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Electrocardiognosis (a.k.a. Ecgnosis) is a software package designed to assist the physician in formulating diagnoses on 24h ambulatory ECG monitoring (Holter) recordings.
A package for analysing epidemiological data. Contains functions for directly and indirectly adjusting measures of disease frequency, quantifying measures of association on the basis of single or multiple strata of count data presented in a contingency table, and computing confidence intervals around incidence risk and incidence rate estimates. Miscellaneous functions for use in meta-analysis, diagnostic test interpretation, and sample size calculations.
FastQC aims to provide a simple way to do some quality control checks on raw sequence data coming from high throughput sequencing pipelines. It provides a modular set of analyses which you can use to give a quick impression of whether your data has any problems of which you should be aware before doing any further analysis. The main functions of FastQC are * Import of data from BAM, SAM or FastQ files (any variant) * Providing a quick overview to tell you in which areas there may be problems * Summary graphs and tables to quickly assess your data * Export of results to an HTML based permanent report * Offline operation to allow automated generation of reports without running the interactive application
FreeLIMS is a Laboratory Information Management System ( LIMS ). Manage samples; create sample types from methods & parameters; easily generate reports & certificates; fine tune user rights. FreeLIMS is Open Source and Free.
FreeMED is a Web-based medical records (EMR) and patient management system with a printing system, patient scheduling, an HL7 interface, a billing system, XML-RPC Web services, and many other features. It has an extensible modular architecture, allowing it to be customized based on individual practices without having to rewrite core components of the system. It is HIPAA compliant.
The GenomeTools genome analysis system is a free collection of bioinformatics tools.
Gnotary is an asynchronous peer-to-peer digital notary service based on email. The server polls POP3 mail boxes scanning for specified key words in the subject line. A digital signature of the email body is returned to the sender. The main purpose is to allow medical practitioners to establish a peer-to-peer based notary service for their electronic health records.
Gnumed Heckert gnu.tiny.png
GNUmed is an electronic medical record software, supporting paperless medical practices. Several interface languages are supported. It is a comprehensive scalable software solution for electronic medical practices with an emphasis on privacy protection, secure patient centric record sharing, decision support, and ease of use. GNUmed operates in a client-server environment where the client and the server are physically two different machines. Each software component i.e. Client / Server are distributed separately. It is alongside GNU Health, one of the medical solution of the GNU project.
Health Heckert gnu.tiny.png
GNU Health is a program designed for hospitals, offering the following functionalities:
  • Electronic Medical Record (EMR)
  • Hospital Information System (HIS)
  • Health Information System
It has a strong focus on family medicine and primary care, along with socio-economic circumstances. It uses the following disease and medical procedure standards: (ICD-10 / ICD-10-PCS). There are facilities to aid in choosing medicines; prescription writing; patient, hospital finacial, and lab administration tools; a database of 4,200 disease-related genes; epidemiological reporting; and much more. GNU Health is part of GNU Solidario, an NGO offering health and education to the underpriveledged through free software:
Programs for Information Topology Data Analysis Information Topology is a program written in Python (compatible with Python 3.4.x), with a graphic interface built using TKinter [1], plots drawn using Matplotlib [2], calculations made using NumPy [3], and scaffold representations drawn using NetworkX [4]. It computes all the results on information presented in the study [5], that is all the usual information functions: entropy, joint entropy between k random variables (Hk), mutual informations between k random variables (Ik), conditional entropies and mutual informations and provides their cohomological (and homotopy) visualisation in the form of information landscapes and information paths together with an approximation of the minimum information energy complex [5]. It is applicable on any set of empirical data that is data with several trials-repetitions-essays (parameter m), and also allows to compute the undersampling regime, the degree k above which the sample size m is to small to provide good estimations of the information functions [5]. The computational exploration is restricted to the simplicial sublattice of random variable (all the subsets of k=n random variables) and has hence a complexity in O(2^n). In this simplicial setting we can exhaustively estimate information functions on the simplicial information structure, that is joint-entropy Hk and mutual-informations Ik at all degrees k=<n and for every k-tuple, with a standard commercial personal computer (a laptop with processor Intel Core i7-4910MQ CPU @ 2.90GHz * 8) up to k=n=21 in reasonable time (about 3 hours). Using the expression of joint-entropy and the probability obtained using equation and marginalization [5], it is possible to compute the joint-entropy and marginal entropy of all the variables. The alternated expression of n-mutual information given by equation then allows a direct evaluation of all of these quantities. The definitions, formulas and theorems are sufficient to obtain the algorithm [5]. We will further develop a refined interface (help welcome) but for the moment it works like this, and requires minimum Python use knowledge. Please contact pierre.baudot [at] for questions, request, developments (etc.): [1] J.W. Shipman. Tkinter reference: a gui for python. . New Mexico Tech Computer Center, Socorro, New Mexico, 2010. [2] J.D. Hunter. Matplotlib: a 2d graphics environment. Comput. Sci. Eng., 9:22–30, 2007. [3] S. Van Der Walt, C. Colbert, and G. Varoquaux. The numpy array: a structure for efficient numerical computation. Comput. Sci. Eng., 13:22– 30, 2011. [4] A.A. Hagberg, D.A. Schult, and P.J. Swart. Exploring network structure, dynamics, and function using networkx. Proceedings of the 7th Python in Science Conference (SciPy2008). Gel Varoquaux, Travis Vaught, and Jarrod Millman (Eds), (Pasadena, CA USA), pages 11–15, 2008. [5] M. Tapia, P. Baudot, M. Dufour, C. Formisano-Tréziny, S. Temporal, M. Lasserre, J. Gabert, K. Kobayashi, JM. Goaillard . Information topology of gene expression profile in dopaminergic neurons doi:

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