Demelo Lao

Signal peptides (SP) play an important role in protein transport and sorting to the different compartment of the cell. Although SPs have varying lengths and do not have a consensus sequence, almost all possess a common three-region structure: the positively charged n-region, the hydrophobic h-region, and the c-region where the cleavage site occurs [7].

Reported performance of existing transmembrane (TM) topology prediction methods were often based on evaluations which neglected the risk of signal peptides (SP) being predicted as putative TM as well. Here, we evaluated 12 selected TM topology prediction methods (TMpred, TopPred II, DAS, TMAP, MEMSAT 2, SOSUI, PRED-TMR2, TMHMM 2.0, HMMTOP 2.0, SPLIT 3.5, TM Finder, and MPEx) for the effect of SP in prediction performance considering three SP treatments, namely: "remain" (untreated), "removed first", and "removed later". The results showed that the presence of SP significantly affected the prediction performance of the 12 selected TM topology prediction methods for all three predicted attributes (the number of transmembrane segments (TMSs), the number of TMSs plus position, and the N-tail location) and for the predicted topology (combined predictions of three attributes) by causing a reduction in prediction accuracy. In particular, lower prediction accur...

The perceived difficulties and challenges in teaching geography compels the use of innovative instructional approach. Hence, this study developed an augmented reality (AR) application for m-learning, ‘ARGeo Philippines’, to provide undergraduate-level students an interactive learning experience of Philippine geography. We applied the waterfall model in software development, and subjected the AR application to software quality test, usability test, and pedagogical effectiveness test. Using the SCORM standard, software quality requirements for the AR application was met generally. For usability as mobile application, the AR application was rated overall as ‘highly’ acceptable (Mean=4.15, SD= 0.29) using the MLUAT instrument. Pedagogical potential still requires further verification due to the statistically insignificant result; however, using the AR application resulted to higher learning gains than those from the traditional learning approach. Thus, it might be beneficial to further ...

A method for designing and training neural networks using genetic algorithms is proposed, with the aim of getting the optimal structure of the network and the optimized parameter set simultaneously. For this purpose, a fitness function depending on both the output errors and simpleness in the structure of the network is introduced. The validity of this method is checked by experiments on four logical operation problems: XOR, 6XOR, 4XOR-2AND, and 2XOR-2AND-2OR; and on two other problems: 4-bit pattern copying and an 8 ! 8-encoder/decoder. It is concluded that, although this method is less powerful for disconnected networks, it is useful for connected ones.

Purpose The purpose of this paper is to develop a web-based interactive learning object (ILO) of introductory Computer Science (CS) concept on recursion and compare two feedback methods in the learning assessment part. Design/methodology/approach Test driven development (TDD) approach was used to develop ILO. The authors adapted Multimedia Educational Resource for Learning and Online Teaching (MERLOT) standard instrument to evaluate ILO’s effectiveness as an e-learning tool. Three respondents, from a list of pre-identified prospective evaluators, were randomly chosen and served as raters for MERLOT, while 32 student-respondents coming from first-year Math and CS undergraduate majors were randomly assigned to each ILO version implementing either one of the two feedback methods. Findings ILO obtained mean ratings above 4 (in scale 1-5) in three MERLOT criteria, namely, potential effectiveness as teaching tool, ease of use, and quality of content, which is rated highest (mean=4.40, SD=...

Reported performance of existing transmembrane (TM) topology prediction methods were often based on evaluations which neglected the risk of signal peptides (SP) being predicted as putative TM as well. Here, we evaluated 12 selected TM topology prediction methods (TMpred, TopPred II, DAS, TMAP, MEMSAT 2, SOSUI, PRED-TMR2, TMHMM 2.0, HMMTOP 2.0, SPLIT 3.5, TM Finder, and MPEx) for the effect of SP in prediction performance considering three SP treatments, namely: "remain" (untreated), "removed first", and "removed later". The results showed that the presence of SP significantly affected the prediction performance of the 12 selected TM topology prediction methods for all three predicted attributes (the number of transmembrane segments (TMSs), the number of TMSs plus position, and the N-tail location) and for the predicted topology (combined predictions of three attributes) by causing a reduction in prediction accuracy. In particular, lower prediction accur...

Abstract We developed a computer-based learning tool that is an Interactive Learning Object (ILO) for protein synthesis, specifically for eukaryotic organisms. Two (2) modules were developed, each for DNA transcription and protein translation, respectively. The ILO ...

We selected 10 transmembrane (TM) prediction methods (KKD, TMpred, TopPred II, DAS, TMAP, MEMSAT 2, SOSUI, PRED-TMR2, TMHMM 2.0 and HMMTOP 2.0) and re-assessed its prediction performance using a reliable dataset with 122 entries of experimentally-characterized TM topologies. Then, we improved prediction performance by a consensus prediction method. Prediction performance during re-assessment and consensus prediction were based on four attributes: (i) the number of transmembrane segments (TMSs), (ii) the number of TMSs plus TMS-position, (iii) N-tail location and (iv) TM topology. We noted that hidden Markov model-based methods dominate over other methods by individual prediction performance for all four attributes. In addition, all top-performing methods generally were model-based. Among prokaryotic sequences, HMMTOP 2.0 solely topped among other methods with prediction accuracies ranging from 64% to 86% across all attributes. However, among eukaryotic sequences, prediction performance for all the attributes was relatively poor compared with prokaryotic ones. On the other hand, our results showed that our proposed consensus prediction method significantly improved prediction performance by, at least, an additional nine percentage points particularly among prokaryotic sequences for the number of TMS (84%), number of TMS and position (80%), and TM topology attributes (74%). Although our consensus prediction method improved also the prediction performance among eukaryotic sequences, the obtained accuracies for all attributes were relatively lower than that obtained by prokaryotic counterparts particularly for TM topology.