Dr DMASS
Summary | Software Dr DMASS which has been developed to effectively analyze mass spectral data using multivariate analysis consist of three steps, (i) Peak Correction, (ii) Multivariate Data Processing, and (iii) Multivariate Analysis. In Peak Correction process, we correct experimental m/z values based on the relation between experimental and desired values in internal mass calibrants (IMCs). This software and its instruction manual is freely available at the present site. Java j2sdk-1.4.2. is required to be installed in the user's computer to use this software. (cited from the original site) |
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Data type | mass spectral data |
Dr DMASS+
Summary | Software DrDMASS+ has been developed to effectively analyze mass spectral data based on multivariate analysis. A flow diagram of Data processing consists of four stages, (i) Peak Correction, (ii) Multivariate Data Processing, (iii) Unsupervised Learning, and (iv) Supervised Learning(cited from the original site). Dr DMASS+ is an improved version of Dr DMASS ( https://medals.jp/elist/detail/116.html ) to add (iii)(iv). |
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Data type | mass spectral data |
FragmentAlign
Summary | This program is a JAVA program which can align metabolite peaks from GC-MS analysis using retention time and MS fragmentation pattern information and also GUI-based edit the alignment. It can use NIST format or MassBase MST format text files to form the alignment. As edit functions, it contains add/delete function of peak in peak group, add/delete function of peak group, merge function of peaks and peak groups, etc. It can also annotate aligned peaks using pre-defined data library of MS fragmentation pattern of known metabolites. User can edit the data library. Resulting alignment can save as text file. Other programs (Microsoft Excel, etc) can use this output file for comparative and/or statistical analysis. Old version (Metabolometrisc) is downloadable. |
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Data type | Metabolite repression , mass spectra |
GRIFFIN
Summary | It is a high-throughput system to predict GPCR - G-protein coupling selectively with the input of GPCR sequence and ligand molecular weight. This system consists of two parts:1) HMM section using family specific multiple alignment of GPCRs, 2)SVM section using physico-chemical feature vectors in GPCR sequence. |
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Data type | Ptotein-sequence , amino acid sequence |
GRisk
Summary | GRISK is a software for Windows that calculates genetic risk of Mendelian genetic diseases based on small pedigree data. It consists of a family data registration screen to register information on family lineage, and a disease information screen to register information on diseases and calculate the risk. By combining registered family data with disease information, it is possible to calculate genetic risk for family members. |
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Data type | Disease risk |
HapRisk
Summary | HapRisk is software that runs on Windows to calculate the expression probability of an unknown phenotype and obtained genotype data using an algorithm for simultaneous estimation of qualitative phenotype and haplotype. The purpose of this software is to calculate the onset risk of an individual at a clinical or laboratory site based on genotype data obtained from a sample. |
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Data type | Expression risk |
HEAT
Summary | H-InvDB Enrichment Analysis Tool (HEAT) is a data-mining tool for automatically identifying features specific to a given human gene set. HEAT searches for H-InvDB annotations that are significantly enriched in a user-defined gene set, as compared with the entire H-InvDB representative transcripts. This technique is called Gene Set Enrichment Analysis (GSEA), and is popularly used in analyzing results of microarray experiments. Fisher's exact probability is used in statistical tests of HEAT. |
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Data type | Annotation |
HESS
Summary | HESS has two databases. One is a toxicity knowledge database, which contains information on repeated dose toxicity and toxicity mechanisms. The other is a metabolism knowledge database containing rat metabolism maps and information on absorption, distribution, metabolism and excretion (ADME) in rats and humans. HESS allows chemicals to be categorized on the basis of structural, physicochemical and mechanistic similarities and helps predict the repeated dose toxicity of untested chemicals by means of the category approach.(cited from original site) |
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Data type | Chemical compound toxicity knowledge |