PREDICTING WITH TECHNOLOGY: Bioinformatics helps apply computing power and modelling to assess the scope of biological research.

The PG programmes and modular courses offered by the Centre of Excellence in Bioinformatics, Pondicherry University, seek to turn out well-trained persons in bioinformatics.

Bioinformatics has been gaining increasing significance with growing focus on research in areas of genomics, proteomics, drug discovery and development, structural biology and many more related subjects over the years. The need of the hour — well-trained persons in bioinformatics from institutions imparting quality training.

P.P. Mathur, coordinator, Centre of Excellence in Bioinformatics at the Pondicherry University, describes bioinformatics as an interface of physics, chemistry and biology. “India was one of the first countries to start bioinformatics in the late 1980s. Bioinformatics has been evolving over the years and even the basic applications of the subject have expanded. No biologist can survive without bioinformatics,” he explains.

The centre is offering M.Sc. Bioinformatics and Ph.D. in Bioinformatics, besides modular courses. The M.Sc. course, with an intake of 31 students, is being offered under the University Grants Commission’s scheme of Innovative Programme-Teaching and Research in Interdisciplinary and Emerging Areas. Students who have completed undergraduation in any science subject are eligible to join the course.

Courses in bioinformatics include cell and molecular biology, physics and chemistry for biologists, data structures and programming concepts, genomics and proteomics, biophysical chemistry, statistics for biologists, structural biology, molecular modelling and drug design and applications of bioinformatics.

For M.R.N. Murthy, professor of Molecular Biophysics Unit, Indian Institute of Science, Bangalore, bioinformatics not only reveals new potential in biology but also provides the leads. “Every biological research costs money. Bioinformatics can reveal which way is more likely to succeed by giving us some leads. It has become extremely important for industries especially pharmaceuticals for drug discovery,” he elaborates.

In fact, courses in bioinformatics are gaining overwhelming response from students. For instance, the course at the university has been attracting students from across the country, Professor Mathur notes.

Going a step further, Pondicherry University along with Anna University and Madurai Kamaraj University will jointly offer a regular M.Sc. programme in Computational Biology from next year.

The Department of Biotechnology has sanctioned funds for the course which will have video-conferencing facility, and student selection will be on all India basis, says Professor Mathur.

Modular courses

The centre has also been offering modular courses for those employed in the industries and research scholars such as in drug designing and computer applications in bioinformatics. Each modular course will provide a certificate to the candidate, while a postgraduate diploma will be awarded to those completing all four modular courses.

Interestingly, many of the students aim at pursuing research in the field, he says. Bioinformatics has various applications in genomics, drug design and development, pharmacogenomics, proteomics and structural biology. Not all drugs are effective in all persons. So the concept of personalised drugs comes in and bioinformatics plays a vital role in addressing such issues of drug design and development according to genetic structures of persons.

Many foreign companies outsource jobs and there are plenty of opportunities for research and employment, experts note.

Professor Murthy adds with a caution: “The quality of training is very important. There are several educational institutions offering the course and the quality of training varies from one institute to another. There is a very large number of extremely poor graduates. Well-trained persons will find good opportunities in bioinformatics.”

In a bid to emphasise quality, the Department of Biotechnology, Government of India, is in the process of standardising the teaching of bioinformatics. It is conducting a national-level examination — Bioinformatics National Certification examination, testing the degree of theoretical and practical knowledge of students. Those who emerge successful in the exam would receive fellowship and opportunities to conduct research in the country, says Professor Mathur.

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Here is a list of companies working in Chemoinformatics and Drug Discovery.

