Received: from ics.uci.edu by ICS.UCI.EDU id aa12577; 9 Jul 89 15:53 PDT To: ML-LIST: ; Cc: TEMP-ML-LIST: ; Subject: Machine Learning List: Vol. 1 No. 1 Reply-to: ml@ICS.UCI.EDU Date: Sun, 09 Jul 89 15:48:05 -0700 From: Michael Pazzani Message-ID: <8907091553.aa12577@ICS.UCI.EDU> Machine Learning List: Vol. 1 No. 1 Sunday, July 9, 1989 Contents: ML List Announcement Biotech data for use with machine learning algorithms (Hunter) Experimentation (Nordhausen) Grand Challenges (Pazzani) The Machine Learning List is moderated. Contributions should be relevant to the scientific study of machine learning. Mail contributions to ml@ics.uci.edu. Mail requests to be added or deleted to ml-request@ics.uci.edu - ---------------------------------------------------------------------- This issue of ML-LIST is being mailed to anyone who requested it by sending mail to ml@ics.uci.edu and to everyone who attended either of two learning workshops at Cornell recently. Future issues will be mailed only to those who have requested it. Please send all future requests to be added or removed from ML-LIST to ml-request@ics.uci.edu. Local redistribution is encouraged. - ---------------------------------------------------------------------- Date: Thu, 6 Jul 89 14:39:17 ADT From: Larry Hunter Subject: Biotech data for use with machine learning algorithms As I discussed in my talk at the recent Machine Learning Workshop, I believe that (1) the test of the quality of a machine learning algorithm ought to be measured primarily by the significance of its novel discoveries, and that (2) Molecular biology provides datasets with the potential for current ML algorithms to make novel discoveries. Mike Pazzani asked if I would post some material about how to get access to these datasets to this bulletin board. There are several molecular biology databases that are freely available to the machine learning community. In general, databases are distributed both by the maintainers and by others who have obtained copies. Maintainers generally charge a media and handling fee. There are generally no restrictions on redistribution, so it may be cheaper to copy a database from someone who already has one. Since the databases are large, downloading them over the internet is frowned on, but it is possible to query the some of the databases by email. In addition to having the raw data in your hands, some of you may find it helpful to acquire some "background knowledge," i.e. some comprehensible readings on molecular biology. I would suggest Mark Ptashne's "A Genetic Switch: Gene Control and Phage Lambda" (Blackwell Scientific Publications, Boston, MA, 1986) as an excellent, accessible example of both the kinds of knowledge relevant to solving molecular biology questions, and of the reasoning processes of biologists. A somewhat idiosyncratic, but very useful collection of algorithms for analyzing genetic and protein sequence data can be found in Russell Doolittle's "Of URFs and ORFs: A Primer on How To Analyze Derived Amino Acid Sequences" (University Science Books, Mill Valley, CA, 1987). A good, detailed text on protein structure for those with some chemistry background is Georg Schultz & Heiner Schirmer's "Principles of Protein Structure" (Springer-Verlag, NY NY, 1978). There are hundreds of potentially useful databases. There is a loosely organized group of computer scientists and biologists working on what they call "The Matrix of Biological Knowledge," a set of tools for integrating and using these many databases. They are running a conference this August 18-19 in New Hampshire. Try contacting Karen Gruskin, Molecular Biology Computer Research Resource, Dana-Farber Cancer Institute, 44 Binney St. Boston, MA 02115 for more information about the conference. What follows is the most current information I have about the major molecular biology databases. The databases are updated on the order of every 6 weeks, often with megabytes of new data (notice the large sizes in ()'s next to each database below). Also, responsibility for maintaining the databases is in a state of flux, so if you have trouble finding something, let me know and I'll try to help you out. A good place to start is a database that lists many of MB databases, where to get them, what's in them, etc. This database of databases is called BDIR (Biological Database Information Resource), and is maintained by: Kathleen Arnett Bioinformatics Division American Type Culture Collection 12301 Parklawn Dr. Rockville, MD 20852-1776 (301) 231-5585 KA3@NIHCU.BITNET Perhaps the most central of all the biology databases is Genbank, which contains well over 10,000 DNA