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 2: of Drug repositioning using drug-disease vectors based on an integrated network

Predicted drugs and probabilities corresponding to the drugs for diseases. (XLSX 2039 kb

Additional file 1: of Drug repositioning using drug-disease vectors based on an integrated network

The mean AUC and standard deviation from repeating 10-fold cross-validations for each disease using random forest and neural network. (XLSX 31 kb

Adjacency-Based Inference.

<p>Adjacency-Based Inference.</p

AUC comparison of three individual networks and the integrated network.

<p>AUC comparison of three individual networks and the integrated network.</p

System overview.

<p>(a) “Adjacency-Based Inference” measures the drug-drug (disease-disease) adjacency among known drug-disease associations, and infers new drug-disease association. “Module-Distance-Based Inference” derives drug-drug (disease-disease) gene module among known drug-disease associations, measures the distance between the gene module and disease (drug), and infers new drug-disease association. (b) Drug-disease relationship represented by score becomes features. Various machine learning based classifiers are built with those features, and predict unknown drug-disease relationship.</p

Visualization of potential pathway associated with targets of telmisartan and genes related to Alzheimer’s disease.

<p>Visualization of potential pathway associated with targets of telmisartan and genes related to Alzheimer’s disease.</p

Performance evaluation of each method.

<p>Results from the adjacency-based inference (ABI) method, the module-distance-based inference (MDBI) method, and the integrated method of ABI and MDBI (INTG) are compared.</p

A Network-Based Classification Model for Deriving Novel Drug-Disease Associations and Assessing Their Molecular Actions

<div><p>The growing number and variety of genetic network datasets increases the feasibility of understanding how drugs and diseases are associated at the molecular level. Properly selected features of the network representations of existing drug-disease associations can be used to infer novel indications of existing drugs. To find new drug-disease associations, we generated an integrative genetic network using combinations of interactions, including protein-protein interactions and gene regulatory network datasets. Within this network, network adjacencies of drug-drug and disease-disease were quantified using a scored path between target sets of them. Furthermore, the common topological module of drugs or diseases was extracted, and thereby the distance between topological drug-module and disease (or disease-module and drug) was quantified. These quantified scores were used as features for the prediction of novel drug-disease associations. Our classifiers using Random Forest, Multilayer Perceptron and C4.5 showed a high specificity and sensitivity (AUC score of 0.855, 0.828 and 0.797 respectively) in predicting novel drug indications, and displayed a better performance than other methods with limited drug and disease properties. Our predictions and current clinical trials overlap significantly across the different phases of drug development. We also identified and visualized the topological modules of predicted drug indications for certain types of cancers, and for Alzheimer’s disease. Within the network, those modules show potential pathways that illustrate the mechanisms of new drug indications, including propranolol as a potential anticancer agent and telmisartan as treatment for Alzheimer’s disease.</p></div

Ten-fold cross-validation.

<p>Ten-fold cross-validation.</p