Abstract: Methods and genetic sequences are described for use in determining the diagnosis, subtype, prognosis, and disease course of high-grade gliomas, such as glioblastoma multiforme. One such method includes determining increased expression of at least one gene on a chromosome segment in cells of the glioma, the segment being 17:57,851,812-17:57,973,757; 7:127,892,509-7:127,947,649; 12:33,854-12:264,310; or 19:33,329,393-19:35,322,055; and estimating, based on the expression, a predicted length of survival, a probability of survival, or a predicted response to a therapy for the glioma.

Abstract: Data can be characterized and compared by applying an unfolding algorithm to each of at least two Nth order tensors, representing the data, to generate at least two matrices, wherein N>2. The at least two tensors can have a matching number of columns in each of all dimensions except an Nth dimension. The applying the unfolding algorithm preserves the number of columns in one dimension common to (a) one of the at least two tensors and (b) a corresponding one of the at least two matrices, wherein each of the at least two matrices is a full column rank matrix. Each of the matrices is a unique, weighted sum of subtensors having a matching number of columns in each of all dimensions, at least two of the sums having different weighting coefficients. A relative significance of the subtensors is determined as a ratio of the weighting coefficients.

Abstract: According to various embodiments herein, methods for performing diagnosis and prognosis of OV have been provided. In embodiments, a method of determining an estimated outcome or predicting a clinical response to chemotherapy for a patient having ovarian serous cystadenocarcinoma (OV), comprises obtaining a biological sample from a patient diagnosed with OV, said sample comprising at least one of nucleic acids and proteins from the patient; detecting in said sample a value of an indicator of a differential expression; and calculating, by a processor, a weighted sum pattern based on the value of one or more of the indicators of differential expression; and estimating, by the processor and based on the weighted sum pattern, a predicted length of survival of the patient or a predicted clinical response to chemotherapy for the patient.

Abstract: Methods and genetic sequences are described for use in determining the diagnosis, subtype, prognosis, and disease course of high-grade gliomas, such as glioblastoma multiforme. One such method includes determining increased expression of at least one gene on a chromosome segment in cells of the glioma, the segment being 17:57,851,812-17:57,973,757; 7:127,892,509-7:127,947,649; 12:33,854-12:264,310; or 19:33,329,393-19:35,322,055; and estimating, based on the expression, a predicted length of survival, a probability of survival, or a predicted response to a therapy for the glioma.

Abstract: Systems and methods are described for medical characterization of biological data. One such method includes applying a decomposition algorithm, by a processor, to an Nth-order tensor representing data, wherein N?2, to generate, from at least two submatrices A and B of the tensor, eigenvectors of each of AAT, ATA, BBT, and BTB; where the data comprise indicators, represented in respective rows and columns of the tensor, of values of at least two index parameters; and determining an indicator of a health parameter of a subject, the determining being based on the eigenvectors and on values, associated with the subject, of the at least two index parameters. In some cases, the eigenvectors of ATA are the same as the eigenvectors of BTB.