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Items: 1 to 20 of 4980

1.

ClinPred: Prediction Tool to Identify Disease-Relevant Nonsynonymous Single-Nucleotide Variants.

Alirezaie N, Kernohan KD, Hartley T, Majewski J, Hocking TD.

Am J Hum Genet. 2018 Sep 5. pii: S0002-9297(18)30271-4. doi: 10.1016/j.ajhg.2018.08.005. [Epub ahead of print]

PMID:
30220433
2.

ARIADNA: machine learning method for ancient DNA variant discovery.

Kawash JK, Smith SD, Karaiskos S, Grigoriev A.

DNA Res. 2018 Sep 11. doi: 10.1093/dnares/dsy029. [Epub ahead of print]

PMID:
30215675
3.

Visceral fat mass as a novel risk factor for predicting gestational diabetes in obese pregnant women.

Balani J, Hyer SL, Shehata H, Mohareb F.

Obstet Med. 2018 Sep;11(3):121-125. doi: 10.1177/1753495X17754149. Epub 2018 Mar 14.

PMID:
30214477
4.

Prediction of Antitubercular Peptides From Sequence Information Using Ensemble Classifier and Hybrid Features.

Usmani SS, Bhalla S, Raghava GPS.

Front Pharmacol. 2018 Aug 28;9:954. doi: 10.3389/fphar.2018.00954. eCollection 2018.

5.

A Computational Method for Classifying Different Human Tissues with Quantitatively Tissue-Specific Expressed Genes.

Li J, Chen L, Zhang YH, Kong X, Huang T, Cai YD.

Genes (Basel). 2018 Sep 7;9(9). pii: E449. doi: 10.3390/genes9090449.

6.

Machine Learning-Based Method for Obesity Risk Evaluation Using Single-Nucleotide Polymorphisms Derived from Next-Generation Sequencing.

Wang HY, Chang SC, Lin WY, Chen CH, Chiang SH, Huang KY, Chu BY, Lu JJ, Lee TY.

J Comput Biol. 2018 Sep 8. doi: 10.1089/cmb.2018.0002. [Epub ahead of print]

PMID:
30204480
7.

Development of a Protein-Ligand Extended Connectivity (PLEC) fingerprint and its application for binding affinity predictions.

Wójcikowski M, Kukielka M, Stepniewska-Dziubinska MM, Siedlecki P.

Bioinformatics. 2018 Sep 8. doi: 10.1093/bioinformatics/bty757. [Epub ahead of print]

PMID:
30202917
8.

HLBS-PopOmics: an online knowledge base to accelerate dissemination and implementation of research advances in population genomics to reduce the burden of heart, lung, blood, and sleep disorders.

Mensah GA, Yu W, Barfield WL, Clyne M, Engelgau MM, Khoury MJ.

Genet Med. 2018 Sep 10. doi: 10.1038/s41436-018-0118-1. [Epub ahead of print]

PMID:
30197419
9.

Data-driven approach for the prediction and interpretation of core-electron loss spectroscopy.

Kiyohara S, Miyata T, Tsuda K, Mizoguchi T.

Sci Rep. 2018 Sep 6;8(1):13548. doi: 10.1038/s41598-018-30994-6.

10.

BioWorkbench: a high-performance framework for managing and analyzing bioinformatics experiments.

Mondelli ML, Magalhães T, Loss G, Wilde M, Foster I, Mattoso M, Katz D, Barbosa H, de Vasconcelos ATR, Ocaña K, Gadelha LMR Jr.

PeerJ. 2018 Aug 29;6:e5551. doi: 10.7717/peerj.5551. eCollection 2018.

11.

Twenty years of bioinformatics research for protease-specific substrate and cleavage site prediction: a comprehensive revisit and benchmarking of existing methods.

Li F, Wang Y, Li C, Marquez-Lago TT, Leier A, Rawlings ND, Haffari G, Revote J, Akutsu T, Chou KC, Purcell AW, Pike RN, Webb GI, Ian Smith A, Lithgow T, Daly RJ, Whisstock JC, Song J.

Brief Bioinform. 2018 Aug 29. doi: 10.1093/bib/bby077. [Epub ahead of print]

PMID:
30184176
12.

Precision Lasso: Accounting for Correlations and Linear Dependencies in High-Dimensional Genomic Data.

Wang H, Lengerich BJ, Aragam B, Xing EP.

Bioinformatics. 2018 Sep 1. doi: 10.1093/bioinformatics/bty750. [Epub ahead of print]

PMID:
30184048
13.

A brief review on software tools in generating Chou's pseudo-factor representations for all types of biological sequences.

Zhao W, Wang L, Zhang TX, Zhao ZN, Du PF.

Protein Pept Lett. 2018 Sep 4. doi: 10.2174/0929866525666180905111124. [Epub ahead of print]

PMID:
30182829
14.

Protease target prediction via matrix factorization.

Marini S, Vitali F, Rampazzi S, Demartini A, Akutsu T.

Bioinformatics. 2018 Aug 29. doi: 10.1093/bioinformatics/bty746. [Epub ahead of print]

PMID:
30169576
15.

Perspectives and applications of machine learning for evolutionary developmental biology.

Feltes BC, Grisci BI, Poloni JF, Dorn M.

Mol Omics. 2018 Aug 31. doi: 10.1039/c8mo00111a. [Epub ahead of print] Review.

PMID:
30168572
16.

miES: predicting the essentiality of miRNAs with machine learning and sequence features.

Song F, Cui C, Gao L, Cui Q.

Bioinformatics. 2018 Aug 28. doi: 10.1093/bioinformatics/bty738. [Epub ahead of print]

PMID:
30165607
17.

Machine learning and image-based profiling in drug discovery.

Scheeder C, Heigwer F, Boutros M.

Curr Opin Syst Biol. 2018 Aug;10:43-52. doi: 10.1016/j.coisb.2018.05.004. Review.

18.

Large-scale comparison of machine learning methods for drug target prediction on ChEMBL.

Mayr A, Klambauer G, Unterthiner T, Steijaert M, Wegner JK, Ceulemans H, Clevert DA, Hochreiter S.

Chem Sci. 2018 Jun 6;9(24):5441-5451. doi: 10.1039/c8sc00148k. eCollection 2018 Jun 28.

19.

A New Machine Learning-Based Framework for Mapping Uncertainty Analysis in RNA-Seq Read Alignment and Gene Expression Estimation.

McDermaid A, Chen X, Zhang Y, Wang C, Gu S, Xie J, Ma Q.

Front Genet. 2018 Aug 14;9:313. doi: 10.3389/fgene.2018.00313. eCollection 2018.

20.

A deep convolutional neural network approach for astrocyte detection.

Suleymanova I, Balassa T, Tripathi S, Molnar C, Saarma M, Sidorova Y, Horvath P.

Sci Rep. 2018 Aug 27;8(1):12878. doi: 10.1038/s41598-018-31284-x.

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