This study proposes a drug recommendation model based on a multi-objective optimization algorithm, aiming to provide personalized medication strategies for Systemic Lupus Erythematosus and Antiphospholipid Syndrome patients, taking into account individual differences and medication risks. Using a multi-objective optimization algorithm, the model comprehensively considers drug efficacy, safety and individual differences to achieve multi-dimensional screening of drugs. Drugs are categorized as therapeutic drugs for Systemic Lupus Erythematosus, Antiphospholipid Syndrome and other related complications, ensuring that recommendations meet different patient needs. Experimental results show that the Drug-ESIC-PC recommendation model we proposed has an accuracy of$\text{9 2 \%, } \text{8 8 \%}$, and$\text{8 4 \%}$for recommending two, three, and four drugs, respectively.

Large-scale integration covering genetic traits, compound properties, and pharmacological mechanisms opens new avenues for drug repurposing in retinal diseases. This study presents SNF-LapDTI, a framework combining similarity network fusion (SNF) with Laplacian regularized least squares (LapRLS). SNF creates a unified similarity representation from heterogeneous sources, while LapRLS builds predictive functions in drug and target spaces to infer interaction likelihoods. Evaluations on multiple datasets confirm superiority, reaching AUROC 95.91% and AUPR 95.49% on RD.

With the acceleration of global energy transition and the popularization of Electric Vehicles (EVs), regional electricity demand and EV charging price forecasting has become an important issue in power system management. In this paper, a Cross-Scale Adaptive Weighted Fusion Network(CSAWFNet) based on triple weighted fusion LSTM model is proposed to address the limitations of the existing forecasting methods in dealing with the complex and changing power demand and price fluctuations. The feature extraction was first performed using machine learning algorithms, and then as inputs to the model. The model can effectively capture the short-term high-frequency changes and long-term trends of electricity demand and price fluctuations through the improvements of sliding window, multi-scale memory fusion mechanism, adaptive memory pruning mechanism, and cross-layer connections. The experimental re-sults show that CSAWFNet outperforms traditional models in forecasting regional electricity demand and EV charging prices, particularly during peak and trough load periods and price fluctuations.

Cardiovascular disease and depression often require combined use of cardiovascular and psychotropic drugs. This paper introduces Multi-Feature Fusion Learning for Discovering Synergistic Relationships (MFFL-DSR), a method to predict drug interactions. It first constructs matrices of drug features, including classification, targets, enzymes, pathways, and molecular structure. Drugs from different feature domains are then projected into a shared interaction domain. A regularization term is formulated to represent drug pairing relationships in the inter-action space, forming the MFFL-DSR objective function. Finally, iterative optimization identifies all potential drug combinations. The results show that MFFL-DSR outperforms baseline methods in six metrics: AUPR, AUC, Precision, Accuracy, Recall, and F1 score.

In this paper, we proposed a Weighted Subgraph Clustering for Protein Complex Identification (WSC-PCI) method , which combined with Gene Ontology (GO) annotations and improved Edge Clustering Coefficient (ECC) to indentify the protein complex over weighted Protein- Protein Interaction (PPI) network. By using the comprehensive weighted method to calculate the node weights within the PPI network, in order to mine the valuable and core subgraphs, which are related to the biologically significant and real protein complexes based on the core-attachment structure requirements. And then, calculating Biocompatibility value (BA-value) to identify potential proteins, which have more stable and good biocompatibility. By comparing with multiple algorithms on real datasets of different species, the results show that WSC-PCI outperforms other methods with regard to recall, precision and MMR, and the authenticity and biological significance of the identification results are verified by p-value analysis.

Healthcare data, collected from multiple sources and representing various perspectives, has become an increasingly challenging and important research topic. It permeates many aspects of evidence-based clinical practice, the healing process, and health policy formulation. However, the characteristics of redundancy, diversity, volume, inconsistency, and incompleteness make it difficult to capture valuable semantic properties and linguistic relationships. In this paper, we propose a CNN-based Bidirectional Extension of Phrase Boundary (BEPB) approach to reduce over-reliance on term frequency and mine multiple latent topical key phrases, aiming to improve the quality of key phrases in downstream Natural Language Processing (NLP) applications. The experiments indicate that the BEPB trained from unstructured user-contributed content on health social media sites has successfully mined more relevant topical phrases, particularly in the areas of clinical symptoms and co-occurrence patterns. To summarize, these findings serve as a key contributor to the advancements in Evidence-Based Medicine (EBM), paving the way for improvements in the prevention, diagnosis, treatment, and nursing of diseases.

