Tag Archives: literature mining

CNETS Team Uses Instagram to monitor Drug Interactions and Adverse Reactions

Subnetwork of symptoms and drugs associated with Psoriasis and Epilepsy
Subnetwork of symptoms and drugs associated with Psoriasis and Epilepsy

Update: On March 21st, 2016 the paper described below (PMC4720984) was highlighted by Russ Altman from Stanford University in his yearly review as one of 30 important papers of the year in translational bioinformatics.

Using complex networks analysis and social media mining, CNETS researchers from the CASCI team have found that Instagram, a growing social media platform among teens, can be used “to uncover drug-drug interactions (DDI) and adverse drug reactions (ADR).” The work shows that this popular social media service is “a very powerful source of data with great promise in the public-health domain”. The study, “Monitoring Potential Drug Interactions and Reactions via Network Analysis of Instagram User Timelines,” supported by an R01 grant from the National Institutes of Health as well as a gift from Persistent Inc., was recently published and presented at the Pacific Symposium on Biocomputing (PSB 2016), in Hawaii. (PubMed, arXiv). The results are based on almost 7.000 user timelines associated with depression drugs which combined have 5+ million posts.

Continue reading CNETS Team Uses Instagram to monitor Drug Interactions and Adverse Reactions

NIH Project to study Drug-Drug Interaction

Prof. Luis Rocha from CNETS at IU Bloomington, Prof. Lang Li from IUPUI Medical School, and Prof. Hagit Shatkay from the University of Delaware have been awarded a four-year, $1.7M grant from NIH/NLM to study the large-scale extraction of drug-Interaction from medical text. Drug-drug interaction (DDI) leads to adverse drug reactions, emergency room visits and hospitalization, thus posing a major challenge to public health. To circumvent risk to patients, and to expedite biomedical research, both clinicians and biologists must have access to all available knowledge about potential DDI, and understand both causes and consequences of such interactions. However, mere identification of interactions does not directly support such understanding, as evidence for DDI varies broadly, from reports of molecular interactions in basic-science journals, to clinical descriptions of adverse-effects in a myriad of medical publications. This project will develop tools that focus directly on large-scale identification and gathering of various types of reliable experimental evidence of DDI from diverse sources. The successful completion of the project will provide clinicians and biologists with substantiated knowledge about drug interactions and with informatics tools to obtain such information on a large-scale, laying the basis for preventing adverse drug reactions and for exploring alternative treatments.

PIARE (Protein Interaction Abstract Relevance Evaluator)

Research | People | Academics | News and Meetings | Publications-online | Media Mentions | Relevant Conferences

The Protein Interaction Abstract Relevance Evaluation (PIARE) service, was created to implement the binary classifier we produced for the Protein-Protein Interaction Article Classification Task in Biocreative II and Biocreative II.5. Supplementary materials for our Biocreative III paper are available as a zip file with instructions.

Literature Mining
Biomedical Literature Mining

Relevant Publications:

A. Abi-Haidar, J. Kaur, A. Maguitman, P. Radivojac, A. Retchsteiner, K. Verspoor, Z. Wang, and L.M. Rocha [2008]. “Uncovering protein interaction in abstracts and text using a novel linear model and word proximity networks“. Genome Biology. 9(Suppl 2):S11

A. Kolchinsky, A. Abi-Haidar, J. Kaur, A.A. Hamed and L.M. Rocha [2010]. “Classification of protein-protein interaction full-text documents using text and citation network features.IEEE/ACM Transactions On Computational Biology And Bioinformatics, 7(3):400-411. DOI: doi.ieeecomputersociety.org/10.1109/TCBB.2010.55

A. Lourenço, M. Conover, A. Wong, A. Nematzadeh, F. Pan, H. Shatkay, and L.M. Rocha [2011].”A Linear Classifier Based on Entity Recognition Tools and a Statistical Approach to Method Extraction in the Protein-Protein Interaction Literature“. BMC Bioinformatics. 12(Suppl 8):S12. DOI: 10.1186/1471-2105-12-S8-S12.

Luis Rocha

Luis Rocha
Luis Rocha

School of Informatics and Computing
Indiana University

My web site and Research Lab (CASCI).


Luis M. Rocha is Professor of Informatics and Cognitive Science at Indiana University, Bloomington, USA. He is director of the Complex Networks & Systems graduate Program in Informatics, member of the Indiana University Network Science Institute, and core faculty of the Cognitive Science Program at Indiana University, Bloomington, USA. Dr. Rocha is a Fulbright Scholar and is also Principal Investigator and the director of the Computational Biology Collaboratorium and in the Direction of PhD program in Computational Biology at the Instituto Gulbenkian da Ciencia, Portugal. His research is on complex networks & systems, Computational & Systems Biology, and Computational Intelligence and he is the Principal Investigator of the Complex Adaptive Systems & Computational Intelligence lab (CASCI). He received his Ph.D in Systems Science in 1997 from the State University of New York at Binghamton. From 1998 to 2004 he was a permanent staff scientist at the Los Alamos National Laboratory, where he founded and led a Complex Systems Modeling Team during 1998-2002, and was part of the Santa Fe Institute research community. He has organized major conferences such as the Tenth International Conference on the Simulation and Synthesis of Living Systems (Alife X) and the Ninth European Conference on Artificial Life (ECAL 2007). He has published many articles in scientific and technology journals, and has been the recipient of several scholarships and awards. At Indiana University, he has received the Indiana University, School of Informatics & Computing, Trustees Award for Teaching Excellence in 2006 and 2015 after developing the complex systems training program and syllabi for several courses. Additional information about Prof. Rocha’s research, academic and personal activities is available on his website. Contact Information:

In The USA:
Center for Complex Networks and Systems Research
School of Informatics & Computing
Indiana University, 919 E. 10th St
Bloomington IN, 47408, USA

In Portugal:
Instituto Gulbenkian de Ciência
Rua da Quinta Grande, 6
Apartado 14, P-2781-901 Oeiras, Portugal

Course and research-related blogs


Complex Adaptive Systems and Computational Intelligence

The Complex Adaptive Systems and Computational Intelligence (CASCI) group at Indiana University and the Instituto Gulbenkian de Ciencia works on complex networks & systems and their applications to informatics, biology, health, and social systems. We are particularly interested in the informational properties of natural and artificial systems which enable them to adapt and evolve. This means both understanding how information is fundamental for the evolutionary capabilities of natural systems, as well as abstracting principles from natural systems to produce adaptive information technology.

Our research projects are on complex networks & systems, computational and systems biology, and computational intelligence; all our publications are available online as are news about our group. Additional information available on Luis Rocha’s Website and our group page at the Instituto Gulbenkian de Ciencia.

See our current roster and information on how to join our group. As a group, we are seriously interconnected with other research groups and networks: The Center for Complex Networks and Systems (CNets), the Indiana University Network Science Institute, the Cognitive Science Program, the FLAD Computational Biology Collaboratorium, the Instituto Gulbenkian de Ciência, and the Champalimaud Neuroscience Program.

You are welcome to join our mailing list CASCI-L by sending an email message to list@list.indiana.edu from the address you want to subscribe to the list, with subject line: subscribe CASCI-L Firstname Lastname (leave the message body blank.) More detailed instructions are available.