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

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

Abstract Background Diverse interactions occur between biomolecules, such as activation, inhibition, expression, or repression. However, previous network-based studies of drug repositioning have employed interaction on the binary protein-protein interaction (PPI) network without considering the characteristics of the interactions. Recently, some studies of drug repositioning using gene expression data found that associations between drug and disease genes are useful information for identifying novel drugs to treat diseases. However, the gene expression profiles for drugs and diseases are not always available. Although gene expression profiles of drugs and diseases are available, existing methods cannot use the drugs or diseases, when differentially expressed genes in the profiles are not included in their network. Results We developed a novel method for identifying candidate indications of existing drugs considering types of interactions between biomolecules based on known drug-disease associations. To obtain associations between drug and disease genes, we constructed a directed network using protein interaction and gene regulation data obtained from various public databases providing diverse biological pathways. The network includes three types of edges depending on relationships between biomolecules. To quantify the association between a target gene and a disease gene, we explored the shortest paths from the target gene to the disease gene and calculated the types and weights of the shortest paths. For each drug-disease pair, we built a vector consisting of values for each disease gene influenced by the drug. Using the vectors and known drug-disease associations, we constructed classifiers to identify novel drugs for each disease. Conclusion We propose a method for exploring candidate drugs of diseases using associations between drugs and disease genes derived from a directed gene network instead of gene regulation data obtained from gene expression profiles. Compared to existing methods that require information on gene relationships and gene expression data, our method can be applied to a greater number of drugs and diseases. Furthermore, to validate our predictions, we compared the predictions with drug-disease pairs in clinical trials using the hypergeometric test, which showed significant results. Our method also showed better performance compared to existing methods for the area under the receiver operating characteristic curve (AUC)

Prediction of Side Effects Using Comprehensive Similarity Measures

Identifying the potential side effects of drugs is crucial in clinical trials in the pharmaceutical industry. The existing side effect prediction methods mainly focus on the chemical and biological properties of drugs. This study proposes a method that uses diverse information such as drug-drug interactions from DrugBank, drug-drug interactions from network, single nucleotide polymorphisms, and side effect anatomical hierarchy as well as chemical structures, indications, and targets. The proposed method is based on the assumption that properties used in drug repositioning studies could be utilized to predict side effects because the phenotypic expression of a side effect is similar to that of the disease. The prediction results using the proposed method showed a 3.5% improvement in the area under the curve (AUC) over that obtained when only chemical, indication, and target features were used. The random forest model delivered outstanding results for all combinations of feature types. Finally, after identifying candidate side effects of drugs using the proposed method, the following four popular drugs were discussed: (1) dasatinib, (2) sitagliptin, (3) vorinostat, and (4) clonidine

Additional file 3: of Drug voyager: a computational platform for exploring unintended drug action

Predicted side effects of drugs. The list of 11,152 novel side effects of drugs spanning 39 drugs and 1,598 side effects. (XLSX 179 kb

Additional file 4: Figure S1. of Drug voyager: a computational platform for exploring unintended drug action

The overlap between drug-signaling pathways (haloperidol and valproic acid). The shared pathways derived from each drug-signaling pathway for haloperidol and valproic acid. (JPG 933 kb