A number of block-based image-denoising methods have been presented in the literature. Those methods, however, are generally adapted to denoising the Gaussian noise, and subsequently do not show good performance for denoising random-valued impulse, and salt-and-pepper noise. We propose an efficient block-based image-denoising method, which is devised specially for fast denoising of impulse noise. The method first constructs a set of array pointers to image blocks containing a specific pixel value at a specific location. With this scheme, finding of blocks similar to a given block can be done by considering only the blocks pointed by the pointers corresponding to the pixel values of the block without comparing all the blocks in the input image. The experimental results show that the proposed method can achieve superior denoising performance in terms of computational time and signal-to-noise ratio measure.

Weighted itemset mining, which is one of the important areas in frequent itemset mining, is an approach for mining meaningful itemsets considering different importance or weights for each item in databases. Because of the merit of the weighted itemset mining, various related works have been studied actively. As one of the methods in the weighted itemset mining, FWI (Frequent Weighted Itemset) mining calculates weights of transactions from weights of items and then finds FWIs based on the transaction weights. However, previous FWI mining methods still have limitations in terms of runtime and memory usage performance. For this reason, in this paper, we propose two algorithms for mining FWIs more efficiently from databases with weights of items. In contrast to the previous approaches storing transaction IDs for mining FWIs, the proposed methods employ new types of prefix tree structures and mine these patterns more efficiently without storing any transaction ID. Through extensive experimental results in this paper, we show that the proposed algorithms outperform state-of-the-art FWI mining algorithms in terms of runtime, memory usage, and scalability.

The prevention of hypertension is one of the most important topics in health research. In the most of the previous studies used statistical methods for analyzing the association between hypertension prevalence and dietary. However, statistical methods have some limitation which are, it is difficult to interpret variables interaction at a time. Thus we apply the data mining techniques for generation of prognosis factors based on association rule mining. In our experiment, we conducted Korean National Health and Nutrient Examination Survey (KNHANES) data from 2007 to 2014. We used to filter-based feature selection method for find prognosis factors and we generate the rules based on discovered risk factors of prognosis in hypertension. We evaluated discovered rules by support and confidence. In the results shows that, we can find useful rules for prognosis of hypertension. We expected to support medical decision making and easy to interpret prognosis of hypertension.

In frequent pattern mining, items are considered as having the same importance in a database and their occurrence are represented as binary values in transactions. In real-world databases, however, items not only have relative importance but also are represented as non-binary values in transactions . High utility pattern mining is one of the most essential issues in the pattern mining field, which recently emerged to address the limitation of frequent pattern mining. Meanwhile, tree construction with a single database scan is significant since a database scan is a time-consuming task. In utility mining, an additional database scan is necessary to identify actual high utility patterns from candidates. In this paper, we propose a novel tree structure, namely SIQ-Tree (Sum of Item Quantities), which captures database information through a single-pass. Moreover, a restructuring method is suggested with strategies for reducing overestimated utilities. The proposed algorithm can construct the SIQ-Tree with only a single scan and decrease the number of candidate patterns effectively with the reduced overestimation utilities, through which mining performance is improved. Experimental results show that our algorithm outperforms a state-of-the-art one in terms of runtime and the number of generated candidates with a similar memory usage.

In electricity customer classification, the most important task is to avoid the curse of dimensionality problem, as the consumption diagrams have a large number of dimensions. To avoid the curse of dimensionality problem, field indices (load shape factor) are often used instead of consumption diagrams. Field indices are directly extracted from consumption diagrams according to a predefined formula. Previous studies show that the most important thing for defining such a formula is to find meaningful time intervals from consumption diagrams. However, the inconvenient thing is that there are still a lack of details to explain how to define such time intervals. In our study, we propose a data mining--based method named SFATIE to support the extraction of field indices. The performance of the proposed method is evaluated by comparing it with other dimensionality reduction methods during the classification. For the classification, most often we have used classification methods like C5.0, SVM, Neural Net, Bayes Net, and Logistic. The experimental results show that our method is better or close to other dimensionality reduction methods. In addition, the experimental results show that our proposed method can produce the good quality of field indices and that these indices can improve the performance of electricity customer classification.

