Learn More
Support vector machines (SVMs) have shown strong generalization ability in a number of application areas, including protein structure prediction. However, the poor comprehensibility hinders the success of the SVM for protein structure prediction. The explanation of how a decision made is important for accepting the machine learning technology, especially(More)
Prediction of protein secondary structures is an important problem in bioinformatics and has many applications. The recent trend of secondary structure prediction studies is mostly based on the neural network or the support vector machine (SVM). The SVM method is a comparatively new learning system which has mostly been used in pattern recognition problems.(More)
Recent discovery of the copy number variation (CNV) in normal individuals has widened our understanding of genomic variation. However, most of the reported CNVs have been identified in Caucasians, which may not be directly applicable to people of different ethnicities. To profile CNV in East-Asian population, we screened CNVs in 3578 healthy, unrelated(More)
In recent years, there have been many studies focusing on improving the accuracy of prediction of transmembrane segments, and many significant results have been achieved. In spite of these considerable results, the existing methods lack the ability to explain the process of how a learning result is reached and why a prediction decision is made. The(More)
SUMMARY The method for genome-wide association study (GWAS) based on copy number variation (CNV) is not as well established as that for single nucleotide polymorphism (SNP)-GWAS. Although there are several tools for CNV association studies, most of them do not provide appropriate definitions of CNV regions (CNVRs), which are essential for CNV-association(More)
BACKGROUND Since hepatocellular carcinoma (HCC) is one of the leading causes of cancer death worldwide, it is still important to understand hepatocarcinogenesis mechanisms and identify effective markers for tumor progression to improve prognosis. Amplification and overexpression of Tropomyosin3 (TPM3) are frequently observed in HCC, but its biological(More)
SecA is an important component of protein translocation in bacteria, and exists in soluble and membrane-integrated forms. Most membrane prediction programs predict SecA as being a soluble protein, with the exception of TMpred and Top-Pred. However, the membrane associated predicted segments by TMpred and TopPred are inconsistent across bacterial species in(More)
The full extent of chromosomal alterations and their biological implications in early breast carcinogenesis has not been well examined. In this study, we aimed to identify chromosomal alterations associated with poor prognosis in early-stage breast cancers (EBC). A total of 145 EBCs (stage I and II) were examined in this study. We analyzed copy number(More)
The explanation of a decision is important for the acceptance of machine learning technology in bioinformatics applications such as protein structure prediction. In past research, we have already combined SVM with decision tree to extract rules for understanding transmembrane segments prediction. However, rules we have gotten are as many as about 20,000.(More)