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Medical data mining using evolutionary computation.

In this paper, we introduce a system for discovering medical knowledge by learning Bayesian networks and rules. Evolutionary computation is used as the search algorithm. The Bayesian networks can provide an overall structure of the relationships among the attributes. The rules can capture detailed and interesting patterns in the database. The system is applied to real-life medical databases for limb fracture and scoliosis. The knowledge discovered provides insights to and allows better understanding of these two medical domains.

Algorithms↗

Data mining for simple sequence repeats in expressed sequence tags from barley, maize, rice, sorghum and wheat.

Plant genomics projects involving model species and many agriculturally important crops are resulting in a rapidly increasing database of genomic and expressed DNA sequences. The publicly available collection of expressed sequence tags (ESTs) from several grass species can be used in the analysis of both structural and functional relationships in these genomes. We analyzed over 260000 EST sequences from five different cereals for their potential use in developing simple sequence repeat (SSR) markers. The frequency of SSR-containing ESTs (SSR-ESTs) in this collection varied from 1.5% for maize to 4.7% for rice. In addition, we identified several ESTs that are related to the SSR-ESTs by BLAST analysis. The SSR-ESTs and the related sequences were clustered within each species in order to reduce the redundancy and to produce a longer consensus sequence. The consensus and singleton sequences from each species were pooled and clustered to identify cross-species matches. Overall a reduction in the redundancy by 85% was observed when the resulting consensus and singleton sequences (3569) were compared to the total number of SSR-EST and related sequences analyzed (24 606). This information can be useful for the development of SSR markers that can amplify across the grass genera for comparative mapping and genetics. Functional analysis may reveal their role in plant metabolism and gene evolution.

Computational Biology↗

Association of genes to genetically inherited diseases using data mining.

Although approximately one-quarter of the roughly 4,000 genetically inherited diseases currently recorded in respective databases (LocusLink, OMIM) are already linked to a region of the human genome, about 450 have no known associated gene. Finding disease-related genes requires laborious examination of hundreds of possible candidate genes (sometimes, these are not even annotated; see, for example, refs 3,4). The public availability of the human genome draft sequence has fostered new strategies to map molecular functional features of gene products to complex phenotypic descriptions, such as those of genetically inherited diseases. Owing to recent progress in the systematic annotation of genes using controlled vocabularies, we have developed a scoring system for the possible functional relationships of human genes to 455 genetically inherited diseases that have been mapped to chromosomal regions without assignment of a particular gene. In a benchmark of the system with 100 known disease-associated genes, the disease-associated gene was among the 8 best-scoring genes with a 25% chance, and among the best 30 genes with a 50% chance, showing that there is a relationship between the score of a gene and its likelihood of being associated with a particular disease. The scoring also indicates that for some diseases, the chance of identifying the underlying gene is higher.

Chromosome Mapping↗

Data mining the p53 pathway in the Fugu genome: evidence for strong conservation of the apoptotic pathway.

The p53 tumour suppressor gene belongs to a small family of related proteins that includes two other members, p63 and p73. Phylogenetic and functional studies suggest that p63 and p73 are ancient genes that have essential roles in normal development, whereas p53 seems to have evolved more recently to prevent cell transformation. In mammalian cells, a plethora of proteins have been found to specifically regulate p53 activity. The genome of the fish Fugu rubripes has been recently published. It is the second vertebrate genome for which the entire sequence is now available. Phylogenetic studies are essential in order to analyse and define signalling pathways important for cell cycle regulation. The presence or absence of a critical member in any pathway can shed light about the evolution of these pathways. The Fugu genome databank has been analysed for several members of the p53 network, including p53, p63 and p73. A good conservation of the network that regulates p53 stability and apoptosis has been found. We also discovered that some cofactors that cooperate with p53 for apoptosis are also well conserved and belong to multigene families not detected in the human genome.

Animals↗

G-language Genome Analysis Environment: a workbench for nucleotide sequence data mining.

SUMMARY: G-language Genome Analysis Environment (G-language GAE) is an open source generic software package aimed for higher efficiency in bioinformatics analysis. G-language GAE has an interface as a set of Perl libraries for software development, and a graphical user interface for easy manipulation. Both Windows and Linux versions are available. AVAILABILITY: From http://www.g-language.org/ under GNU General Public License. CD-ROMs are distributed freely in major conferences.

Database Management Systems↗

Acupoint Selection Patterns and Potential Mechanisms of Acupuncture in Knee Osteoarthritis: A Combined Data Mining and Network Pharmacology Study.

OBJECTIVE: To identify the core acupoint prescription and Kellgren-Lawrence (K-L) grade-dependent compatibility patterns of acupuncture for KOA through complex network analysis, and to predict the potential molecular mechanisms underlying the core prescription via network pharmacology. METHODS: Literature was retrieved from PubMed, EMbase, Cochrane Library, Web of Science, CNKI, Wanfang, VIP, and SinoMed (inception to September 3, 2025). Frequency, association rule, complex network, and K-L grade subgroup analyses were applied. Potential targets of the core prescription were identified via network pharmacology and intersected with disease targets from OMIM, Therapeutic Target, GeneCards, and DrugBank. A protein-protein interaction (PPI) network was constructed, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to explore the potential molecular mechanisms. RESULTS: We included 522 studies, yielding 582 prescriptions involving 123 acupoints. The core prescription comprised 24 acupoints, including Dubi (ST35), Neixiyan (EX-LE4), Liangqiu (ST34), Xuehai (SP10), Zusanli (ST36), Yanglingquan (GB34), Yinlingquan (SP9), among others. K-L subgroup analysis revealed ST35, GB34, SP9, and SP10 as universal core acupoints. The mild-to-moderate subgroup mainly used local acupoints, while the moderate-to-severe subgroup centered on ST35, with increased distal acupoint usage and higher degree values. Network pharmacology analysis identified 77 overlapping targets. Core targets included tumor necrosis factor (TNF), interleukin 6 (IL6), interleukin 1 beta (IL1B), tumor protein p53 (TP53), matrix metallopeptidase 9 (MMP9), signal transducer and activator of transcription 3 (STAT3), transforming growth factor beta 1 (TGFB1), caspase 3 (CASP3), and B-cell lymphoma 2 (BCL2), which were enriched in inflammation and immunity, cartilage metabolism, and tissue repair pathways. CONCLUSION: The core acupoint prescription for KOA features local acupoints combined with distal ones, exhibiting distinct patterns across K-L grades. Our computational findings suggest that core acupoints may potentially delay knee joint degeneration by synergistically regulating inflammation, cartilage metabolism, apoptosis, and tissue repair, although these predictions require experimental validation. These findings provide preliminary evidence and a theoretical basis for standardized clinical point selection and further mechanistic research.

KOA↗

Improving dialysis services through information technology: from telemedicine to data mining.

This paper discusses the issues related to use of Information Technology (IT) solutions in dialysis, and describes the implementation of some of them in a medium size dialysis center. First, starting from the analysis of the organization of public-health nephrology services, the potential role of IT is highlighted. Second, the main directions for IT exploitation in dialysis, namely telemedicine and automated monitoring of dialysis sessions are discussed. Third, the on-field implementation of these services is described, together with some preliminary results. The work here presented shows how IT may improve dialysis services by ameliorating quality and reducing costs.

Ambulatory Care Facilities↗