京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第20026号 / 情博第621号 / 新制||情||108(附属図書館) / 33122 / 京都大学大学院情報学研究科社会情報学専攻 / (主査)教授 田中 克己, 教授 吉川 正俊, 教授 黒橋 禎夫 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

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Pattern separation is the means by which the brain discriminates similar experiences. It enables retrieval of individuated memories without confusing them with other memories. It is the reason one remembers where one parked the car today and does not mix it up with where one parked it previously. Adult neurogenesis refers to the ongoing production of neurons in the mature brain. One of the likely roles of adult neurogenesis in the hippocampus is facilitating pattern separation. Induced reduction of adult neurogenesis in non-human animals is associated with depression- and anxiety-like behaviors. One possible explanation is that reduced neurogenesis leads to reduced pattern separation, further leading to overgeneralization of threat situations. Instead of perceiving threats where it should, the animal risks perceiving threats everywhere. Emotional resilience is the ability to recover from adversity with a minimum of lingering negative effects such as depression or anxiety. This thesis investigates whether pattern separation in the human hippocampus supports emotional resilience. I performed a systematic review of studies that used the Mnemonic Similarity Task – a memory task commonly used to measure human pattern separation – to investigate the relationship between pattern separation and anxiety. The results are inconclusive but suggest a possible interaction effect whereby pattern separation and high-arousal states like stress predict anxiety. Together with the evidence from the non-human animal studies, this suggests that reduced pattern separation as caused by reduced neurogenesis could make one vulnerable to developing anxiety disorders.

The Internet has marked this era as the information age. There is no precedent in the amazing amount of information, especially network news, that can be accessed by Internet users these days. As a result, the problem of seeking information in online news articles is not the lack of them but being overwhelmed by them. This brings huge challenges regarding processing of online news feeds, i.e., how to determine which news article is important, how to determine the quality of each news article, and how to filter irrelevant and redundant information. In this thesis, we propose a method for filtering redundant and less-informative RSS news articles that solves the problem of excessive number of news feeds observed in RSS news aggregators. Our filtering approach measures similarity among RSS news entries by using the Fuzzy-Set Information Retrieval model and a fuzzy equivalent relation for computing word/sentence similarity to detect redundant and less-informative news articles.

Similarity searches are an essential component to most bioinformatic applications. They form the bases of structural motif identification, gene identification, and insights into functional associations. With the rapid increase in the available genetic data through a wide variety of databases, similarity searches are an essential tool for accessing these data in an informative and productive way. In our chapter, we provide an overview of similarity searching approaches, related databases, and parameter options to achieve the best results for a variety of applications. We then provide a worked example and some notes for consideration. Homology detection is one of the most basic and fundamental problems at the heart of bioinformatics. It is central to problems currently under intense investigation in protein structure prediction, phylogenetic analyses, and computational drug development. Currently discriminative methods for homology detection, which are not readily interpretable, are substantially more powerful than their more interpretable counterparts, particularly when sequence identity is very low. Here I present a computational graph-based framework for homology inference using physiochemical amino acid properties which aims to both reduce the gap in accuracy between discriminative and generative methods and provide a framework for easily identifying the physiochemical basis for the structural similarity between proteins. The accuracy of my method slightly improves on the accuracy of PSI-BLAST, the most popular generative approach, and underscores the potential of this methodology given a more robust statistical foundation.