• Accelrys
• ACD/Labs (Advanced Chemistry Development, Inc.)
• Afferent Systems Inc.
• Agilent Technologies
• Aureus Pharma
• Barnard Chemical Information Ltd. (John Barnard and Geoff Downs) SEE: digital chemistry
• Bio-Rad Laboratories
• BioReason
• BioSolveIT
• BlueObelisk.org
• Bruker Daltonics Inc.
• Cambridge Crystallographic Data Centre
• CambridgeSoft Corporation
• CARB Center for Advanced Research in Biotechnology (NIST/University of Maryland)
• Cengent Therapeutics (Genes to Leads). Acquired by Inncardio, Inc.
• Center for Molecular Modeling (NIH)
• Centre for Molecular and Biomolecular Informatics CMBI (formerly: CAOS/CAMM: Dutch National Center for Computer-Assisted Chemistry and Molecular Modelling)
• Cerep
• ChemAxon
• ChemDiv, Inc. (Chemical Diversity)
• Chemical Abstracts Service
• Chemical Computing Group, Inc.
• Chemical Simulations Group
• ChemInnovation Software
• ChemNavigator
• ChemSW (Chemistry Software for Windows, formerly: WindowChem)
• Chenomx Inc.
• CIS Chemical Information System
• CombiChem.net
• CompuChem
• CrossFire Beilstein
• CSA Cambridge Scientific Abstracts
• Daylight Chemical Information Systems
• Derwent (Thomson)
• Dialog (Thomson)
• digital chemistry (formerly BCI, Barnard Chemical Information)
• Drug Design Methodologies
• eduSoft (Glen E. Kellogg, Donald J. Abraham) (hint!, molconn-z, HASL)
• Eidogen-Sertanty
• Elsevier MDL
• European Bioinformatics Institute (EMBL)
• FamilyGenetix (formerly, Cherwell Scientific) (Cyrillic, ModelMaker, Modelmanager)
• FIZ Chemie Berlin (Fachinformationszentrum Chemie GmbH)
• Fujitsu (CAChe)
• Gaussian
• GCC GenChemiCs (William J. (Bill) Welsh)
• gNova Scientific Software
• GridChem
• Hypercube, Inc.
• Iconix Pharmaceuticals
• IDBS
• Incyte
• InforSense (ChemSense, InforSense KDE)
• Inpharmatica Ltd. (Biopendium, Chematica)
• Invitrogen (Vector NTI)
• IOS Improved Outcomes Software (GeneLinker)
• ISI Web of Knowledge (Thomson Scientific)
• Jubilant Biosys
• Knovel
• LabVantage
• LabWare
• LeadScope, Inc.
• Lhasa Limited
• LION bioscience
• Matrix Science
• MEDIT – Molecular Extended Distribution in Information Technology
• Metaphorics, LLC
• Modgraph Consultants, Ltd.
• Molecular Connections (Netpro)
• Molecular Discovery
• Molecular Networks
• Molecular Solutions (Recruiting Company for computational chemistry and informatics)
• Molegro
• Molinspiration
• MolMine Bioinformatics Software Solutions
• Molsearch
• MolSoft (ICM)
• NCBI US National Center for Biotechnology Information
• New River Kinematics (MoluCAD)
• NLM US National Library of Medicine
• OpenEye Scientific Software
• Ovid
• Pharma Algorithms
• Princeton Simulations (Conformer)
• Proteome Systems
• Q-Chem, Inc.
• Questel/Orbit, Inc.
• Rosetta Biosoftware
• Schrödinger
• SciTegic
• Semantic Laboratories
• SemiChem (AMPAC, Codessa)
• Serena Software, Inc. (Pcmodel)
• STN
• Sunset Molecular Discovery, LLC
• Symyx Technologies
• TDS Technical Database Services, Inc. (DIPPR/EHS, COSMOtherm, LogKOW)
• TimTec
• Tripos
• Waters (Empower, NuGenesis, MassLynx)
• Wavefunction (Spartan)
• Wiley-VCH Publishers
• WinMOPAC (Fujitsu)

Also check out these:

• Cheminformatics Companies on wiki
• Network Science, NetSci’s Biotech & Pharmaceutical YellowPages
• LIMSource
• Scientific Computing’s LIMS Guide
• Chembiogrid

The area of drug discovery tools is one of the newest and most important sectors of pharmaceutical research and development. The term drug discovery tools usually refers to high-content screening (HCS) and analysis and is composed of those applications that require sufficient levels of sample throughput, whereby complex cellular events and phenotypes can be studied. Elements of drug performance like toxicity and specificity can be established simultaneously using mixed cell types-primary cells, cell lines, cell subpopulations. HCS seeks to assess the impact of phenotypic and cellular changes that are brought about by gene modification (such as with RNA interference (RNAi) approaches) and/or drug (or compound) treatment. The purpose of this examination by TriMark Publications is to describe the specific segments of the global drug discovery tools market. Within this area, the report covers those segments that are highly active in terms of innovation and growth. Specifically, this study examines the markets for small lab equipment all the way up to highly automated, large automated platforms, as well as accessory equipment such as reagents, supplies and manufacturers’ original equipment manufacturer (OEM) additional equipment.
 
