Curator: Aviva Lev-Ari, PhD, RN
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HAPAA: a tool for ancestral haploblock reconstruction. Specifically, given the genotype (for instance, as derived by an Illumina genotyping array) of an individual of admixed ancestry, find the source population for each segment of the individual’s genome.
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Protein Interaction Networks
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A tool for aligning multiple global protein interaction networks; Graemlin also supports search for homology between a query module of proteins and a database of interaction networks.
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CONTRA: Conditionally trained models for sequence analysis. SeeCONTRAlign, a protein sequence aligner with very high accuracy, especially in twilight alignments. See CONTRAfold, an RNA secondary structure prediction tool. Stay tuned for more…
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CONTRAfold: Prediction of RNA secondary structure with a Conditional Log-Linear model that relies on automatically trained parameters, rather than on a physics-based energy model of RNA folding.
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Protein Alignment
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CONTRAlign: A protein sequence aligner that users can optionally train on feature sets such as secondary structure and solvent accessibility; see the CONTRA project above.
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A protein multiple sequence aligner that exhibits high accuracy on popular benchmarks.
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A protein multiple aligner that automatically finds domain structures of sequences with shuffled and repeated domain architectures.
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Motif Finding
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MotifCut: a non-parametric graph-based motif finding algorithm.
MotifScan: a non-parametric method for representing motifs and scanning DNA sequences for known motifs.
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CompareProspector: motif finding with Gibbs sampling & alignment.
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Genomic Alignment
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Stanford ENCODE: Multiple Alignments of 1% of the Human genome.
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Typhon: BLAST-like sequence search to a multiple alignments database.
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LAGAN: tools for genomic alignment. These include the MLAGAN multiple alignment tool, and Shuffle-LAGAN for alignment with rearrangements.
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Microarray Analysis
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Application of Independent Component Analysis (ICA) to microarrays.
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Researchers Hope New Database Becomes Universal Cancer Genomics Tool
Swiss scientists hope that a new online database called “arrayMap” will bring cancer genomics to the desktop, laptop, and tablet computers of pathologists and researchers everywhere.
The database combines genomic information from three sources: large repositories such as the NCBI Gene Expression Omnibus (GEO) and Cancer Genome Atlas (CGA); journal literature; and submissions from individual investigators. It incorporates more than 42,000 genomic copy number arrays—normal and abnormal DNA comparisons—from 195 cancer types.
“arrayMap includes a wider range of human cancer copy number samples than any single repository,” said principal investigator Michael Baudis, M.D. Ease of access, visualization, and data manipulation, he added, are top priorities in its ongoing development.
A product of the University of Zurich Institute for Molecular Life Sciences, where Baudis researches bioinformatics and oncogenomics, arrayMap illustrates the importance of copy number abnormalities (CNA)—dysfunctional DNA gains or losses that visibly lengthen or shorten certain chromosomes—in the diagnosis, staging, and treatment of various malignancies.
“I have this particular tumor type—are there any CNAs in it that can tell me anything about prognosis or treatment?” said Michael Rossi, Ph.D., director of the Winship Cancer Institute cancer genomics program at the Emory University School of Medicine in Atlanta. “Data mining tools like arrayMap are incredibly useful to help answer such questions.”
arrayMap – genomic arrays for copy number profiling in human cancer
arrayMap is a curated reference database and bioinformatics resource targeting copy number profiling data in human cancer. The arrayMap database provides an entry point for meta-analysis and systems level data integration of high-resolution oncogenomic CNA data. The current data reflects:
42875 genomic copy number arrays
634 experimental series
256 array platforms
197 ICD-O cancer entities
480 publications (Pubmed entries)
For the majority of the samples, probe level visualization as well as customized data representation facilitate gene level and genome wide data review. Results from multi-case selections can be connected to downstream data analysis and visualization tools, as we provide through our Progenetix project.
arrayMap is developed by the group “Theoretical Cytogenetics and Oncogenomics” at the Institute of Molecular Life Sciences of the University of Zurich.
