Bioinformatics

Applications of AI to Biology and Biomedical Problems

"The genetics revolution is generating such a gigantic glut of information that artificial intelligence may be the only way scientists will ever put it to practical use."

- excerpt from A Map That Maps Gene Functions

"'We have now demonstrated that combining proteomic technology with artificial intelligence based bioinformatics can be a powerful tool, and is a new paradigm in the detection and diagnosis of both ovarian and prostate cancers,' said Lance Liotta, M.D., Ph.D., the senior investigator on the study from NCI's Center for Cancer Research."

- excerpt from Protein Patterns In Blood May Predict Prostate Cancer Diagnosis

Applications

Contents

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Good Starting Places

Publication

AI in Medicine: The Spectrum of Challenges from Managed Care to Molecular Medicine

"AI has embraced medical applications from its inception, and some of the earliest work in successful application of AI technology occurred in medical contexts. Medicine in the twenty-first century will be very different than medicine in the late twentieth century. Fortunately, the technical challenges to AI that emerge are similar, and the prospects for success are high."

Source

By Altman, Russ, 1999
Publication

Artificial Intelligence and Molecular Biology

The current volume is an effort to bridge that range of exploration, from nucleotide to abstract concept, in contemporary AI/MB research. Although this book is currently out of print in hardcopy form, links to the electronic (PDF) files of the book can be accessed from the book’s contents, listed below.

Brochure

Foreward / ix
Joshua Lederberg

Chapter 1
Molecular Biology for Computer Scientists / 1
Lawrence Hunter

Chapter 2
The Computational Linguistics of Biological Sequences / 47
David B. Searls

Chapter 3
Neural Networks, Adaptive Optimization, and RNA Secondary Structure Prediction / 121
Evan W. Steeg

Chapter 4
Predicting Protein Structural Features With Artificial Neural Networks / 161
Stephen R. Holbrook, Steven M. Muskal, and Sung-Hou Kim

Chapter 5
Developing Hierarchical Representations for Protein Structures: An Incremental Approach / 195
Xiru Zhang and David Waltz

Chapter 6
Integrating AI with Sequence Analysis / 210
Richard H. Lathrop, Teresa A. Webster, Randall F. Smith, Patrick H. Winston, and Temple F. Smith

Chapter 7
Planning to Learn about Protein Structure / 259
Lawrence Hunter

Chapter 8
A Qualitative Biochemistry and its Application to the Regulation of the Tryptophan Operon / 289
Peter D. Karp

Chapter 9
Identification of Qualitatively Feasible Metabolic Pathways / 325
Michael L. Mavrovouniotis

Chapter 10
Knowledge-Based Simulation of DNA Metabolism:Prediction of Action and Envisionment of Pathways / 365
Adam R. Galper, Douglas L. Brutlag and David H. Millis

Chapter 11
An AI Approach to the Interpretation of the NMR Spectra of Proteins / 396
Peter Edwards, Derek Sleeman, Gordon C. K. Roberts, and Lu Yun Lian

Chapter 12
Molecular Scene Analysis: Crystal Structure Determination Through Imagery / 433
Janice I. Glasgow, Suzanne Fortier and Frank H. Allen

By Lawrence Hunter (Editor), 1993
book, collection

General Readings

Publication

Daniela Witten: Using artificial intelligence to study genomes

Daniela Witten, an assistant professor of biostatistics at the University of Washington in Seattle, is developing artificial intelligence programs to sort the slurry, helping researchers develop more personalized and effective treatments for cancer and other diseases. Witten is using statistical machine learning to discover the nuggets of gold in the ore of biological research. A statistical analysis of the 3 billion base pairs of DNA making up a cancer cell may be able to identify the pairs or combination of pairs responsible for certain characteristics of the cancer. Before I got to college, I was planning to study foreign languages, Witten said in an interview with the blog Simply Statistics.

