Abstract: An automated life science laboratory or a storage facility for biological specimens may be located together with or in close proximity to a data center. The location of the data center, the automated life science laboratory, and the storage facility may be a location in which land and/or electricity are less expensive than locations where the biological specimens are collected. The automated life science laboratory may have a high-capacity data connection to the data center. The life science laboratory, storage facility, and the data center may share a connection to the electrical grid, an HVAC system, and/or a security perimeter. A biological specimen may be removed from storage at the storage facility, process by one or more biotechnology protocols at the automated life science laboratory, and data from the processing may be stored in the data center.

Abstract: An automated life science laboratory or a storage facility for biological specimens may be located together with or in close proximity to a data center. The location of the data center, the automated life science laboratory, and the storage facility may be a location in which land and/or electricity are less expensive than locations where the biological specimens are collected. The automated life science laboratory may have a high-capacity data connection to the data center. The life science laboratory, storage facility, and the data center may share a connection to the electrical grid, an HVAC system, and/or a security perimeter. A biological specimen may be removed from storage at the storage facility, process by one or more biotechnology protocols at the automated life science laboratory, and data from the processing may be stored in the data center.

Abstract: The subject invention provides for an advanced and robust system and method that facilitates detecting spam. The system and method include components as well as other operations which enhance or promote finding characteristics that are difficult for the spammer to avoid and finding characteristics in non-spam that are difficult for spammers to duplicate. Exemplary characteristics include analyzing character and/or number sequences, strings, and sub-strings, detecting various entropy levels of one or more character sequences, strings and/or sub-strings and analyzing message headers.

Abstract: Shift invariant predictors are described herein. By way of example, a system for predicting binding information relating to a binding of a protein and a ligand can include a trained binding model and a prediction component. The trained binding model can include a hidden variable representing an unknown alignment of the ligand at a binding site of the protein. The prediction component can be configured to predict the binding information by employing information about the protein's sequence, the ligand's sequence and the trained binding model.

Abstract: A system and method are employed to construct an association network to visualize relationships between variables of a data set. The relationships characterized by the association network may include symmetric or asymmetric measures of association between variables learned from the data. The association network includes nodes, which represent variables, and edges, which represent associations between variables. As a result, the association network helps a user to visualize useful information from data according to the determined measure of association.

Abstract: The subject invention provides for an advanced and robust system and method that facilitates detecting spam. The system and method include components as well as other operations which enhance or promote finding characteristics that are difficult for the spammer to avoid and finding characteristics in non-spam that are difficult for spammers to duplicate. Exemplary characteristics include examining origination features in pairs, analyzing character and/or number sequences, strings, and sub-strings, detecting various entropy levels of one or more character sequences, strings and/or sub-strings as well as analyzing message and/or feature sizes.

Abstract: The subject invention provides for an advanced and robust system and method that facilitates detecting spam. The system and method include components as well as other operations which enhance or promote finding characteristics that are difficult or the spammer to avoid and finding characteristics in non-spam that are difficult for spammers to duplicate. Exemplary characteristics include examining origination features in pairs, analyzing character and/or number sequences, strings, and sub-strings, detecting various entropy levels of one or more character sequences, strings and/or sub-strings as well as analyzing message and/or feature sizes.

Abstract: The subject invention provides systems and methods that facilitate AIDS vaccine cocktail assembly via machine learning algorithms such as a cost function, a greedy algorithm, an expectation-maximization (EM) algorithm, etc. Such assembly can be utilized to generate vaccine cocktails for species of pathogens that evolve quickly under immune pressure of the host. For example, the systems and methods of the subject invention can be utilized to facilitate design of T cell vaccines for pathogens such HIV. In addition, the systems and methods of the subject invention can be utilized in connection with other applications, such as, for example, sequence alignment, motif discovery, classification, and recombination hot spot detection. The novel techniques described herein can provide for improvements over traditional approaches to designing vaccines by constructing vaccine cocktails with higher epitope coverage, for example, in comparison with cocktails of consensi, tree nodes and random strains from data.

Abstract: Systems and methodologies for efficient vaccine design are disclosed herein. A methodology for efficient vaccine design in accordance with one or more embodiments disclosed herein may be operable to receive a graph having vertices corresponding to epitope sequences present in the pathogen population, weights for respective vertices corresponding to respective frequencies with which corresponding epitope sequences appear in the pathogen population, and directed edges that connect vertices that correspond to overlapping epitope sequences. Such a methodology may also be operable to determine a candidate vaccine sequence of overlapping epitope sequences by identifying a path though the graph corresponding to a series of connected vertices and directed edges that maximizes the total weight of the vertices in the path for a desired vaccine sequence length.

Abstract: Cluster models are described herein. By way of example, a system for predicting binding information relating to a binding of a protein and a ligand can include a trained binding model and a prediction component. The trained binding model can include a probability distribution and a hidden variable that represents a cluster of protein sequences, and/or a set of hidden variables representing learned supertypes. The prediction component can be configured to predict the binding information by employing information about the protein's sequence, the ligand's sequence and the trained binding model.