MIT: A New Approach Uses Compression to Speed Up Genome Analysis
Public-Domain Computing Resources
Structural Bioinformatics
The BetaWrap program detects the right-handed parallel beta-helix super-secondary structural motif in primary amino acid sequences by using beta-strand interactions learned from non-beta-helix structures.
Wrap-and-pack detects beta-trefoils in protein sequences by using both pairwise beta-strand interactions and 3-D energetic packing information
The BetaWrapPro program predicts right-handed beta-helices and beta-trefoils by using both sequence profiles and pairwise beta-strand interactions, and returns coordinates for the structure.
The MSARi program indentifies conserved RNA secondary structure in non-coding RNA genes and mRNAs by searching multiple sequence alignments of a large set of candidate catalogs for correlated arrangements of reverse-complementary regions
The Paircoil2 program predicts coiled-coil domains in protein sequences by using pairwise residue correlations obtained from a coiled-coil database. The original Paircoil program is still available for use.
The Trilogy program discovers novel sequence-structure patterns in proteins by exhaustively searching through three-residue motifs using both sequence and structure information.
The ChainTweak program efficiently samples from the neighborhood of a given base configuration by iteratively modifying a conformation using a dihedral angle representation.
The TreePack program uses a tree-decomposition based algorithm to solve the side-chain packing problem more efficiently. This algorithm is more efficient than SCWRL 3.0 while maintaining the same level of accuracy.
PartiFold: Ensemble prediction of transmembrane protein structures. Using statistical mechanics principles, partiFold computes residue contact probabilities and sample super-secondary structures from sequence only.
tFolder: Prediction of beta sheet folding pathways. Predict a coarse grained representation of the folding pathway of beta sheet proteins in a couple of minutes.
RNAmutants: Algorithms for exploring the RNA mutational landscape.Predict the effect of mutations on structures and reciprocally the influence of structures on mutations. A tool for molecular evolution studies and RNA design.
AmyloidMutants is a statistical mechanics approach for de novo prediction and analysis of wild-type and mutant amyloid structures. Based on the premise of protein mutational landscapes, AmyloidMutants energetically quantifies the effects of sequence mutation on fibril conformation and stability.Genomics
GLASS aligns large orthologous genomic regions using an iterative global alignment system. Rosetta identifies genes based on conservation of exonic features in sequences aligned by GLASS.
MinoTar – Predict microRNA Targets in Coding Sequence.Systems Biology
The Struct2Net program predicts protein-protein interactions (PPI) by integrating structure-based information with other functional annotations, e.g. GO, co-expression and co-localization etc. The structure-based protein interaction prediction is conducted using a protein threading server RAPTOR plus logistic regression.
IsoRank is an algorithm for global alignment of multiple protein-protein interaction (PPI) networks. The intuition is that a protein in one PPI network is a good match for a protein in another network if the former’s neighbors are good matches for the latter’s neighbors.Other
t-sample is an online algorithm for time-series experiments that allows an experimenter to determine which biological samples should be hybridized to arrays to recover expression profiles within a given error bound.http://people.csail.mit.edu/bab/computing_new.html#systems
Compressive genomics
STANFORD UNIVERSITY: Resources
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BMIR is committed to the development of research tools as part of its goal to provide reusable, computational building blocks to facilitate the development of a vast array of systems. Some of these resources are described below.
Resources
- The National Center for Biomedical Ontology (NCBO)
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The National Center for Biomedical Ontology is a consortium of leading biologists, clinicians, informaticians, and ontologists who develop innovative technology and methods that allow scientists to create, disseminate, and manage biomedical information and knowledge in machine-processable form.
- Protégé
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Protégé is a free, open-source platform that provides its community of more than 80,000 users with a suite of tools to construct domain models and knowledge-based applications with ontologies.