 
Table of Contents : 
 1. Overview 4
 1.1 Objectives of the Report 4
 1.2 Methodology 5
 1.3 Scope of the Report 6
 1.4 Executive Summary 7

 2. Technologies and Product Offering for High-Content Analysis 10
 2.1 Definition of High-Content Analysis and Why it is so Attractive a Discipline 11
 2.2 Classes of Measurements Possible with High-Content Analysis Approaches and Biologies Interrogated 14
 2.3 Instrumentation Platforms for High-Content Analysis 17
 2.3.1 High-Content Screening Technology 17
 2.4 Reagent and Assay Platforms for High-Content Analysis 24
 2.5 Cell-based Screening Technologies in Drug Development 28
 2.5.1 Applications of Cell-based Assays 28
 2.5.2 Pharma Drug Discovery Paradigm and Compound Screening 28
 2.5.3 High-Content Analysis in the Biopharmaceutical Industry 29

 3. Market Analysis of the High-Content Tools Space 31
 3.1 High-Content Analysis Market Size and Growth 31
 3.2 Market Survey to Assess Qualitative and Quantitative Parameters of the High-Content Analysis Space 31
 3.3 Experimental and Research Trends in High-Content Analysis 33
 3.4 Challenges and Market Drivers in High-Content Analysis 38
 3.4.1 Barriers to High-Content Analysis 40
 3.4.2 Drivers of High-Content Analysis 41
 3.5 High-Content Analysis in Combination with RNAi 41
 3.6 Market Landscape of Instrumentation for High-Content Analysis 43
 3.7 Reagents and Assays Usage in High-Content Analysis 47
 3.8 Trends in High-Content Analysis Assays/Reagents Space-Major Product Vendors 50
 3.9 Emerging Market Trends in High-Content Analysis 52
 3.10 Market Forecasts for the High-Content Analysis Space 54
 3.11 Use of HCS in Pharmaceutical Companies 56
 3.12 Qualitative Opportunities and Challenges for Market Adoption 57

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Understanding Biotechnology – BSN

Genetically Modified Organisms (GMOs) – Arti S. Pandey

Food Biotechnology in Nepal – Dr. Tika Bahadur Karki

Status of Biotechnology in Livestock Sector, Nepal – Dr. Suderson Prasad Gautam

Drug Discovery and Generic Drugs – Dr. Pramod Aryal

Status, trend and application of plant biotechnology in Nepal – Dr. Hari P. Bimb

Strengthening Farmers’ Participation in Agricultural Biotechnology in Nepal – Tara Lama (LI-BIRD)

Intellectual Property Rights, Technology Transfer and Farmer’s rights – Kamalesh Adhikari (SAWTEE)

One of the readers posted this question to me, and I thought I’ll share my perspective as someone who is closely involved with the earliest phase of medical research and drug discovery

It is due to the very expensive process of drug/treatment discovery.

Over the last few thousands of years, humans have been doing science by observing our surroundings and our own bodies using our 5 senses — our eyes, noise, ears, mouth, skin to touch. Over this long period of time, we have discovered almost everything that can be discovered using this 5 senses.

In the last century, because of electricity and other fundamental inventions, we have been able to invent machines that have in turn allowed us to make greater quantum leaps in science. Machines like the telescope, the microscope, the x-ray machine etc.

Today, drug/treatment discovery in the field of medicine is made primarily with through the use of all these sophisticated equipment. These equipment costs a bomb — many of them the price of a HDB flat easily. So you can imagine to cost of research for one single drug.

Because these discoveries and inventions are not easy to make (there are no model answers at the back of life’s books) , the success rate (drugs making it from the start of the reseach process, to passing the safety tests etc and going on sale) of drug inventions is 1 in 10,000.

So when companies sell these drugs, they have to cover the cost of the 9,999 failures, by selling that 1 remaining drugs.

Also, there needs to be a sufficient profit margin so that investors would be willing to take on such huge risks (1 in 10,000 and no one knows when this 1 will pop out) in investing in the drug research process.

I’m sure many of you have heard about generic or low cost medicines. Such medicines are cheaper usually because the company’s patent has expired, or is willing to allow generic versions to be made. In this case, the cost of the drug is used to cover the cost of the manufacture —- it usually doesn’t cover the cost of the 9,999 failures, or the risk that the investors need to take.

