Computational Proteomics Using Network-Based Strategies

This thesis examines the productive application of networks towards proteomics, with a specific biological focus on liver cancer. Contempory proteomics (shot- gun) is plagued by coverage and consistency issues. These can be resolved via network-based approaches. The application of 3 classes of network-based approaches are examined: A traditional cluster based approach termed Proteomics Expansion Pipeline), a generalization of PEP termed Maxlink and a feature-based approach termed Proteomics Signature Profiling. PEP is an improvement on prevailing cluster-based approaches. It uses a state- of-the-art cluster identification algorithm as well as network-cleaning approaches to identify the critical network regions indicated by the liver cancer data set. The top PARP1 associated-cluster was identified and independently validated. Maxlink allows identification of undetected proteins based on the number of links to identified differential proteins. It is more sensitive than PEP due to more relaxed requirements. Here, the novel roles of ARRB1/2 and ACTB are identified and discussed in the context of liver cancer. Both PEP and Maxlink are unable to deal with consistency issues, PSP is the first method able to deal with both, and is termed feature-based since the network- based clusters it uses are predicted independently of the data. It is also capable of using real complexes or predicted pathway subnets. By combining pathways and complexes, a novel basis of liver cancer progression implicating nucleotide pool imbalance aggravated by mutations of key DNA repair complexes was identified. Finally, comparative evaluations suggested that pure network-based methods are vastly outperformed by feature-based network methods utilizing real complexes. This is indicative that the quality of current networks are insufficient to provide strong biological rigor for data analysis, and should be carefully evaluated before further validations.Open Acces

Utilizing supervised models to infer consensus labels and their quality from data with multiple annotators

Real-world data for classification is often labeled by multiple annotators. For analyzing such data, we introduce CROWDLAB, a straightforward approach to estimate: (1) A consensus label for each example that aggregates the individual annotations (more accurately than aggregation via majority-vote or other algorithms used in crowdsourcing); (2) A confidence score for how likely each consensus label is correct (via well-calibrated estimates that account for the number of annotations for each example and their agreement, prediction-confidence from a trained classifier, and trustworthiness of each annotator vs. the classifier); (3) A rating for each annotator quantifying the overall correctness of their labels. While many algorithms have been proposed to estimate related quantities in crowdsourcing, these often rely on sophisticated generative models with iterative inference schemes, whereas CROWDLAB is based on simple weighted ensembling. Many algorithms also rely solely on annotator statistics, ignoring the features of the examples from which the annotations derive. CROWDLAB in contrast utilizes any classifier model trained on these features, which can generalize between examples with similar features. In evaluations on real-world multi-annotator image data, our proposed method provides superior estimates for (1)-(3) than many alternative algorithms

OPTIMIZATION OF PORTFOLIO USING FUZZY SELECTION

The problem of portfolio optimization concerns the allocation of the investor’s wealth between several security alternatives so that the maximum profit can be obtained. One of the methods used is Fuzzy Portfolio Selection to understand it better. This method separates the objective function of return and the objective function of risk to determine the limit of the membership function that will be used. The goal of this study is to understand the application of the Fuzzy Portfolio Selection method over shares that have been chosen on a portfolio optimization problem, understand return and risk, and understand the budget proportion of each claim. The subject of this study is the shares of 20 companies included in Bursa Efek Indonesia from 1 January 2021 until 1 January 2022. The result of this study shows that from 20 shares, there are 10 shares that is suitable in the forming of optimal portfolio, those are ADRO (0%), ANTM (43.3%), ASII (0%), BBCA (0%), BBRI (0%), BBTN (0%), BRPT (0%), BSDE (0%), ERAA (16%), and INCO (40.7%). The expected return from the portfolio is 0.0878895207 or 8.8% for the return and 0.0226022117 or 2.3% for the risk

Study of Strontium Titanate and Barium Zirconate Properties Using Molecular Dynamics Simulation

Molecular dynamics simulation has been carried out on strontium titanate and barium zirconate in order to study the microscopic atomic behavior, and the macroscopic thermodynamic and thermal transport properties of the perovskite materials. The intricate interatomic potentials can be simpli�ed into pairwise interactions, which consist of ionic interaction, short-range repulsion, Van der Waals attraction and Morse covalent bonding. New sets of potential parameters of strontium titanate and barium zirconate have been presented. Radial distribution functions have been obtained to study the atomic and structural behavior. Structural parameters, thermal expansion coe�cient, isothermal compressibility, heat capacity and thermal conductivity have been evaluated in the temperature range of 298 - 2000 K and pressure ranging from 1 atm to 20.3 GPa. At room temperature, the values of lattice parameters of strontium titanate and barium zirconate are obtained to be 3.9051 �A and 4.1916 �A. While the calculation of thermal expansion coe�cients of strontium titanate and barium zirconate gives 1.010 �1

Hybridization Of Deterministic And Metaheuristic Approaches In Global Optimization

