Automatic nuclear segmentation. In an embodiment, a plurality of superpixels are determined in a digital image. For each of the superpixels, any superpixels located within a search radius from the superpixel are identified, and, for each unique local combination between the superpixel and any identified superpixels located within the search radius from the superpixel, a local score for the local combination is determined. One of a plurality of global sets of local combinations with an optimum global score is identified based on the determined local scores.

In response to receiving a user inquiry describing one more symptoms of a medical condition, an online hospital visit prediction system can determine whether the user will visit the hospital for treatment related to the medical condition the day following the user inquiry, termed “hospital day.” Query logs are stored for a large plurality of users. Using the query logs, a prediction model can be trained using predetermined features extracted from the query logs of each of a plurality of users for a predetermined period of time prior to the day the user visited the hospital. The predetermined features can be used to train the prediction model with a high accuracy to determine whether a future user will visit the hospital the day following a query about a medical condition.

Methods and systems for knowledge pattern search and analysis for selecting microorganisms based on desired metabolic properties or biological behaviors are disclosed in various embodiments of the invention. In one embodiment of the invention, a computer-implemented method for selecting a purpose-specific microorganism first compiles microorganisms' profiles by linking each microorganism's methanogenic, hydrogenic, electrogenic, another metabolic property, and/or another biological behavior to genetic and chemical fingerprints of metabolic and energy-generating biological pathways. Then, based on the compiled profiles of the microorganisms, the computer-implemented method groups the microorganisms into pathway characteristics using machine-learning and pattern recognition performed on a computer system, and subsequently generates a prediction called “discovered characteristics” for a desired metabolic property or a desired biological behavior of at least one microorganism. Furthermore, a profile match score may be calculated to indicate usefulness of one or more microorganisms for renewable energy generation from biological waste materials or wastewater.

A system, method and apparatus for executing a bioinformatics analysis on genetic sequence data includes an integrated circuit formed of a set of hardwired digital logic circuits that are interconnected by physical electrical interconnects. One of the physical electrical interconnects forms an input to the integrated circuit that may be connected with an electronic data source for receiving reads of genomic data. The hardwired digital logic circuits may be arranged as a set of processing engines, each processing engine being formed of a subset of the hardwired digital logic circuits to perform one or more steps in the bioinformatics analysis on the reads of genomic data. Each subset of the hardwired digital logic circuits may be formed in a wired configuration to perform the one or more steps in the bioinformatics analysis.

A method and system for determining interaction sites between biosequences is described herein. A dataset of contact data for a plurality of biomolecule pairs is obtained to account their frequency of occurrence. Statistical weights are determined for each frequency of occurrence. Each vector of a statistical residual vector space (SRV) is decomposed through principal component decomposition. The vectors of the SRV are re-projected back to a new SRV with a new set of coordinates. A feature vector is generated and inputted into a predictor for outputting a likelihood of an interaction site.

A business method for use in classifying patient samples. The method includes steps of collecting case samples representing a clinical phenotypic state and control samples representing patients without said clinical phenotypic state. Preferably the system uses a mass spectrometry platform system to identify patterns of polypeptides in said case samples and in the control samples without regard to the specific identity of at least some of said polypeptides. Based on identified representative patterns of the state, the business method provides for the marketing of diagnostic products using representative patterns. The present invention relates to systems and methods for identifying new markers, diagnosing patients with a biological state of interest, and marketing/commercializing such diagnostics. The present invention relates to systems and methods of greater sensitivity, specificity, and/or cost effectiveness.

The invention provides methods for the use of gene expression measurements to classify or identify tumors in samples obtained from a subject in a clinical setting, such as in cases of formalin fixed, paraffin embedded (FFPE) samples.

The invention relates to a method of determining an infection of a patient with Klebsiella species potentially resistant to antimicrobial drug treatment by detecting mutations in the genes, parC, KPN 01607, gyrA, KPN 02451, baeR, aceF, ybgH, ynjE, KPN 01951, KPN 01961, KPN 02114, mhpA, KPN 02128, KPN 02144, KPN 02149, ydiJ, btuE, oppC, pth, KPN 02298, KPN 02302, dadA, yoaA, ftn, cbl, hisB, yegQ, yehY, KPN 02580, yejH, KPN 02621, yfaW, KPN 02170, KPN 02025, livG, livM, livH, fliY, yedQ, abgB, treA, baeS, KPN 02399, ydcR, anmK, ccmF, KPN 02440, KPN 02540, KPN 01752, and KPN 04195, and/or KOX 26125, KOX 13365, KOX 16735, KOX 25695, KOX 12270, and KOX 15055; a method of selecting a treatment of a patient suffering from an antibiotic resistant Klebsiella infection; and a method of determining an antibiotic resistance profile for bacterial microorganisms of Klebsiella species, as well as computer program products used in these methods. In an exemplary method, a sample is used for molecular testing and then a molecular fingerprint is taken. The result is then compared to a reference library and the result is reported.

The disclosure provides a method of predicting de novo resistance to androgen receptor (AR) targeted therapy in a tumor of a prostate cancer patient comprising (a) performing a direct analysis comprising immunofluorescent staining and morphological characteristization of nucleated cells in a blood sample obtained from the patient to generate circulating tumor cell (CTC) data, wherein the analysis comprises determining a measurable feature of a panel of traditional and non-traditional CTC biomarkers for de novo resistance to androgen receptor (AR) targeted therapy, and (b) evaluating the CTC data to determine the probability of de novo resistance to the AR targeted therapy in the tumor of the prostate cancer patient. Further disclosed are the panel of traditional and non-traditional CTC biomarkers for the methods.

This disclosure presents a model for identifying correlations in genome-wide association studies (GWAS) with function-valued traits that provides increased power and computational efficiency by use of a Gaussian process regression with radial basis function (RBF) kernels to model the function-valued traits and specialized factorizations to achieve speed. A Gaussian Process is assigned to each partition for each allele of a given single nucleotide polymorphism (SNP) which yields flexible alternative models and handles a large number of data points in a way that is statistically and computationally efficient. This model provides techniques for handling missing and unaligned function values such as would occur when not all individuals are measured at the same time points. If the data is complete algebraic re-factorization by decomposition into Kronecker products reduces the time complexity of this model thereby increasing processing speed and reducing memory usage as compared to a naive implementation.