sequences (> 78M). GenBank: c/o Intelligenetics 700 E. El Camino Real Mountain View, CA 94040 (415)962-7364 genbank@bionet-20.bio.net (warning: bionet-20 disappears 30 September 1989) Protein Idenfication Resource (PIR): Protein sequence database, (>16M) c/o National Biomedical Research Foundation Georgetown University Medical Center 3900 Reservoir Rd. NW Washington, DC 20007 (202)625-2121 pirmail@gunbrf.bitnet Protein Databank (PDB, also called Brookhaven). Crystallographic data about the shape of about 100 proteins. Important cross reference with PIR for structural prediction work (>55M) PDB c/o Brookhaven National Laboratory Chemistry Dept Bldg. 555 Upton, NY 11973 (516)282-4382 no email I've been able to find. EMBL the European version of GenBank. Some complementary data (>58M) c/o European Molecular Biology Laboratory Postfach 10 22 09 6900 Heidelberg 1 West Germany datalib@embl.bitnet Also, the National Library of Medicine is interested in supporting machine learning research in molecular biology domains. You might try talking to Peter Clepper in the extramural programs biomedical information support branch (they give grants, graduate student fellowships, etc.) (301) 496-4221. This should be enough to get you off the ground. There will be an AAAI symposium on AI and molecular biology next spring, just in time to present your early results... Have fun! Larry Hunter National Library of Medicine Lister Hill Center, MS-54 Bethesda, MD 20894 (301) 496-9300 hunter@mcs.nlm.nih.gov - ---------------------------------------------------------------------- Date: Fri, 07 Jul 89 15:10:40 -0700 From: Bernd Nordhausen Subject: experimentation Some thoughts about experimentation in machine learning: To say it up front, I am for experimental validation of ML systems, and you will see learning curves in my own work. I think it is valuable that the machine learning community has started to think about validating systems through experiments. But I am cautioning that while experimentation is worthwhile, and we should definitely pursue it, it is not the answer to all of our validation problems. The introduction of comparative studies (i. e. using the same data to evaluate and compare different systems) was progress. However, for many systems the domains are so different that no common data is available. Thus people create their domains and dependent measures to experimentally validate their systems. In these cases there CAN be a danger, that experimental results start to become less meaningful, because too many hidden factors might be involved in the experiments. Furthermore, I argue that not every claim has to be substantiated by a learning curve. Especially at a workshop, it is ok to make a claim and say "I have preliminary results which suggests this claim," without a learning curve to confirm this assertion. Sometimes learning curves can do much more harm than they can do good. A learning curve should imply a careful evaluation, and not just some preliminary results. These are my 2 Pfennigs worth. I am interested to hear from other people what they think about the subject of experimentation, so let the flames roll. Bernd Nordhausen - ---------------------------------------------------------------------- Date: Sunday, 09 Jul 89 11:10:46 -0700 From: Michael Pazzani Subject: Grand Challenges At the recent Machine Learning Talk, Tom Dietterich gave an interesting talk on Grand Challenges for Machine Learning. One of the challenges was learning from natural language texts. In some ways, I think it would be a good idea for people in machine learning to look at natural language processing. Some of the most advanced work on knowledge represention and inference has been motivated by natural language understanding problems. Unlike many explanation-based learning systems, very few NLP systems use Horn clauses to represent knowledge and backward-chaining depth-first search as an inference process. However, many hard problems in learning from natural language text are pure natural language problems that have very little to do with learning (e.g., word sense disambiguation, finding the referents of noun phrases, etc.). Until progress is made in NLP, I doubt that much progress can be made in learning from natural language texts. Perhaps a better challenge for the learning community as opposed to the natural language community would be to incorporate the hand-coded representations of natural language texts into a large memory. How much progress has the CYC project made on this project? Michael Pazzani - ---------------------------------------------------------------------- End of ML-LIST 1.1