- PharmGKB
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PharmGKB curates information that establishes knowledge about the relationships among drugs, diseases and genes, including their variations and gene products. Our mission is to catalyze pharmacogenomics research.
- Simbios
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About Simbios
Simbios, the National NIH Center for Physics-based Simulation of Biological Structures is devoted to helping biomedical researchers understand biological form and function. It provides infrastructure, software, and training to assist users as they create novel drugs, synthetic tissues, medical devices, and surgical interventions.
Simbios scientists investigate structure-function studies on a wide scale of biology – from molecules to organisms, and are currently focusing on challenging biological problems in RNA folding, myosin dynamics, neuromuscular biomechanics and cardiovascular dynamics.
Stanford BioMedical Informatics Research (BMIR) – Publications by Project
There are 8 publications for the project “Genomic Nosology for Medicine (GNOMED)”.
- BMIR-2009-1362
- Identifying compartment-specific non-HLA targets after renal transplantation by integrating transcriptome and ‘‘antibodyome’’ measures
- L. Li, P. Wadia, M. Sarwal, N. Kambham, T. Sigdel, D. B. Miklos, R. Chen, M. Naesens, A. J. Butte
- PNAS, 106, 11, 4148-4153. Published in 2009
- BMIR-2008-1338
- Using SNOMED-CT For Translational Genomics Data Integration
- J. Dudley, D. P. Chen, A. J. Butte
- Ronald Cornet, Kent Spackman (eds.): Representing and sharing knowledge using SNOMED. Proceedings of the 3rd International Conference on Knowledge Rep, Pheonix (AZ), USA, CEUR Workshop Proceedings, ISSN 1613-0073, online CEUR-WS.org/Vol-410/, 91-96. Published in 2008
- BMIR-2008-1303
- The Ultimate Model Organism
- A. J. Butte
- Science, 320, 5874, 325-327. Published in 2008
- BMIR-2008-1293
- Novel Integration of Hopsital Electronic Medical Records and Gene Expression Measurements to Identify Genetic Markers of Maturation
- D. P. Chen, S. C. Weber, P. S. Constantinou, T. A. Ferris, H. J. Lowe, A. J. Butte
- Pacific Symposium on Biocomputing, Big Island, Hawaii, 13, 243-254. Published in 2008
- BMIR-2008-1292
- Enabling Integrative Genomic Analysis of High-Impact Human Diseases through Text Mining
- J. Dudley, A. J. Butte
- Pacific Symposium on Biocomputing, Big Island, Hawaii, 13, 580-591. Published in 2008
- BMIR-2007-1297
- Methodologies for Extracting Functional Pharmacogenomic Experiments from International Repository
- Y. Lin, A. P. Chiang, P. Yao, R. Chen, A. J. Butte, R. S. Lin
- AMIA Annual Symposium, Chicago, IL, 463-467. Published in 2007
- BMIR-2007-1296
- Clinical Arrays of Laboratory Measures, or “Clinarrays”, Built from an Electronic Health Record Enable Disease Subtyping by Severity
- D. P. Chen, S. C. Weber, P. S. Constantinou, T. A. Ferris, H. J. Lowe, A. J. Butte
- AMIA Annual Symposium, Chicago, IL, 115-119. Published in 2007
- BMIR-2006-1232
- Finding Disease-Related Genomic Experiments Within an International Repository: First Steps in Translational Bioinformatics
- A. J. Butte, R. Chen
- Annual Symposium of the American Medical Informatics Association, Washington, D.C., 106-10. Published in 2006
- http://bmir.stanford.edu/publications/project.php/genomic_nosology_for_medicine_gnomed
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Featured Publications
- BMIR-2011-1468
- The National Center for Biomedical Ontology
- M. A. Musen, N. F. Noy, C. G. Chute, M. A. Storey, B. Smith, N. H. Shah
