wSDTNBI: a novel network-based inference method for virtual screening

In recent years, the rapid development of network-based methods for the prediction of drug-target interactions (DTIs) provides an opportunity for the emergence of a new type of virtual screening (VS),...

PENERAPAN FUZZY TSUKAMOTO UNTUK MENENTUKAN JUMLAH PRODUKSI RAKIK

The home industry, especially in the culinary sector, has grown in recent years. Many business actors produce the same products as other business actors, so competition often occurs. One of them is the Usaha Rakik Julidar which since 25 years ago made and sold rakik. There are so many types of rakik that there are minimarkets with different business actors. This makes the rakik not sold out at the convenience store and has an effect on the amount of production. This research was conducted to assist the Usaha Rakik Julidar in predicting the amount of production using Fuzzy Tsukamoto inference method. This research creates a web-based application to help predict the amount of production by looking at incoming demand and the number of workers available. From the prediction results with Fuzzy Tsukamoto, if the demand is 125 packs and the number of workers is 35 people, then the total production to be done is 140 packs. By obtaining a clear amount of production, this application really helps Usaha Rakik Julidar in maximizing production. Keywords: fuzzy tsukamoto; prediction; production; rakik Abstrak: Industri rumah tangga khususnya di sektor kuliner banyak tumbuh dalam beberapa tahun terakhir. Banyak pelaku usaha menghasilkan produk yang sama dengan pelaku usaha lainnya, sehingga sering terjadi persaingan. Salah satunya adalah Usaha Rakik Julidar yang sejak 25 tahun yang lalu membuat dan menjual rakik. Banyak sekali jenis Rakik yang ada minimarket dengan pelaku usaha yang berbeda. Hal ini membuat rakik tidak terjual habis di minimarket dan berpengaruh pada jumlah produksi. Penelitian ini dilakukan untuk membantu Usaha Rakik Julidar dalam melakukan prediksi jumlah produksi dengan menggunakan metode inferensi fuzzy Tsukamoto. Penelitian ini membuat aplikasi berbasis web untuk membantu melakukan prediksi jumlah produksi dengan melihat permintaan yang masuk dan jumlah pekerja yang tersedia. Dari hasil prediksi dengan fuzzy Tsukamoto, jika permintaan sebanyak 125 bungkus dan jumlah tenaga kerja 35 orang, maka jumlah produksi yang harus dilakukan adalah 140 bungkus. Dengan diperolehnya jumlah produksi yang jelas, aplikasi ini sangat membantu Usaha Rakik Julidar dalam memaksimalkan produksi. Kata kunci: fuzzy tsukamoto; prediksi; produksi; rakik

A New Perspective of Paramodulation Complexity by Solving 100 Sliding Block Puzzles

This paper gives complete guidelines for authors submitting papers for the AIRCC Journals. A sliding puzzle is a combination puzzle where a player slides pieces along specific routes on a board to reach a certain end configuration. In this paper, we propose a novel measurement of the complexity of 100 sliding puzzles with paramodulation, which is an inference method of automated reasoning. It turned out that by counting the number of clauses yielded with paramodulation, we can evaluate the difficulty of each puzzle. In the experiment, we have generated 100 * 8 puzzles that passed the solvability checking by countering inversions. By doing this, we can distinguish the complexity of 8 puzzles with the number generated with paramodulation. For example, board [2,3,6,1,7,8,5,4, hole] is the easiest with score 3008 and board [6,5,8,7,4,3,2,1, hole] is the most difficult with score 48653.Besides, we have succeeded in obverse several layers of complexity (the number of clauses generated) in 100 puzzles. We can conclude that the proposed method can provide a new perspective of paramodulation complexity concerning sliding block puzzles.

Usability in the Product Design Process Based on the Intelligent Microprocessor

The sharing and reuse of product design experience knowledge is beneficial to shorten the product development cycle and improve product quality. The intelligent microprocessor has greatly changed the way of interaction between people and computers. This article mainly studies the usability analysis in the product design process based on the intelligent microprocessor. The experiment must first perform initial finite element calculations, and the purpose is to complete the data collection. The collection of evaluation indicators is carried out with the help of perceptual vocabulary classification, and the hierarchical inference method is used to build a tree-like analysis chart. After the indicator collection is completed, the Likert scale method is used to screen the indicators, and the evaluators will rate the selected indicators according to their importance. The Petri net modeling method is used to model the product design process, which mainly includes task element definition, task module division, task planning, and task allocation. Finally, the usability test of the product is carried out. Experimental data shows that the probability of completing the design task within 30 days is 27.74%. The results show that the intelligent microprocessor shortens the time-consuming process of product design and at the same time enhances product usability.