So why don’t we go the way of producing low cost generic drugs from the start, for everything? Why do we use intellectual property and anti-piracy laws to protect rich pharmaceuticals? Because without this financial incentive, many investors would no want to take on such huge risks of failed drugs — they’ll channel their money into more profitable investments. Without this financial investment, research will slow and consequentially medical advancement will slow. While some argue that the State should take on these investments, others argue that it is irresponsible to use taxpayer money for such high risk ventures, and this should be left up to high-risk venture capitalists.

At the end of the day, all these makes new drugs/treatments very expensive, and the aged need more repairing than the young , and so we need to decide who and how to pay for them.

Bioinformatics provides the computational support for functional genomics which will link the behavior of cells, organism amd population to the information encoded in the genomes, as well as structural genomics. The utility of bioinformatics lies in the identification of useful genes leading to the development of new gene products. The subject covers topics such as protein modeling and sequence alignment, expression data analysis, and comparartive genomics. It combines algorithmic, statistical and database methods for studying biological problems also.

The greatest achievement of bioinformatics methods, the Human Genome Project. Because of this the nature and priorities of bioinformatics research and applications are changing. Many experts believe that this will affect bioinformatics in several ways. For instance some scientists also believe what some people refer to as research or medical informatics, the management of all biomedical experimental data associated with particular molecules or patients – from mass spectroscopy, to in vitro assays to clinical side-effects-move from the concern of those working in drug company and hospital IT (information technology) into the mainstream of cell and molecular biology and migrate from the commercial and clinical to academic sectors.

Drug Development

Only 10% of drug molecules identified in research make it through development. This means that many potential drugs do not make it to market, and expensive time and resources are invested m molecules that will generate no revenue. Simulation and informatics can significantly increase these odds by improving the efficiency of drug development, cutting costs, and improving margins.

Formulation Design

Formulation is the process of mixing Ingredients in such a way as to produce a new or improved product. The formulation department must balance the different marketing and deliverability requirements with cost and chemical constraints to come up with the best possible drug delivery method at the best price. With laboratory results stored in legacy systems, it takes expert company knowledge and experience to know which methods and suppliers are available, let alone to locate them quickly. In many cases scientists find that it is easier to repeat an experiment than to find previous results. This situation is compounded in global R&D set-ups, and after mergers and acquisitions.

Crystallisation and Structure Determination

Determining the crystal structure of an active compound is one of the first steps in pharmaceutical development. The crystal structure of a drug affects how easy it is to formulate, its bio-avail- ability, and its shelf life. Knowledge of the different possible polymorphs of a crystal can also give better patent protection for a drug.

Polymer Modeling

Drug delivery is a complex task. The drug must be delivered in a way that transports the active component intact to the appropriate part of the body. The way the cell takes up the drug is also very important: drugs that go to parts of the body other than the intended target are wasted and may lead to unwanted side effects.

Many delivery devices are polymeric with the drug either solubilised or emulsified in the polymer. Drug delivery systems have mesoscale structures; between 10 to 1000 nm. The amount of computing power required to model these systems at an atomistic level is prohibitive, and macroscale techniques such as Finite element analysis or computational fluid dynamics do not give the required level of detail. Mesoscale modeling, focusing on the nanometer length scale, is helping scientists to develop colloidal delivery systems for drugs.

The great advances in human healthcare that are presaged by the Human Genome Project can be realized by the pharmaceutical industry. A prerequisite for this will be the successful integration of bioinformatics into most aspects of drug discovery. Although, from a scientific viewpoint, this is not a difficult problem, there are formidable technological obstacles. Once these are overcome, rapid progress can be expected.

BioFocus DPI has partnered with the cancer drug candidate and biomarker company Oncodesign to offer an integrated service platform, Galapagos division BioFocus DPI said today.

The agreement will create a service that combines BioFocus DPI’s target discovery and screening capabilities with Oncodesign’s biomarker and pharmacological services to offer target validation and clinical candidate selection services, the company said.

BioFocus DPI offers gene-to-drug candidate discovery services, including in vitro and cell-based screening, chemogenomic and informatics offerings, storage, distribution, and other structural biology and medicinal chemistry services.

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Global health has been on my mind again recently. An article I wrote for Nature Reviews Drug Discovery (subscription required) examines efforts to find new drugs for tuberculosis.