- . Published in 2011
- BMIR-2009-1378
- Prototyping a Biomedical Ontology Recommender Service
- C. Jonquet, N. H. Shah, M. A. Musen
- Bio-Ontologies: Knowledge in Biology, SIG, ISMB ECCB 2009, Stockholm, Sweden. Published in 2009
- BMIR-2009-1376
- Translational bioinformatics applications in genome medicine
- A. J. Butte
- Genome Medicine, 1, 6, 64. Published in 2009
- BMIR-2009-1362
- Identifying compartment-specific non-HLA targets after renal transplantation by integrating transcriptome and ‘‘antibodyome’’ measures
- L. Li, P. Wadia, M. Sarwal, N. Kambham, T. Sigdel, D. B. Miklos, R. Chen, M. Naesens, A. J. Butte
- PNAS, 106, 11, 4148-4153. Published in 2009
- BMIR-2009-1361
- Technology for Building Intelligent Systems: From Psychology to Engineering
- M. A. Musen
- Modeling Complex Systems, Bill Shuart, Will Spaulding and Jeffrey Poland, U Nebraska P, Lincoln, Nebraska, Vol 52 of the Nebraska Symposium on Motivation, 145-184. Published in 2009
- BMIR-2009-1358
- Software-Engineering Challenges of Building and Deploying Reusable Problem Solvers
- M. J. O’Connor, C. I. Nyulas, A. Okhmatovskaia, D. Buckeridge, S. W. Tu, M. A. Musen
- Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 24, 3. Published in 2009
- BMIR-2009-1355
- Data-Driven Methods to Discover Molecular Determinants of Serious Adverse Drug Events
- A. P. Chiang, A. J. Butte
- Clinical Pharmacology and Therapeutics, 28 January 2009, Advance online publication, doi:10.1038/clpt.2008.274. Published in 2009
- BMIR-2009-1318
- Knowledge-Data Integration for Temporal Reasoning in a Clinical Trial System
- M. J. O’Connor, R. D. Shankar, D. B. Parrish, A. K. Das
- International Journal of Medical Informatics, 78, Suppl. 1, S77-S85. Published in 2009
- BMIR-2008-1353
- GeneChaser: Identifying all biological and clinical conditions in which genes of interest are differentially expressed
- R. Chen, R. Mallelwar, A. Thosar, S. Venkatasubrahmanyam, A. J. Butte
- BMC Bioinformatics, 9, 1, 548. (doi:10.1186/1471-2105-9-548). Published in 2008
- BMIR-2008-1346
- FitSNPs: highly differentially expressed genes are more likely to have variants associated with disease
- R. Chen, A. A. Morgan, J. Dudley, A. M. Deshpande, L. Li, K. Kodama, A. P. Chiang, A. J. Butte
- Genome Biology, 9, 12, R170 (doi:10.1186/gb-2008-9-12-r170). Published in 2008
- BMIR-2008-1341
- Translational Bioinformatics: Coming of Age
- A. J. Butte
- Journal of the American Medical Informatics Association, JAMIA, 15, 6, 709-14. Published in 2008
- BMIR-2008-1329
- An Ontology-Driven Framework for Deploying JADE Agent Systems
- C. I. Nyulas, M. J. O’Connor, S. W. Tu, A. Okhmatovskaia, D. Buckeridge, M. A. Musen
- IEEE/WIC/ACM International Conference on Intelligent Agent Technology, Sydney, Australia, 2, 573-577. Published in 2008
- BMIR-2008-1322
- Understanding Detection Performance in Public Health Surveillance: Modeling Aberrancy-Detection Algorithms
- D. Buckeridge, A. Okhmatovskaia, S. W. Tu, C. I. Nyulas, M. J. O’Connor, M. A. Musen
- Journal of the American Medical Informatics Association, 15, 6, 760-769. Published in 2008
- BMIR-2008-1319
- Network Analysis of Intrinsic Functional Brain Connectivity in Alzheimer’s Disease
- K. S. Supekar, V. Menon, M. A. Musen, D. L. Rubin, M. Greicius
- Public Library of Science-Computational Biology., PLoS Computational Biology, June 2008. Published in 2008
- BMIR-2008-1315
- Medical Imaging on the Semantic Web: Annotation and Image Markup
- D. L. Rubin, P. Mongkolwat, V. Kleper, K. S. Supekar, D. S. Channin
