM, Wadén J, Gordin D, Taskinen MR, Groop PH; FinnDiane Study Group.

J Intern Med. 2013 Nov;274(5):469-79. doi: 10.1111/joim.12111. Epub 2013 Aug 1.

46.

Ligand-stabilized Au13Cu(x) (x = 2, 4, 8) bimetallic nanoclusters: ligand engineering to control the exposure of metal sites.

Yang H, Wang Y, Lei J, Shi L, Wu X, Mäkinen V, Lin S, Tang Z, He J, Häkkinen H, Zheng L, Zheng N.

J Am Chem Soc. 2013 Jul 3;135(26):9568-71. doi: 10.1021/ja402249s. Epub 2013 Jun 21.

PMID:
23789787
47.

A novel min-cost flow method for estimating transcript expression with RNA-Seq.

Tomescu AI, Kuosmanen A, Rizzi R, Mäkinen V.

BMC Bioinformatics. 2013;14 Suppl 5:S15. doi: 10.1186/1471-2105-14-S5-S15. Epub 2013 Apr 10.

48.

Using recombinant Lactococci as an approach to dissect the immunomodulating capacity of surface piliation in probiotic Lactobacillus rhamnosus GG.

von Ossowski I, Pietilä TE, Rintahaka J, Nummenmaa E, Mäkinen VM, Reunanen J, Satokari R, de Vos WM, Palva I, Palva A.

PLoS One. 2013 May 14;8(5):e64416. doi: 10.1371/journal.pone.0064416. Print 2013.

49.

Systems Biology Approaches and Applications in Obesity, Diabetes, and Cardiovascular Diseases.

Meng Q, Mäkinen VP, Luk H, Yang X.

Curr Cardiovasc Risk Rep. 2013 Feb;7(1):73-83. Epub 2012 Oct 18.

50.

Triglyceride-cholesterol imbalance across lipoprotein subclasses predicts diabetic kidney disease and mortality in type 1 diabetes: the FinnDiane Study.

Mäkinen VP, Soininen P, Kangas AJ, Forsblom C, Tolonen N, Thorn LM, Viikari J, Raitakari OT, Savolainen M, Groop PH, Ala-Korpela M; Finnish Diabetic Nephropathy Study Group.

J Intern Med. 2013 Apr;273(4):383-95. doi: 10.1111/joim.12026. Epub 2013 Jan 18.

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