Scanning electron micrograph of TB bacterium/ image: CDC/Dr. Ray Butler, credit: Janice Haney Carr

TB is a wily organism that finds a way to wall itself off in the body. Under the best circumstances, knocking out “the best” TB, the drug susceptible variety, requires 6 to 9 months of antibiotics. That’s a long time to stay on drugs, particularly in a developing country where the health clinic could be miles away. But if people start treatment without completing it, drug resistant disease can develop. With multi-drug resistant (MDR) or extensively drug resistant (XDR) disease, that prognosis is far more uncertain. The treatments being given in these cases don’t have much scientific validation to show that they work. In some cases of XDR TB, doctors are cutting out portions of diseased lung tissue from patients because there’s not much else that they can do.

My article mostly talks about the drug discovery challenges: how do you find a new drug for an old disease particularly when half a century ago we thought this problem was solved. But there’s another piece of this story that didn’t make it into the article: how do you then do the clinical studies to test these drugs among the people who need these drugs most?

In countries where health care systems are already taxed and where doctors and nurses may not be trained to carry out clinical trial protocols, there’s an additional wrinkle. TB treatments– somewhat uniquely– are written into health care policy in countries around the world. Almost all other diseases  have recommended treatments, but the final decision belongs with the medical professionals who see the patients. What does that mean for TB clinical trials? Many more layers of red tape to set up a clinical trial. Not only do researchers have to work out trial protocols with the hospitals, they also have to get approvals from local and national authorities to modify the existing TB treatment protocols. I was amazed at the amount of coordination that’s involved.

Learn more about the search for new TB treatments at the Global Alliance for TB Drug Development.

Mettre sur le marché un nouveau médicament prend (au mieux) une dizaine d’années et coute près d’un milliard de dollars. Pour un nouveau médicament commercialisé, on estime que 5000 à 10.000 composés ont été testé en amont (source)

Red Pills, originally uploaded by Xavi Calvo.

Le processus de R&D est partagé entre plusieurs types d’acteurs : des industriels, biotech ou pharma, et des chercheurs du monde public qui interviennent principalement au niveau de la recherche fondamentale en décodant les mécanismes biologiques des maladies et proposant des scénarii de traitement possible, sous forme de molécules d’origine chimique ou biologique qui vont interagir avec le mécanisme pathologique.

Ces deux mondes se rencontrent naturellement depuis longtemps et les collaborations sont nombreuses, mais le web peut-il aujourd’hui booster ces collaborations et devenir un outil utile au drug discovery ?

C’est le pari qu’à récemment fait Eli Lilly, laboratoire pharmaceutique nord-américain, qui a mis sur pied la plate-forme PD2 (prononcer “Pi Di Square”) pour Phenotypic Drug Discovery.

Le principe en est simple : permettre à des structures de recherche universitaire ou des entreprises de biotech, de tester les molécules développées en interne dans des batteries de tests validés, d’abord par des techniques de modélisation moléculaire (recherche in silico), puis in vitro, dans des modèles de pathologies humaines, les modèles phénotypiques.

La première phase de test consiste pour l’institution publique ou l’entreprise de biotech à la soumission, via une plate-forme web sécurisée et de manière confidentielle, d’une structure chimique modélisée qui va subir une première batterie de tests in silico afin d’identifier des structures chimiques potentiellement intéressantes pour un développement ultérieur.

La seconde phase, toujours basée sur la plate-forme web, et toujours confidentielle, permettra à l’institution publique ou l’entreprise de biotech de faire tester physiquement ses échantillons dans des modèles phénotypiques in vitro, propriété d’Eli Lilly : Maladie d’Alzheimer, Diabète, Cancer et Ostéoporose et d’obtenir un ensemble de résultats caractérisant l’activité de son composé dans ces différents modèles.

Une fois passées ces deux phases – décrites en détail ici – pourront débuter d’éventuelles négociations entre Lilly et l’institution propriétaire du composé sur les suites à donner au développement : publication des résultats ou partenariat de R&D avec partage de propriété sur la molécule.

A nouveau et comme dans la plupart des cas de transfert de données, ce sont la sécurisation et la garantie de confidentialité du système qui sont les points critiques de la solution développée par Lilly, mais c’est en tout cas une initiative à suivre pour vérifier que, comme cela devrait être le cas à mon sens, le web est et sera de plus en plus un outil incontournable en recherche biomédicale.

The Future of Biobanks: Regulation, ethics, investment and the humanization of drug discovery

Report Overview

Biobanks collect and store human biospecimens, playing a vital role in the development of novel drugs and diagnostics. In recent years, large population-based biobanks have been established which monitor the health status of participants over time to assess the natural occurrence and progression of common diseases. Hundreds of disease-based biobanks are located around the world, which are a valuable resource for biomarker discovery as well as for studying progression, mortality and responses to treatment.

