Kelimpahan Dan Keanekaragaman Plankton Di Perairan Laguna Desa Tolongano Kecamatan Banawa Selatan

Penelitian bertujuan untuk mengetahui kelimpahan dan keanekaragaman plankton yang ada di Perairan Laguna, Desa Tolongano, Kecamatan Banawa Selatan. Penelitian dilaksanakan pada bulan Juni – Juli 2009. Pengambilan sampel plankton bertempat di Perairan Laguna, Desa Tolongano, Kecamatan Banawa Selatan, Kabupaten Donggala. Identifikasi sampel dilakukan di Laboratorium Budidaya Perairan, Fakultas Pertanian, Universitas Tadulako. Metode penelitian yang digunakan adalah purpossive sampling method (penempatan titik sampel dengan sengaja). Stasiun pengambilan sampel terdiri atas 5 stasiun, dilakukan sebanyak 3 kali yaitu pada pukul 07.00, 12.00, dan 17.00 WITA. Hasil penelitian menunjukkan, bahwa kelimpahan fitoplankton dari kelas Bacillariophyceae berkisar antara 8.925 – 16.135 ind/l dan kelimpahan zooplankton dari kelas Crustacea berkisar antara 35 – 70 ind/l, indeks keanekaragaman fitoplankton dari kelas Bacillariophyceae berkisar antara 2,010 – 2,504 dan indeks keanekaragaman zooplankton dari kelas Crustacea berkisar antara 0 – 0,6931, indeks dominansi dari kelas Bacillariophyceae berkisar antara 1,1995 – 1,2326 menunjukkan ada jenis plankton yang mendominasi, yaitu Nitzchia sp

Additional file 1: of PMAMCA: prediction of microRNA-disease association utilizing a matrix completion approach

Table S1. Notation. Table S2. Top-50 candidate miRNAs for breast cancer predicted by PMAMCA. Table S3. Top-50 candidate miRNAs for lung cancer predicted by PMAMCA. Table S4. List of validated cancer hallmark-based signature and their genes. Table S5. List of confirmed driver and passenger genes. (additional experimental result) Table S6. Top-50 candidate miRNAs for colon cancer predicted by PMAMCA. (additional experimental result). Figure S1. The workflow for prioritizing candidate miRNAs. Figure S2. Applying matrix factorization into miRNA-disease association extraction. Figure S3. Performance comparisons between PMAMCA and four state-of-the-art methods. Figure S4. Performance of PMAMCA with different values of k. Figure S5. Numbers of correctly retrieved known disease-related miRNAs for various rank thresholds. (ZIP 2223 kb

Additional file 1: of Novel deep learning model for more accurate prediction of drug-drug interaction effects

Table S1. DDI types. Table S2. Prediction of DDI (prediction score ≥ 0.5). (XLSX 59 kb

Optimal combination of two thresholds for each dataset in 10-fold cross validation.

<p>Sen. = Sensitivity, Spec. = Specificity.</p

Functional enrichment results for the GSE15222 dataset.

An asterisk of a pathway and GO term indicates that it has been reported in previous studies. (A) We used GSEA with a FDR q-value threshold of 0.001 and selected 15 pathways that satisfy the threshold. Interestingly, several AD-related pathways, such as Regulation of actin cytoskeleton and Neurotrophin signalling pathway, were enriched as well as the Alzheimer’s disease pathway. (B) We used FuncAssociates 3.0 with the default evidence code. The p-value threshold was 0.001 and we selected 20 GO terms that are potentially related to AD. We found that many GO terms related to AD were significantly enriched. (C) We used GSEA with a FDR q-value threshold of 0.001 and selected 15 GO terms in the cellular component category that satisfy the threshold and are potentially related to neuronal functions.</p

Integrative Gene Network Construction to Analyze Cancer Recurrence Using Semi-Supervised Learning

<div><p>Background</p><p>The prognosis of cancer recurrence is an important research area in bioinformatics and is challenging due to the small sample sizes compared to the vast number of genes. There have been several attempts to predict cancer recurrence. Most studies employed a supervised approach, which uses only a few labeled samples. Semi-supervised learning can be a great alternative to solve this problem. There have been few attempts based on manifold assumptions to reveal the detailed roles of identified cancer genes in recurrence.</p><p>Results</p><p>In order to predict cancer recurrence, we proposed a novel semi-supervised learning algorithm based on a graph regularization approach. We transformed the gene expression data into a graph structure for semi-supervised learning and integrated protein interaction data with the gene expression data to select functionally-related gene pairs. Then, we predicted the recurrence of cancer by applying a regularization approach to the constructed graph containing both labeled and unlabeled nodes.</p><p>Conclusions</p><p>The average improvement rate of accuracy for three different cancer datasets was 24.9% compared to existing supervised and semi-supervised methods. We performed functional enrichment on the gene networks used for learning. We identified that those gene networks are significantly associated with cancer-recurrence-related biological functions. Our algorithm was developed with standard C++ and is available in Linux and MS Windows formats in the STL library. The executable program is freely available at: <a href="http://embio.yonsei.ac.kr/~Park/ssl.php" target="_blank">http://embio.yonsei.ac.kr/~Park/ssl.php</a>.</p></div

Representation of a breast cancer recurrence-specific gene sub-network related to cancer proliferation.

<p>The orange-colored nodes are oncogenes.</p

Overview of the proposed approach.

<p>Gene expression data with two class labels are normalized by the z-scoring approach. For class label 1, which indicates disease, possible gene pairs are selected by incorporating disease-related genes and interactome data. For class label 0, which indicates normal, the same number of gene pairs as that for class label 1 is randomly selected. From all gene pairs, 22 features are extracted and used to inform the machine learning-based model. In order to evaluate performance, 10-fold cross validation is performed.</p

Machine learning-based identification of genetic interactions from heterogeneous gene expression profiles

<div><p>The identification of disease-related genes and disease mechanisms is an important research goal; many studies have approached this problem by analysing genetic networks based on gene expression profiles and interaction datasets. To construct a gene network, correlations or associations among pairs of genes must be obtained. However, when gene expression data are heterogeneous with high levels of noise for samples assigned to the same condition, it is difficult to accurately determine whether a gene pair represents a significant gene–gene interaction (GGI). In order to solve this problem, we proposed a random forest-based method to classify significant GGIs from gene expression data. To train the model, we defined novel feature sets and utilised various high-confidence interactome datasets to deduce the correct answer set from known disease-specific genes. Using Alzheimer’s disease data, the proposed method showed remarkable accuracy, and the GGIs established in the analysis can be used to build a meaningful genetic network that can explain the mechanisms underlying Alzheimer’s disease.</p></div

Predicting performance comparison of the proposed method with four existing methods using PPI data to identify informative genes.

<p>For each experiment, the optimal combination of two thresholds was obtained using the approach mentioned above and was applied to an independent test using unlabeled samples. Bold font indicates the superior performer.</p><p>TSVM: <i>P</i> (the ratio of two class labels).</p><p>SVM: PolyKernel –C 250007–E 1.0, The complexity parameter C (1.0), epsilon (1.0E−12), filterType (Normalized training data).</p><p>Naïve Bayesian: No parameters.</p><p>Random Forest: numTrees (10), seed (1).</p