Source

May 1 2012, By Williams, Clint
Witten, Daniela
interview
Publication

Deep Learning How I Did It: Merck 1st place interview

Kaggle Competition = 

Help develop safe and effective medicines by predicting molecular activity.

Whenever possible, we prefer to learn features rather than engineer them. This preference probably gives us a disadvantage relative to other Kaggle competitors who have more practice doing effective feature engineering....

As far as supervised learning goes, our solution had three essential components: single-task neural networks, multi-task neural networks, and Gaussian process regression. The neural nets typically had multiple hidden layers, used rectified linear hidden units, and used "dropout" to prevent overfitting.

Source

By Dahl, George, 2012
competition, hinton, interview
Publication

AI system predicts medicine's hidden powers

Treatments for new or drug-resistant infectious diseases may already be in our medicine cabinets, say the molecular biologists responsible for developing an artificial-intelligence system that can predict unknown antibiotic properties of existing drugs.

Source

By Inman, Mason, 2007
Publication

Protein secondary structure prediction using logic-based machine learning

By Muggleton, S., R. King, and M. Sternberg, 1992
classic
Link

The Joshua Lederberg Papers

From the National Library of Medicine's Profiles in Science archival collection.

Contains a wealth of information about DENDRAL, "a prototype for expert systems and the first use of artificial intelligence in biomedical research." as well as other aspects of the life and scientific achievements of Nobel Prize winner,  Joshua Lederberg.

Source

collection
Link

A Machine With a Mind of Its Own

A Machine With a Mind of Its Own---Ross King wanted a research assistant who would work 24/7 without sleep or food. So he built one. By Oliver Morton. Wired Magazine (August 2004, Issue 12.08). "The 'robot scientist' (King has resisted the temptation of a jazzy acronym) may look like a mere labor-saving gizmo, shuttling back and forth ad nauseam, but it's much more than that. Biology is full of tools with which to make discoveries. Here's a tool that can make discoveries on its own."

Source

News

AI in the News

Finding Hidden Side Effects, With Web Search Data

Using automated software tools to examine queries by 6 million Internet users taken from Web search logs in 2010, the researchers looked for searches relating to an antidepressant, paroxetine, and a cholestorol lowering drug, pravastatin. They were able to find evidence that the combination of the two drugs caused high blood sugar.

 

Source

Mar 7 2013, By Markoff, John
AI in the News

Using computational biology for the annotation of proteins

Research carried out at Universidad Carlos III of Madrid in collaboration with the Centro Nacional de Investigaciones Oncologicas employed computational techniques to improve the characterization of proteins. The system they developed has allowed them to predict, for example, the relationship between two human proteins and telomeres, which led to their possible implication in cellular aging and the development of cancer; this awaits experimental verification.

Source

Nov 26 2012
protein
AI in the News

NAB to customers: you're the voice on security

National Australia Bank will begin using voice recognition technology to identify its phone customers in the latest move towards the use of biometric security among the big banks. The company said that the technology, which identifies a person by their speech, will cut waiting times for users and boost security.

Source

Nov 21 2012
AI in the News

Dangerous side effect of common drug combination discovered by data mining

A widely used combination of two common medications may cause unexpected increases in blood glucose levels, according to a study conducted at the Stanford University School of Medicine, Vanderbilt University and Harvard Medical School. ... “It’s very exciting because we were led to this conclusion by mining data that already exists, but of which many people were skeptical,” said [Prof. Russ] Altman. “Physicians tend to think of electronic medical records as ways to better track data about single patients, but there’s another really important component to them — their utility in looking at population effects. The information is there to change health-care practice in a meaningful, substantial way.” Altman, the Guidant Professor for Applied Biomedical Engineering and the chair of Stanford’s bioengineering department, is the senior author of the study, published online May 25 in Clinical Pharmacology and Therapeutics.

Source

May 25 2011

Classic Articles & Books

Publication

Protein secondary structure prediction using logic-based machine learning

By Muggleton, S., R. King, and M. Sternberg, 1992
classic

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