BACKGROUND: Recently, rapid improvements in technology and decrease in sequencing costs have made RNA-Seq a widely used technique to quantify gene expression levels. Various normalization approaches have been proposed, owing to the importance of normalization in the analysis of RNA-Seq data. A comparison of recently proposed normalization methods is required to generate suitable guidelines for the selection of the most appropriate approach for future experiments. RESULTS: In this paper, we compared eight non-abundance (RC, UQ, Med, TMM, DESeq, Q, RPKM, and ERPKM) and two abundance estimation normalization methods (RSEM and Sailfish). The experiments were based on real Illumina high-throughput RNA-Seq of 35- and 76-nucleotide sequences produced in the MAQC project and simulation reads. Reads were mapped with human genome obtained from UCSC Genome Browser Database. For precise evaluation, we investigated Spearman correlation between the normalization results from RNA-Seq and MAQC qRT-PCR values for 996 genes. Based on this work, we showed that out of the eight non-abundance estimation normalization methods, RC, UQ, Med, TMM, DESeq, and Q gave similar normalization results for all data sets. For RNA-Seq of a 35-nucleotide sequence, RPKM showed the highest correlation results, but for RNA-Seq of a 76-nucleotide sequence, least correlation was observed than the other methods. ERPKM did not improve results than RPKM. Between two abundance estimation normalization methods, for RNA-Seq of a 35-nucleotide sequence, higher correlation was obtained with Sailfish than that with RSEM, which was better than without using abundance estimation methods. However, for RNA-Seq of a 76-nucleotide sequence, the results achieved by RSEM were similar to without applying abundance estimation methods, and were much better than with Sailfish. Furthermore, we found that adding a poly-A tail increased alignment numbers, but did not improve normalization results. CONCLUSION: Spearman correlation analysis revealed that RC, UQ, Med, TMM, DESeq, and Q did not noticeably improve gene expression normalization, regardless of read length. Other normalization methods were more efficient when alignment accuracy was low; Sailfish with RPKM gave the best normalization results. When alignment accuracy was high, RC was sufficient for gene expression calculation. And we suggest ignoring poly-A tail during differential gene expression analysis.

BACKGROUND: Pairwise relationships extracted from biomedical literature are insufficient in formulating biomolecular interactions. Extraction of complex relations (namely, biomedical events) has become the main focus of the text-mining community. However, there are two critical issues that are seldom dealt with by existing systems. First, an annotated corpus for training a prediction model is highly imbalanced. Second, supervised models trained on only a single annotated corpus can limit system performance. Fortunately, there is a large pool of unlabeled data containing much of the domain background that one can exploit. RESULTS: In this study, we develop a new semi-supervised learning method to address the issues outlined above. The proposed algorithm efficiently exploits the unlabeled data to leverage system performance. We furthermore extend our algorithm to a two-phase learning framework. The first phase balances the training data for initial model induction. The second phase incorporates domain knowledge into the event extraction model. The effectiveness of our method is evaluated on the Genia event extraction corpus and a PubMed document pool. Our method can identify a small subset of the majority class, which is sufficient for building a well-generalized prediction model. It outperforms the traditional self-training algorithm in terms of f-measure. Our model, based on the training data and the unlabeled data pool, achieves comparable performance to the state-of-the-art systems that are trained on a larger annotated set consisting of training and evaluation data.

This paper proposes a new method of patterns analysis using SOM in medical data sets for medical treatment service under ubiquitous computing environment which is required by real time accessibility and agility. In this paper, it is necessary for us to classify disease patterns in the medical historical record to join the information of patient, using SOM neural network with input vectors of different features, disease code, input factors in order to take the medical treatment service in medical data sets, to reduce patients' search effort to get the information of diagnosis for recovering their health and to improve the rate of accuracy. To verify improved performance, we make experiments with dataset collected in medical center.

Generally, association rule mining uses only a single minimum support threshold for the whole database. This model implicitly assumes that all items in the database have the same nature. In real applications, however, each item can have different nat

Maximal frequent pattern mining has been suggested for data mining to avoid generating a huge set of frequent patterns. Conversely, weighted frequent pattern mining has been proposed to discover important frequent patterns by considering the weighted

MOTIVATION: Gene selection for cancer classification is one of the most important topics in the biomedical field. However, microarray data pose a severe challenge for computational techniques. We need dimension reduction techniques that identify a small set of genes to achieve better learning performance. From the perspective of machine learning, the selection of genes can be considered to be a feature selection problem that aims to find a small subset of features that has the most discriminative information for the target. RESULTS: In this article, we proposed an Ensemble Correlation-Based Gene Selection algorithm based on symmetrical uncertainty and Support Vector Machine. In our method, symmetrical uncertainty was used to analyze the relevance of the genes, the different starting points of the relevant subset were used to generate the gene subsets and the Support Vector Machine was used as an evaluation criterion of the wrapper. The efficiency and effectiveness of our method were demonstrated through comparisons with other feature selection techniques, and the results show that our method outperformed other methods published in the literature.

Coronary heart disease is being identified as the largest single cause of death along the world. The aim of a cardiac clinical information system is to achieve the best possible diagnosis of cardiac arrhythmias by electronic data processing. Cardiac information system that is designed to offer remote monitoring of patient who needed continues follow up is demanding. However, intra- and interpatient electrocardiogram (ECG) morphological descriptors are varying through the time as well as the computational limits pose significant challenges for practical implementations. The former requires that the classification model be adjusted continuously, and the latter requires a reduction in the number and types of ECG features, and thus, the computational burden, necessary to classify different arrhythmias. We propose the use of adaptive learning to automatically train the classifier on up-to-date ECG data, and employ adaptive feature selection to define unique feature subsets pertinent to different types of arrhythmia. Experimental results show that this hybrid technique outperforms conventional approaches and is, therefore, a promising new intelligent diagnostic tool.