In solving general global optimization problems, various approaches methods have been developed since 1970’s which can be divided into two classes named deterministic and the probabilistic/metaheuristic approaches. Deterministic approaches provided a theoretical guarantee of locating the -global optimum solution. However, most of the time deterministic approaches required very high cost and time of computational to obtain the global optimum solution. The probabilistic/metaheuristic approaches are methods based on probability, genetic and evolution as its metaheuristic function for the guidance when solving the global optimization problem, and their accuracy of the solution obtained are not guaranteed. However, some time the metaheuristic approaches work very well in selected problems. The main objective of this research is to increase the accuracy of the solution obtained by Metaheuristic approaches by hybridization with some well-developed local deterministic approaches such as Steepest descent method, conjugate gradient methods and quasi-Newton’s methods. In the analysis of the literature, Artificial Bees Colony (ABC) Algorithm has been selected as the metaheuristic approach to be improved its capability and efficiency to solve the global optimization problems. Several enhancements have been done in this research. For derivative free, a new method called Simplexed ABC method hav an obtained a more accurate global optimum solution by using only 10 colony e been introduced. The numerical results show that Simplexed ABC c of bees with 10 cycle each compare to the 10,000 colony of bees with 100 cycles each in original ABC method. The successful of Simplexed ABC method leads this research to develop a mechanism to transform those well-developed gradient based local deterministic optimization approaches into solving global optimization approaches. These enhancements had produced methods called as ABCED Steepest Descent Method, five variants of ABCED Conjugate Gradient Methods and three variants of ABCED Quasi-Newton’s Methods. The numerical results prove that the enhanced ABCED Steepest Descent and two variants of ABCED Quasi-Newton Methods had perfectly solving all the selected benchmark global optimization problems. In another hand, numerical results of ABCED Conjugate Gradient Methods also achieved up to 80.95% of the selected benchmark global optimization been solved successfully. Besides that, the comparison results also indicated that the numerical performance of the new developed methods converges faster than the original ABC algorithm. The results reported are obtained by using standard benchmark test problems and all computation is done by using C++ programming language

Characterization of tumorspheres generated from nasopharyngeal carcinoma cell line, TW06 and chemoresistance to docetaxel and oxaliplatin

In this study, tumorspheres were generated from TW06 nasopharyngeal carcinoma cell line and examined their expression of putative cancer stem-like cell surface markers and drug sensitivity. The rate of tumorsphere expansion from dissociated late passage TW06 tumorspheres (≥ passage 15) was higher than that from parental cells and dissociated 10-day-old (passage 0) tumorspheres. The expression of CD24 surface marker was lost in the generation of tumorspheres and the loss was reversible after differentiating the tumorspheres in monolayer culture conditions. Drug sensitivity assay showed that late passage tumorspheres were resistant to docetaxel and oxaliplatin treatment. Our data suggest that serially passaged tumorspheres possess the characteristics of CSCs that render them a suitable preclinical in vitro model for evaluating anticancer drug efficacy and elucidating the underlying mechanisms of drug resistance

Aktivitas Marketing Communication di Swiss-Belhotel Batam

Swiss-Belhotel Harbour Bay merupakan hotel bintang empat yang berada di Kota Batam. Dalam kegiatan kerja magang penulis selama periode magang dari tanggal 18 Agustus 2021 hingga 30 November 2021, penulis ditempatkan pada posisi Internship Divisi Marketing Communication pada Departemen Sales and Marketing. Program magang yang penulis lakukan bertujuan untuk dapat memberikan kontribusi positif dengan menerapkan teori yang telah penulis peroleh dalam perkuliahan dalam mengerjakan dan memenuhi setiap pekerjaan yang diberikan. Dengan membangun kerjasama dengan departemen dan rekan kerja lainnya di Swiss-Belhotel Harbour Bay, menciptakan perspektif baru bagi penulis, khususnya selama bekerja di divisi Marketing Communication pada departemen Sales and Marketing Management. Penulis melakukan praktik kerja magang di Swiss-Belhotel Harbour Bay ini mempunyai kewajiban untuk membantu terlaksananya pemasaran bisnis Swiss- Belhotel Harbour Bay secara lancar. Dalam menjalankan praktik kerja magang, penulis telah melakukan berbagai pekerjaan sebagai seorang Marketing Communcation seperti memposting dan menangani sosial media setiap hari yang berhasil meningkatkan jumlah followers sosial media sebanyak 170 followers di Instagram dan 63 followers di Facebook, merencanakan dan menentukan strategi pemasaran, membuat press release, mengundang influencer dalam berbagai event, membuat bisnis plan dan action plan, membuat laporan mingguan dan bulanan perusahaan, membuat laporan kemitraan ataupun kerjasama, membuat perjanjian kerjasama, serta mengambil foto dan video untuk konten keperluan sosial media dan promosi. Dalam laporan magang ini, penulis akan membahas mengenai aktivitas Marketing Communication di Swiss-Belhotel Batam