Accelerating Causal Inference and Feature Selection Methods through G-Test Computation Reuse

This article presents a novel and remarkably efficient method of computing the statistical G-test made possible by exploiting a connection with the fundamental elements of information theory: by writing the G statistic as a sum of joint entropy terms, its computation is decomposed into easily reusable partial results with no change in the resulting value. This method greatly improves the efficiency of applications that perform a series of G-tests on permutations of the same features, such as feature selection and causal inference applications because this decomposition allows for an intensive reuse of these partial results. The efficiency of this method is demonstrated by implementing it as part of an experiment involving IPC–MB, an efficient Markov blanket discovery algorithm, applicable both as a feature selection algorithm and as a causal inference method. The results show outstanding efficiency gains for IPC–MB when the G-test is computed with the proposed method, compared to the unoptimized G-test, but also when compared to IPC–MB++, a variant of IPC–MB which is enhanced with an AD–tree, both static and dynamic. Even if this proposed method of computing the G-test is presented here in the context of IPC–MB, it is in fact bound neither to IPC–MB in particular, nor to feature selection or causal inference applications in general, because this method targets the information-theoretic concept that underlies the G-test, namely conditional mutual information. This aspect grants it wide applicability in data sciences.

Bayesian Inference under Small Sample Sizes Using General Noninformative Priors

This paper proposes a Bayesian inference method for problems with small sample sizes. A general type of noninformative prior is proposed to formulate the Bayesian posterior. It is shown that this type of prior can represent a broad range of priors such as classical noninformative priors and asymptotically locally invariant priors and can be derived as the limiting states of normal-inverse-Gamma conjugate priors, allowing for analytical evaluations of Bayesian posteriors and predictors. The performance of different noninformative priors under small sample sizes is compared using the likelihood combining both fitting and prediction performances. Laplace approximation is used to evaluate the likelihood. A realistic fatigue reliability problem was used to illustrate the method. Following that, an actual aeroengine disk lifing application with two test samples is presented, and the results are compared with the existing method.

Fuzzy Control of a Servomechanism: Practical Approach using Mamdani and Takagi- Sugeno Controllers

The main objective of this work is to propose two fuzzy controllers: one based on the Mamdani inference method and another controller based on the Takagi- Sugeno inference method, both will be designed for application in a position control system of a servomechanism. Some comparations between the methods mentioned above will be made with regard to the performance of the system in order to identify the advantages of the Takagi- Sugeno method in relation to the Mamdani method in the presence of disturbances and nonlinearities of the system. Some results of simulation and practical application are presented and results obtained showed that controllers based on Takagi- Sugeno method is more efficient than controllers based on Mamdani method for this specific application.

Systematic inference of regulation by protein kinases finds surprising level of transcription factor deactivation

Gene expression is controlled by pathways of regulatory factors often involving the activity of protein kinases on transcription factor proteins. Despite this well established mechanism, the number of well described pathways that include the regulatory role of protein kinases on transcription factors is surprisingly scarce in eukaryotes.To address this, PhosTF was developed to infer functional regulatory interactions and pathways in both simulated and real biological networks, based on linear cyclic causal models with latent variables. GeneNetWeaverPhos, an extension of GeneNetWeaver, was developed to allow the simulation of perturbations in known networks that included the activity of protein kinases and phosphatases on gene regulation. Over 2000 genome-wide gene expression profiles, where the loss or gain of regulatory genes could be observed to perturb gene regulation, were then used to infer the existence of regulatory interactions, and their mode of regulation in the budding yeast Saccharomyces cerevisiae.Despite the additional complexity, our inference performed comparably to the best methods that inferred transcription factor regulation assessed in the DREAM4 challenge on similar simulated networks. Inference on integrated genome-scale data sets for yeast identified ∼8800 protein kinase/phosphatase-transcription factor interactions and ∼6500 interactions among protein kinases and/or phosphatases. Both types of regulatory predictions captured statistically significant numbers of known interactions of their type. Surprisingly, kinases and phosphatases regulated transcription factors by a negative mode or regulation (deactivation) in over 70% of the predictions.Author summaryIn this work we addressed the challenging problem of inferring regulation by protein kinases and phosphatases via their activity on transcription factors. Although many protein kinase activity predictors have been developed for classes of protein kinases on specific amino acids within target sequences, our approach (PhosTF) provides predictions of regulatory activity for specific protein kinases and phosphatases on specific transcription factors. Our inference approach achieves this using the functional output observed in gene expression data of gene knock out stains, along with known transcription factor regulatory interactions. We formulated and tested a model for inference of regulation as well as a model for simulation of genes expression, transcription and translation. The simulation was used for in-silico validation of the inference method, which performed comparably to the best performers on simpler inference problem in the DREAM4 competition. The inference method was then applied to yeast expression data, with significant validation by known kinase/phosphatase interactions. Over 15300 novel regulatory interactions were predicted, suggesting that kinase activity provided a surprising level of repression of gene expression, either through the deactivation of activators or the activation of repressors.