Displaying all 5 publications

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  1. Chew TH, Joyce-Tan KH, Akma F, Shamsir MS
    Bioinformatics, 2011 May 1;27(9):1320-1.
    PMID: 21398666 DOI: 10.1093/bioinformatics/btr109
    birgHPC, a bootable Linux Live CD has been developed to create high-performance clusters for bioinformatics and molecular dynamics studies using any Local Area Network (LAN)-networked computers. birgHPC features automated hardware and slots detection as well as provides a simple job submission interface. The latest versions of GROMACS, NAMD, mpiBLAST and ClustalW-MPI can be run in parallel by simply booting the birgHPC CD or flash drive from the head node, which immediately positions the rest of the PCs on the network as computing nodes. Thus, a temporary, affordable, scalable and high-performance computing environment can be built by non-computing-based researchers using low-cost commodity hardware.
  2. Ahmad S, Gromiha MM
    Bioinformatics, 2002 Jun;18(6):819-24.
    PMID: 12075017
    MOTIVATION: Prediction of the tertiary structure of a protein from its amino acid sequence is one of the most important problems in molecular biology. The successful prediction of solvent accessibility will be very helpful to achieve this goal. In the present work, we have implemented a server, NETASA for predicting solvent accessibility of amino acids using our newly optimized neural network algorithm. Several new features in the neural network architecture and training method have been introduced, and the network learns faster to provide accuracy values, which are comparable or better than other methods of ASA prediction.

    RESULTS: Prediction in two and three state classification systems with several thresholds are provided. Our prediction method achieved the accuracy level upto 90% for training and 88% for test data sets. Three state prediction results provide a maximum 65% accuracy for training and 63% for the test data. Applicability of neural networks for ASA prediction has been confirmed with a larger data set and wider range of state thresholds. Salient differences between a linear and exponential network for ASA prediction have been analysed.

    AVAILABILITY: Online predictions are freely available at: http://www.netasa.org. Linux ix86 binaries of the program written for this work may be obtained by email from the corresponding author.

  3. Cheong WH, Tan YC, Yap SJ, Ng KP
    Bioinformatics, 2015 Nov 15;31(22):3685-7.
    PMID: 26227146 DOI: 10.1093/bioinformatics/btv433
    : We present ClicO Free Service, an online web-service based on Circos, which provides a user-friendly, interactive web-based interface with configurable features to generate Circos circular plots.
  4. Wang X, Yu G, Wang J, Zain AM, Guo W
    Bioinformatics, 2022 Nov 15;38(22):5092-5099.
    PMID: 36130063 DOI: 10.1093/bioinformatics/btac643
    MOTIVATION: Cancer subtype diagnosis is crucial for its precise treatment and different subtypes need different therapies. Although the diagnosis can be greatly improved by fusing multiomics data, most fusion solutions depend on paired omics data, which are actually weakly paired, with different omics views missing for different samples. Incomplete multiview learning-based solutions can alleviate this issue but are still far from satisfactory because they: (i) mainly focus on shared information while ignore the important individuality of multiomics data and (ii) cannot pick out interpretable features for precise diagnosis.

    RESULTS: We introduce an interpretable and flexible solution (LungDWM) for Lung cancer subtype Diagnosis using Weakly paired Multiomics data. LungDWM first builds an attention-based encoder for each omics to pick out important diagnostic features and extract shared and complementary information across omics. Next, it proposes an individual loss to jointly extract the specific information of each omics and performs generative adversarial learning to impute missing omics of samples using extracted features. After that, it fuses the extracted and imputed features to diagnose cancer subtypes. Experiments on benchmark datasets show that LungDWM achieves a better performance than recent competitive methods, and has a high authenticity and good interpretability.

    AVAILABILITY AND IMPLEMENTATION: The code is available at http://www.sdu-idea.cn/codes.php?name=LungDWM.

    SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

  5. Konanov DN, Babenko VV, Belova AM, Madan AG, Boldyreva DI, Glushenko OE, et al.
    Bioinformatics, 2023 Nov 20.
    PMID: 37982752 DOI: 10.1093/bioinformatics/btad702
    MOTIVATION: The Oxford Nanopore technology has a great potential for the analysis of methylated motifs in genomes, including whole genome methylome profiling. However, we found that there are no methylation motifs detection algorithms which would be sensitive enough and return deterministic results. Thus, the MEME suit does not extract all H. pylori methylation sites de novo even using the iterative manually controlled approach implemented in the most up-to-date methylation analysis tool Nanodisco.

    RESULTS: We present Snapper, a new highly-sensitive approach to extract methylation motif sequences based on a greedy motif selection algorithm. Snapper does not require manual control during the enrichment process and has enrichment sensitivity higher than MEME coupled with Tombo or Nanodisco instruments that was demonstrated on H. pylori strain J99 studied earlier by the PacBio technology and on four external datasets representing different bacterial species. We used Snapper to characterize the total methylome of a new H.pylori strain A45. At least four methylation sites that have not been described for H. pylori earlier were revealed. We experimentally confirmed the presence of a new CCAG-specific methyltransferase and inferred a gene encoding a new CCAAK-specific methyltransferase.

    AVAILABILITY: Snapper is implemented using Python and freely available as a pip package named 'snapper-ont'. Also, Snapper and the demo dataset are available in Zenodo (10.5281/zenodo.10117651).

    SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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