- AAAI Spring Symposium Series, Semantic Scientific Knowledge Integration, Stanford. Published in 2008
- BMIR-2008-1303
- The Ultimate Model Organism
- A. J. Butte
- Science, 320, 5874, 325-327. Published in 2008
- BMIR-2008-1298
- BioPortal: A Web Portal to Biomedical Ontologies
- D. L. Rubin, D. de Abreu Moreira, P. P. Kanjamala, M. A. Musen
- AAAI Spring Symposium Series, Symbiotic Relationships between Semantic Web and Knowledge Engineering, Stanford University, (in press). Published in 2008
- BMIR-2007-1295
- AILUN: reannotating gene expression data automatically
- R. Chen, L. Li, A. J. Butte
- Nature Methods, 4, 11, 879. Published in 2007
- BMIR-2007-1281
- Evaluation and Integration of 49 Genome-wide Experiments and the Prediction of Previously Unknown Obesity-related Genes
- S. B. English, A. J. Butte
- Bioinformatics, Epub. Published in 2007
- BMIR-2007-1261
- Protege: A Tool for Managing and Using Terminology in Radiology Applications
- D. L. Rubin, N. F. Noy, M. A. Musen
- Journal of Digital Imaging, J Digit Imaging. Published in 2007
- BMIR-2007-1244
- Efficiently Querying Relational Databases using OWL and SWRL
- M. J. O’Connor, R. D. Shankar, S. W. Tu, C. I. Nyulas, A. K. Das, M. A. Musen
- The First International Conference on Web Reasoning and Rule Systems, Innsbruck, Austria, Springer, LNCS 4524, 361-363. Published in 2007
- BMIR-2006-1090
- Creation and implications of a phenome-genome network
- A. J. Butte, I. S. Kohane
- Nature Biotechnology, 24, 1, 55 – 62. Published in 2006
- http://bmir.stanford.edu/publications/
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NATIONAL CENTERS FOR BIOMEDICAL COMPUTING
National Center for Simulation of Biological Structures (SimBioS) at Stanford University
National Center for the Multiscale Analysis of Genomic and Cellular Networks (MAGNet) at Columbia University
National Alliance for Medical Image Computing (NA-MIC) at Brigham and Women’s Hospital, Boston, MA
Integrating Biology and the Bedside (I2B2) at Brigham and Women’s Hospital, Boston, MA
National Center for Biomedical Ontology (NCBO) at Stanford University
Integrate Data for Analysis, Anonymization, and Sharing (IDASH) at the University of California, San Diego
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[…] http://pharmaceuticalintelligence.com/2012/09/18/genome-and-genetics-resources/ […]
Really informative and a good reference
PUT IT IN CONTEXT OF CANCER CELL MOVEMENT
The contraction of skeletal muscle is triggered by nerve impulses, which stimulate the release of Ca2+ from the sarcoplasmic reticuluma specialized network of internal membranes, similar to the endoplasmic reticulum, that stores high concentrations of Ca2+ ions. The release of Ca2+ from the sarcoplasmic reticulum increases the concentration of Ca2+ in the cytosol from approximately 10-7 to 10-5 M. The increased Ca2+ concentration signals muscle contraction via the action of two accessory proteins bound to the actin filaments: tropomyosin and troponin (Figure 11.25). Tropomyosin is a fibrous protein that binds lengthwise along the groove of actin filaments. In striated muscle, each tropomyosin molecule is bound to troponin, which is a complex of three polypeptides: troponin C (Ca2+-binding), troponin I (inhibitory), and troponin T (tropomyosin-binding). When the concentration of Ca2+ is low, the complex of the troponins with tropomyosin blocks the interaction of actin and myosin, so the muscle does not contract. At high concentrations, Ca2+ binding to troponin C shifts the position of the complex, relieving this inhibition and allowing contraction to proceed.