‘The Future of Biobanks’ is a report published examines major trends in the biobanking industry and identifies the key initiatives to upgrade the biobanking infrastructure. This report explores the applications of biobanks and provides examples of how large sets of high-quality biosamples are creating new ways to diagnose, prevent and treat diseases. The activities of over 180 US biobanks are assessed to illustrate how the scale and scope of biobanking is changing. This report also evaluates the limitations of biobanks, in addition to reviewing the unique legal, regulatory and ethical issues that surround this new frontier of biomolecular research.

Key Findings

• A number of new, high-quality biobanks will accelerate the development of personalized diagnostics and therapeutics over the next decade.
• Increased co-operation between biobanks can advance this progression, although many scientific and political barriers must be overcome.
• An estimated $1bn has been invested in the biobanking industry within the last ten years. The first advances are expected to result in the improved treatment of cancer, and progression for a range of other common diseases will follow.
• Recently established biobanks have reached unprecedented levels of scale, particularly the Taizhou project in China. Studies conducted on the entire population of a country may soon be possible.
• At least 179 biobanks with 345,000 donors exist in the US, most of which were established in the last 10 years.
• As biotechnology companies’ biobanking assets mature in terms of the number of samples collected their R&D productivity and the number of products available for outsourcing will increase

Use this report to…

• Assess the strengths and limitations of biobanks and understand their scientific and commercial relevance with this report’s analysis of biobank-enabled targeted therapeutics and diagnostic/prognostic tests.
• Analyze growth in disease-based biobanks by using this report’s unique review of 180 US-based biobanks by disease type and gain insights into the collaborative networks that can leverage greater sample sizes.
• Evaluate major founder/national population biobanks and gain insights into private sector biobanking with this report’s examination of contract service providers, biomarker discovery companies, pharma collections and research collaborations.
• Review developments in the regulatory framework for biobanking and understand ethical issues including informed consent, withdrawal and ownership, confidentiality and commercialization.

Explore issues including…

• Biobanking expansion. The falling cost of genomic technologies is expanding the scale and scope of biobanking research. This has already resulted in the development of several new diagnostic tests that can improve risk assessment and treatment decisions.
• Potential for personalised medicine. Very few personalized medicines exist, but the creation of large sets of high-quality biosamples should widen the clinical application of new personalized products and services.
• Legal and ethical issues. Although the legal and ethical frameworks that govern biobanking are still evolving, many major issues such as ownership and commercialization are now largely addressed during the consent process.
• Cost of national biobanks. National biobanks have been established in several countries, although their value has been questioned. Conducting large-scale population studies requires significant investment, due to the extremely large collections of biosamples and data involved (including relevant medical history, lifestyle and environmental information).

Discover…

• Why are biobanks a critical resource?
• Which types of biobank are most common?
• What are the most common disadvantages of biobanks?
• Which major drug developers have invested in biobanks?
• What are the major uncertainties surrounding the future of biobanks?
• What growing trend could boost biobanking?
• When will the benefits of biobanking be realized?

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A number of factors have impelled sustained text analytics market growth. The technology; text mining and related visualization and analytical software deliver unmatched capabilities in domains such as intelligence and life science. Text analysis is seen as a subspecies of business intelligence, and capabilities will be an essential component of the eventual creation of the Semantic web. By automating the reading process, text analytics allows analysts and researchers to tap material that had not previously been systematically mined in at least a semi automated manner. It allows to work far faster than before and to analyze far greater volumes of information than ever before. Importantly, text analytics can make a huge difference in text analysis and processing costs and enable the creation of new information products and services.

Applications of text mining in the life sciences includes pharmaceutical lead generation – mining scientific literature to accelerate expensive, time consuming drug-discovery processes, the huge and growing volume of on-line content, advances in search and information retrieval, cheap computing power, and better software have created a market for application of these same text technologies to a much broader variety of business, scientific, and research problems.

In spite of the progress in text mining, there is still a lot of scope for improvement. Not all lingual nuances are picked up; also, incorrect usage due to human error may lead to altered/incorrect results. At the present moment in time, maybe a backup plan; like quick manual checks of the processed materials may be the way. Manual checks are necessary especially in life sciences where synonyms are a norm and replacing one name with another, incorrect one is a high possibility.