In data mining area, weight based sequential pattern mining has been suggested to find important sequential patterns by considering the weights of sequential patterns. More extensions with weight constraints have been proposed such as mining weighted

Environment Observation and Forecasting System(EOFS) is a application for monitoring and providing a forecasting about environmental phenomena. We design an air pollution monitoring system which involves a context model and a flexible data acquisition policy. The context model is used for understanding the status of air pollution on the remote place. It can provide an alarm and safety guideline depending on the condition of the context model. It also supports the flexible sampling interval change for effective the tradeoff between sampling rates and battery lifetimes. This interval is changed depending on the pollution conditions derived from the context model. It can save the limited batteries of geosensors, because it reduces the number of data transmission.

It is well known that amino acids in peptides and proteins show individually distinct preferences for secondary structural conformations. While each amino acid is associated with multiple physicochemical properties, most existing statistical analysis methods treat amino acids separately and independently in calculating the frequencies of residues at each site of protein sequences. Such approaches remain at describing coarse-level tendencies of amino acid preferences for particular conformations. We propose a more advanced and refined method in which co-occurring patterns of two or three physicochemical attributes are examined to identify conformational preferences of amino acids at the sub-residue scale.

Enzymes are the proteins that accelerate the rate of chemical reaction, and both their structures and dynamics may be important to their function of catalyzing biochemical reactions. For the function prediction and classification of enzymes, many methods based on sequence similarity to detect similar proteins have been developed. However, these methods often miscarry in the case of the absence of similar sequences or poor similarity among proteins. Therefore, many researchers have been developing alternative approaches that assign function from protein features without consideration of sequence similarity. In this paper, we propose a method of sequence-driven feature extraction and enzyme functional classification using only the features of protein sequence, excluding predicted secondary structures and annotation information of protein databases. Our experimental results demonstrate that the enzyme classification based on the Chi-Squared ranking method among various attribute selection methods is efficient. Also, we And that amino acid composition of specific enzyme differs from composition of other enzymes.

Similarity search for protein 3D structures become complex and computationally expensive due to the fact that the size of protein structure databases continues to grow tremendously. Recently, fast structural similarity search systems have been required to put them into practical use in protein structure classification whilst existing comparison systems do not provide comparison results on time. Our approach uses multi-step processing that composes of a preprocessing step to represent geometry of protein structures with spatial objects, a filter step to generate a small candidate set using approximate topological string matching, and a refinement step to compute a structural alignment. This paper describes the preprocessing and filtering for fast similarity search using the discovery of topological patterns of secondary structure elements based on spatial relations. Our system is fully implemented by using Oracle 8i spatial. We have previously shown that our approach has the advantage of speed of performance compared with other approach such as DALI. This work shows that the discovery of topological relations of secondary structure elements in protein structures by using spatial relations of spatial databases is practical for fast structural similarity search for proteins.

Estimation of the number of clusters is an essential processing step for various applications. Existing approaches search for an optimal solution by computing and comparing a validity measure for all feasible configurations, and tend to under-estimate the number of clusters incorrectly. We propose a fast and robust method to estimate the number of clusters without adopting an exhaustive search. Our scheme first extracts the relationship of neighboring features, and then uses this information to partition the clusters. The superb performance of the method is verified by the simulation results in determining the number of texture segments in textured images.

In this paper, we present a new rotation-invariant texture description scheme based on an explicit transform of texture features to shapes. Texture features are obtained in a short computational time by applying the partial form of Gabor functions. These features are then transformed to 2-D closed shapes, and their moment invariants and global shape descriptors are derived to classify the rotated textures. Experiments using various texture samples showed high rates of correct classifications.

For the comparison and evaluation of neuro-fuzzy, neural network, and maximum likelihood classifiers, a land cover classification activity was performed using multispectral IKONOS data of part of Daejeon City in Korea. For this purpose, a neuro-fuzzy program was derived from a generic model of a three-layer fuzzy perceptron. The results of the classification and method comparison show that the neuro-fuzzy classifier was the most accurate method. Thus, the neurofuzzy model is more suitable for classifying a mixed-composition area such as the natural environment of the Korean peninsula. The neuro-fuzzy classifier is superior in its suppression of classification errors for mixed land cover signatures. The classified land cover information is important when the results of the classification are integrated into a geographical information system.