Figure 11.25
Association of tropomyosin and troponins with actin filaments. (A) Tropomyosin binds lengthwise along actin filaments and, in striated muscle, is associated with a complex of three troponins: troponin I (TnI), troponin C (TnC), and troponin T (TnT). In (more ) Contractile Assemblies of Actin and Myosin in Nonmuscle Cells
Contractile assemblies of actin and myosin, resembling small-scale versions of muscle fibers, are present also in nonmuscle cells. As in muscle, the actin filaments in these contractile assemblies are interdigitated with bipolar filaments of myosin II, consisting of 15 to 20 myosin II molecules, which produce contraction by sliding the actin filaments relative to one another (Figure 11.26). The actin filaments in contractile bundles in nonmuscle cells are also associated with tropomyosin, which facilitates their interaction with myosin II, probably by competing with filamin for binding sites on actin.
Figure 11.26
Contractile assemblies in nonmuscle cells. Bipolar filaments of myosin II produce contraction by sliding actin filaments in opposite directions. Two examples of contractile assemblies in nonmuscle cells, stress fibers and adhesion belts, were discussed earlier with respect to attachment of the actin cytoskeleton to regions of cell-substrate and cell-cell contacts (see Figures 11.13 and 11.14). The contraction of stress fibers produces tension across the cell, allowing the cell to pull on a substrate (e.g., the extracellular matrix) to which it is anchored. The contraction of adhesion belts alters the shape of epithelial cell sheets: a process that is particularly important during embryonic development, when sheets of epithelial cells fold into structures such as tubes.
The most dramatic example of actin-myosin contraction in nonmuscle cells, however, is provided by cytokinesisthe division of a cell into two following mitosis (Figure 11.27). Toward the end of mitosis in animal cells, a contractile ring consisting of actin filaments and myosin II assembles just underneath the plasma membrane. Its contraction pulls the plasma membrane progressively inward, constricting the center of the cell and pinching it in two. Interestingly, the thickness of the contractile ring remains constant as it contracts, implying that actin filaments disassemble as contraction proceeds. The ring then disperses completely following cell division.
Figure 11.27
Cytokinesis. Following completion of mitosis (nuclear division), a contractile ring consisting of actin filaments and myosin II divides the cell in two.
http://www.ncbi.nlm.nih.gov/books/NBK9961/
This is good. I don’t recall seeing it in the original comment. I am very aware of the actin myosin troponin connection in heart and in skeletal muscle, and I did know about the nonmuscle work. I won’t deal with it now, and I have been working with Aviral now online for 2 hours.
I have had a considerable background from way back in atomic orbital theory, physical chemistry, organic chemistry, and the equilibrium necessary for cations and anions. Despite the calcium role in contraction, I would not discount hypomagnesemia in having a disease role because of the intracellular-extracellular connection. The description you pasted reminds me also of a lecture given a few years ago by the Nobel Laureate that year on the mechanism of cell division.
I actually consider this amazing blog , âSAME SCIENTIFIC IMPACT: Scientific Publishing –
Open Journals vs. Subscription-based « Pharmaceutical Intelligenceâ, very compelling plus the blog post ended up being a good read.
Many thanks,Annette
I actually consider this amazing blog , âSAME SCIENTIFIC IMPACT: Scientific Publishing –
Open Journals vs. Subscription-based « Pharmaceutical Intelligenceâ, very compelling plus the blog post ended up being a good read.
Many thanks,Annette