Judul: Molecular Interaction Modeling of Drugs and Prediction of  Synergistic Combination of Two Drugs
Abstrak: Kanker masih menjadi penyakit yang serius baik di dunia maupun di Indonesia. Data Global Cancer Observatory pada tahun 2018 menunjukan kasus kanker di Indonesia telah mencapai 348.809 kasus dalam satu tahun. Penyakit kanker dapat disebabkan oleh pertumbuhan sel yang tidak normal sehingga mengakibatkan hilangnya mekanisme kontrol sel. Banyak metode pengobatan konvensional untuk mencegah penyebaran sel kanker seperti operasi, kemoterapi, dan monoterapi yaitu pemberian satu obat pada sel kanker. Walaupun penerapan pengobatan ini menunjukkan hasil yang baik karena dapat menekan pertumbuhan sel kanker, namun terdapat efek samping seperti resistensi obat dan sel kanker dapat berkembang kembali. Banyak penelitian yang telah dilakukan sebelumnya mengungkapkan bahwa permasalahan resistensi obat dapat ditangani dengan melakukan kombinasi obat. Kombinasi obat merupakan penggabungan dari dua atau lebih obat yang bertujuan untuk mengatasi penyakit tertentu. Penelitian ini bertujuan untuk merancang pipeline komputasional menggunakan metode LASSO untuk memilih fitur sinergi obat yang paling relevan dan menggunakan metode Random Forest untuk membangun sebuah model prediksi kombinasi obat. Data yang digunakan pada penelitian ini merupakan data sekunder yang berasal dari AstraZeneca-Sanger DREAM Challenge, seperti data obat monoterapi sejumlah 70 obat, data kombinasi obat dan data sinergi obat masing-masing sebanyak 2079, dan data mutasi gen 85 sel kanker. Data monoterapi merupakan data yang berisikan informasi fitur molekular obat. Tahapan yang dilakukan pada penelitian ini ialah melakukan pendeskripsian data menggunakan aplikasi PaDel-Descriptor untuk mempermudah dalam mengenali fitur obat yang terkandung didalam data obat monoterapi. Selanjutnya dilakukan seleksi fitur pada data obat monoterapi menggunakan metode LASSO untuk memilih fitur yang paling berpengaruh. Penerapan metode LASSO untuk menyeleksi fitur molekuler obat menghasilkan sebanyak 72 fitur untuk data monoterapi. Fitur ini kemudian dilakukan proses penggabungan data, sehingga menghasilkan dataset yang terdiri dari data kombinasi obat, data sinergi obat dan data mutasi gen. Data ini nantinya digunakan untuk membentuk suatu model prediksi kombinasi obat menggunakan metode Random Forest. Setelah didapatkan hasil model kombinasi obat kemudian dilakukan analisis model menggunakan confusin matrix. Model yang telah dibuat menggunakan metode Random Forest kemudian diuji menggunakan data kombinasi obat baru yang menghasilkan akurasi sebesar 0,63. Model tersebut juga dapat memprediksi sinergi kombinasi obat dengan tiga kategori, yaitu nol jika kombinasi obat netral, satu jika kombinasi obat mengalami peningkatan sinergi, dan minus satu jika kombinasi obat menurun dan bersifat antagonis. Hasil dari penelitian ini juga dapat digunakan untuk menemukan fitur yang relevan pada obat kanker terutama untuk memprediksi sinergi kombinasi obat.
Keyword: cancer, drug combination, feature selection, LASSO, PaDELDescriptor

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: Biology and ecology of the buffalo fly, haematobia exigua de meijere (diptera:muscidae)
Abstrak: The life history of the buffalo fly was observed at room temperature of 27°-29.5°C and at a relative humidity of 48-68 percent. The eggs hatched after 17-22 hours, with an average of 18 hours. First instar larvae measured 1.25 + 0.16 mm long and 0.27 + 0.037 mm wide. reach the second larval stage. It took 6-12 hours to the second instar larvae measured 2.676 +0.187 mm long and 0.353 +0.028 mm wide. The second instar took about 12-24 hours to reach the third larval stage. The third instar measured 6.833 + 0.189 mm long and 0.919 + 0.051 mm wide. It took about 2-3 days for the third instar to become pupae. Duration of the pupal stage was about 5-7 days before the adults emerged. Under laboratory condition, the buffalo fly completed its life cycle in 9.5 to 10.5 days, whereas under field condition it took 9-14 days with an average of 10.5 days. The growth ratio which was based on the measurements of the cephalo-pharyngeal skeleton of each instar was 1.797. The preoviposition period was 8-12 days and mating usually occurred on the 6th or 7th day. The longevity of the male was 29 days, whereas it was 32 days in the female. The range of egg production under laboratory condition was between 20-139 per female fly. The belly and hind legs of the carabao were the most preferred habitat either during cloudy and cool dry or shiny and warm day. When the weather was clear and the carabao was in the shade, the flies tended to be more distributed particularly on the shoulder, dewlap, back and horns. The carabaos had relatively fewer Musca sp. and Stomoxys calcitrans than cattle; never the less carabaos had a higher infestation of buffalo fly than cattle. Oviposition occured either during the day or night but the buffalo flies were more active during the night than in the day time. The effect of various lengths of exposure time of manure in relation to the oviposition was profound. observed, the maximum number of the buffalo flies was occurred when the dung was exposed for 30 minutes. With increasing time of exposure the number and variety of insects also increase. It was evident that Sepsidae and psychodidae were the most dominant insects emerging from the dung. The number of buffalo flies also increased with increasing
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