Judul: Prediksi Interaksi Drug Target Pada Gen Kanker Menggunakan Metode LASSO-XGBOOST
Abstrak: Pengobatan kanker saat ini sering dilakukan dengan kemoterapi menggunakan obat kimia dan dapat menyebabkan efek samping. Alternatif pengobatan dapat menggunakan senyawa herbal yang diketahui memiliki efek samping lebih sedikit. Analisis Drug Target Interaction (DTI) dapat dilakukan untuk mengetahui interaksi senyawa herbal terhadap protein kanker. Pada penelitian ini dilakukan perancangan model prediksi DTI dengan melakukan seleksi fitur pada dataset menggunakan Least Absolute Shrinkage and Selection Operator (LASSO) lalu dilakukan penyeimbangan data dengan Synthetic Minority Oversampling Technique (SMOTE) dan diprediksi menggunakan Extreme Gradient Boosting (XGBoost). Data protein terkait kanker didapatkan dari daftar Cancer Gene Census, dari daftar tersebut dilakukan penelusuran pada database GDSC, DrugCentral, dan DrugBank untuk menghasilkan daftar senyawa obat yang berinteraksi dengan protein tersebut. Selain itu, senyawa herbal dihasilkan dari database HerbalDB dan Knapsack. Pengujian dilakukan pada beberapa jenis ekstraksi fitur seperti CTD, DC, PseAAC, dan PSSM. Hasil prediksi menunjukkan beberapa senyawa herbal seperti andrographolide, ursolic acid dan oleanolic acid memiliki interaksi pada protein terkait kanker. Selain itu, LASSO-XGBoost dapat memprediksi DTI pada kanker dengan skor F1 0,861; AUROC 0,927; recall 0,85; precision 0,866; dan accuracy 0,897.
Keyword: Cancer, Drug target interaction, Herbal compounds, LASSO, XGBoost

Judul: Network-Based Feature Selection to Predict Drug Synergy in Cancer Cells
Abstrak: Identifying synergistic drug combinationsin cancer treatmentis challengingdue to the complex molecular circuitry of cancer and the exponentially increasing number of drugs. Therefore, computational approaches forpredicting drug synergy are crucialin guidingexperimental effortstowardfinding rational combination therapies. This research selects the molecular features of cancer cells with a diffusion network-based approach.Additionally, a modelis developed using non-linear regression algorithms,namely Random Forest, Extremely Randomized Tree, and XGBoost, to predict the synergy score of drug combinations against the selected cancer cell features.Thedata used are118 drug combination screening data and 85 cancer cell molecules provided by AstraZeneca-Sanger DREAM Challenge. Thepredictionresults indicate that as the data size increases, the correlation value of the model improves, leading to better prediction accuracy. The influential feature analysis revealed that the three most influential mutation features in the AKT_1 and PIK3C drug combination model wereATP8B3, ERBB2, and RNF8. In the drug combination model BCL2_BCL2L1 and FGFR, the three mostinfluential mutation features wereBACH1, ODF2, and BFAR. In the MAP2K_1 and PIK3C drug combination model, TP53, IL12p40*, and SOX4 werethe most influential features.All of these features have a connection between the mutation features and cell lines, aligning with the therapeutic targets of the three-drug combinations, which were the focus of this study.
Keyword: cancer, drug combination, drug synergy, network diffusion kernel, non-linear regression

Judul: Studi Taksonomi Katak Subgenus Papurana (Lesson, 1831) dari Halmahera (Anura, Ranidae, Hylarana), Studi Taksonomi Katak Subgenus Papurana (Lesson, 1831) dari Halmahera (Anura, Ranidae, Hylarana
Abstrak: I reevaluated taxonomic of subgenus Papurana position from Halmahera based on morphological and molecular analyses. My results show that two separate species occur on the Halmahera Island, each nested within two distinct clades (hereafter papua clade and celebensis clade), one of which corresponds to H. moluccana (celebensis clade) and the other to an unidentified species (papua clade). By p-distance ranging from 6.9 to 11.5% on the 16S rRNA gene, the unidentified species (Hylarana sp.) is genetically different from all congeners of the papua clade. The Hylarana sp. can be distinguished from all congeners by following combination of charactes: vomer teeth in two oblique rows with narrow inter-vomer distance; snout rounded dorsally; dorsum with few scattered cone-shaped tubercles that are black with white tips; distinct skin folds (ridges) on the dorsal side of the thigh coinciding with dark brown cross bars; a marbled pattern on the ventral side of the thigh and yellowish groin. I also present new data for H. daemeli, H. volkerjane, and H. arfaki from the western region of Papua. Furthermore, I found that H. celebensis has numerous mtDNA lineages, indicating cryptic diversity within the celebensis clade. The discovery of two separate species on Halmahera necessitates more research into the biogeographic history of Hylarana in Northern Moluccas.
Keyword: frog, Halmahera, Hylarana, morphology, Papurana, phylogenetic