Abstract: Aspects of the subject matter described herein relate to predicting phenotypes. In aspects, a probabilistic predictor is used to summarize a relationship between a set of biological predictors and a phenotype. The probabilistic predictor may use a function that is selected based on the type of the phenotype (e.g., binary, multi-state, or continuous). The probabilistic predictor may use genetic and/or epigenetic information. The probabilistic predictor may be trained on a portion of the data in conjunction with predicting phenotypes in another portion of the data. The probabilistic predictor may be used for various analyses including genome-wide association analysis and gene-set enrichment analysis.

Abstract: Architecture for detecting and removing obfuscating clutter from the subject and/or body of a message, e.g., e-mail, prior to filtering of the message, to identify junk messages commonly referred to as spam. The technique utilizes the powerful features built into an HTML rendering engine to strip the HTML instructions for all non-substantive aspects of the message. Pre-processing includes pre-rendering of the message into a final format, which final format is that which is displayed by the rendering engine to the user. The final format message is then converted to a text-only format to remove graphics, color, non-text decoration, and spacing that cannot be rendered as ASCII-style or Unicode-style characters. The result is essentially to reduce each message to its common denominator essentials so that the junk mail filter can view each message on an equal basis.

Abstract: A unique recording system and method that facilitates recording live meetings, discussions or conversations whereby such recordings are available for immediate or near immediate playback is provided. As a result, a user who has momentarily become distracted or inattentive during the meeting can quickly re-listen to what was missed or misunderstood in order to readily catch up to the current discussion. The current discussion can continue to be recorded during playback of any previously recorded data. User behavior can be monitored to estimate when the user has started to become inattentive and likely segments or time points of the recordings can be suggested for playback. One or more portions of the recordings can be filtered or selected for playback so that any desired content can be eliminated or skipped in the playback version.

Abstract: An indirect calorimeter estimates nutritional caloric intake by periodically monitoring weight and sensing physical exercise (i.e., physiological data and/or motion data related to physical exertion), which can then be used in a calorimetry model derived from regression analysis of a population (e.g., linear regression, feed-forward neural network, Gaussian process, boosted regression tree, etc.). A strap-on user device for tracking exercise can detect one or more of heart rate, body temperature, skin resistance, motion/acceleration sensing (e.g., pedometer, accelerometer), velocity sensing (e.g., global positioning system (GPS)), and an intelligent, integrated exercise machine (e.g., treadmill, exercise bike, etc.). To gain further fidelity, the user can fine-tune the estimate by undergoing a journal-based routine for a relatively short period of time or clinical calorimetry measurement (e.g., respiratory calorimeter), thereby providing a baseline for resting or exercising metabolic rate.

Abstract: A dependency network is created from a training data set utilizing a scalable method. A statistical model (or pattern), such as for example a Bayesian network, is then constructed to allow more convenient inferencing. The model (or pattern) is employed in lieu of the training data set for data access. The computational complexity of the method that produces the model (or pattern) is independent of the size of the original data set. The dependency network directly returns explicitly encoded data in the conditional probability distributions of the dependency network. Non-explicitly encoded data is generated via Gibbs sampling, approximated, or ignored.

Abstract: Epitope prediction models are described herein. By way of example, a system for predicting epitope information relating to a epitope can include a classification model (e.g., logistic regression model). The trained classification model can illustratively operatively execute one ore logistic functions on received protein data, and incorporate one or more of hidden binary variables and shift variables that when processed represent the identification (e.g., prediction) of one or more desired epitopes. The classification model can be configured to predict the epitope information by processing data including various features of an epitope, MHC, MHC supertype, and Boolean combinations thereof.

Abstract: A decision theoretic approach to targeted solicitation, by maximizing expected profit increases, is disclosed. A decision theoretic model is used to identify a sub-population of a population to solicit, where the model is constructed to maximize an expected increase in profits. A decision tree in particular can be used as the model. The decision tree has paths from a root node to a number of leaf nodes. The decision tree has a split on a solicitation variable in every path from the root node to each leaf node. The solicitation variable has two values, a first value corresponding to a solicitation having been made, and a second value corresponding to a solicitation not having been made.

Abstract: A visualization input system is provided. The system includes a visualization component that receives input gestures from a user (or users) and translates the gestures into one or more data manipulation commands. A distribution component receives the data manipulation commands and propagates data modifications across one or more databases in view of the commands. This includes a rights component that enables the data modifications to be implemented across the one or more databases.

Abstract: Statistical models for identifying associations are described herein. By way of example, a system for identifying associations between variables can include a model builder and an association identifier. The model builder can receive observations about the variables and generate a null model and a non-null model. The association identifier can assess the strength of the association between the variables by determining how much the non-null model better explains the observed data than the null model. Additionally or alternatively, the structure of the observed data can be inferred simultaneously with the statistical model.

Abstract: A system comprising a machine learning classifier trained on a plurality of associations between a host and a pathogen to predict a pathogen characteristic is described herein. The pathogen characteristic can relate to a disease state of the host. Computer-executable instructions for performing a method of forecasting a portion of a target molecule anticipated to influence an organism's condition also are described herein. The method comprises employing population data to automatically analyze one or more areas of the target molecule to determine the portion of the target molecule anticipated to influence the organism's condition. The population data can pertain to at least one relationship between at least one diverse organism trait and the target molecule. One or more epitopes forecast by employing the method also are contemplated.