One such example of this type application is XTractor Premium –a platform for discovery, analysis and modelling of published biomedical facts. The application also comes with -XTractor Premium Knowledgebase – the only knowledgebase, which provides “manually” annotated facts from PubMed on a daily basis.

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XTractor knowledgebase touches 200000 facts within just 1 year of its launch

XTractor Premium published in Nature Methods

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The XTractor Premium Knowledgebase now has more than 200000 facts updated in just 1 year of its launch providing information on Disease mechanisms, Biomarkers, Drug effects and interactions, Clinical trials, Prognosis, Pathways, Knockouts, Mutations, RNAi studies, and much more…

http://www.xtractor.in/premium

For a 1-month FREE Trial access: http://www.xtractor.in/trial.do

XTractor is one of its kind biomedical literature knowledgebase which updates refined facts from PubMed every single day.

The XTractor Premium Knowledgebase now has more than 200000 facts updated in just 1 year of its launch providing information on Disease mechanisms, Biomarkers, Drug effects and interactions, Clinical trials, Prognosis, Pathways, Knockouts, Mutations, RNAi studies, and much more…

The product is currently being widely used by more than 2000 researchers across the globe.

http://www.xtractor.in/premium

For a 1-month FREE Trial access: http://www.xtractor.in/trial.do

For trial access contact: http://www.xtractor.in/trial.do

A number of factors have impelled sustained text analytics market growth. The technology; text mining and related visualization and analytical software deliver unmatched capabilities in domains such as intelligence and life science. Text analysis is seen as a subspecies of business intelligence, and capabilities will be an essential component of the eventual creation of the Semantic web. By automating the reading process, text analytics allows analysts and researchers to tap material that had not previously been systematically mined in at least a semi automated manner. It allows to work far faster than before and to analyze far greater volumes of information than ever before. Importantly, text analytics can make a huge difference in text analysis and processing costs and enable the creation of new information products and services.

Applications of text mining in the life sciences includes pharmaceutical lead generation – mining scientific literature to accelerate expensive, time consuming drug-discovery processes, the huge and growing volume of on-line content, advances in search and information retrieval, cheap computing power, and better software have created a market for application of these same text technologies to a much broader variety of business, scientific, and research problems.

In spite of the progress in text mining, there is still a lot of scope for improvement. Not all lingual nuances are picked up; also, incorrect usage due to human error may lead to altered/incorrect results. At the present moment in time, maybe a backup plan; like quick manual checks of the processed materials may be the way. Manual checks are necessary especially in life sciences where synonyms are a norm and replacing one name with another, incorrect one is a high possibility. One such example of this type application is XTractor Premium –a platform for discovery, analysis and modelling of published biomedical facts. The application also comes with -XTractor Premium Knowledgebase – the only knowledgebase, which provides “manually” annotated facts from PubMed on a daily basis.

http://www.xtractor.in/premium

For a 1-month FREE Trial access: http://www.xtractor.in/trial.do

This week we got our first application note on XTractor Premium published in Nature Methods– a science methodology journal publishing laboratory techniques and methods papers in the life sciences and areas of chemistry.

With the growing number of manually annotated biomedical relationships updated from latest PUBMED publications, XTractor Premium now makes its appearance in Nature Methods – a science methodology journal publishing laboratory techniques and methods papers in the life sciences and areas of chemistry.

The article can be accessed at: http://www.nature.com/app_notes/nmeth/2009/090906/full/an7147.html#a1

Title: “XTractor Premium – a Knowledgebase of manually annotated biomedical relationships updated everyday from PubMed abstracts”

Author: Guru Rao
/Molecular Connections Pvt. Ltd. Bangalore IN/

The article initially addresses the main drawbacks of data mining – the organization of the quantum of published data in PUBMED, time and cost consumed in analysing these data. It brings out the utilities of the XTractor Premium, which can accurately process this vast repertoire of scientific information with a quick turn around time. It also features the advanced analytical tools that come in handy with the XTractor Premium Knowledgebase such as Semantic Search, Concept linking, Bibliographic Search and comprehensive downloadable reports for faster analysis of the biomedical data.

The case study “Tracking common gene polymorphisms across multiple Diseases using XTractor” presented in the article provides the ability of XTractor Premium to help us discover newer facts in published literature within a quick 15-20 mins analysis time.

http://www.xtractor.in/premium

For a 1-month FREE Trial access: http://www.xtractor.in/trial.do