Embodiments facilitate generation of a prediction of long-term survival (LTS) or short-term survival (STS) of Glioblastoma (GBM) patients. A first set of embodiments discussed herein relates to training of a machine learning classifier to determine a prediction for LTS or STS based on a radiographic-deformation and textural heterogeneity (r-DepTH) descriptor generated based on radiographic images of tissue demonstrating GBM. A second set of embodiments discussed herein relates to determination of a prediction of disease outcome for a GBM patient of LTS or STS based on an r-DepTH descriptor generated based on radiographic imagery of the patient.

The present disclosure proposes an apparatus for determining an intestinal microbiome index. The apparatus according to the present disclosure may: obtain test information about a biological sample of a subject from a test apparatus; determine, based on the test information, first information about the similarity of intestinal microflora, second information about a proportion of harmful intestinal microflora, third information about a proportion of beneficial intestinal microflora, and/or fourth information about the diversity of intestinal microflora; and determine an intestinal microbiome index indicating a state of intestinal microbiome of the subject based on the determined information.

The present disclosure proposes an apparatus for determining an intestinal microbiome index. The apparatus according to the present disclosure may: obtain test information about a biological sample of a subject from a test apparatus; determine, based on the test information, first information about the similarity of intestinal microflora, second information about a proportion of harmful intestinal microflora, third information about a proportion of beneficial intestinal microflora, and/or fourth information about the diversity of intestinal microflora; and determine an intestinal microbiome index indicating a state of intestinal microbiome of the subject based on the determined information.

Techniques are provided for predicting MHC-peptide binding affinity. A plurality of training peptide sequences is obtained, and a neural network model is trained to predict MHC-peptide binding affinity using the training peptide sequences. An encoder of the neural network model comprising an RNN is configured to process an input training peptide sequence to generate a fixed-dimension encoding output by applying a final hidden state of the RNN at intermediate state outputs of the RNN to generate attention weighted outputs, and linearly combining the attention weighted outputs. A fully connected layer following the encoder is configured to process the fixed-dimension encoding output to generate an MHC-peptide binding affinity prediction output. A computing device is configured to use the trained neural network to predict MHC-peptide binding affinity for a test peptide sequence.

Techniques are provided for predicting MHC-peptide binding affinity. A plurality of training peptide sequences is obtained, and a neural network model is trained to predict MHC-peptide binding affinity using the training peptide sequences. An encoder of the neural network model comprising an RNN is configured to process an input training peptide sequence to generate a fixed-dimension encoding output by applying a final hidden state of the RNN at intermediate state outputs of the RNN to generate attention weighted outputs, and linearly combining the attention weighted outputs. A fully connected layer following the encoder is configured to process the fixed-dimension encoding output to generate an MHC-peptide binding affinity prediction output. A computing device is configured to use the trained neural network to predict MHC-peptide binding affinity for a test peptide sequence.

Described herein are methods such as multi-omic methods for assessing a disease such as cancer. The multi-omic methods may integrate proteomic, transcriptomic, genomic, lipidomic, or metabolomic data. The method screening diseases or disease states. Also described herein are methods for screening for diseases or disease states from biological samples. The methods may include assessing whether a nodule, mass, or cyst is cancerous.

A method for processing electronic images using uncertainty estimation may be used to determine whether to use an artificial intelligence (AI) assisted prediction. The method may include receiving one or more electronic images associated with a pathology specimen and providing the one or more electronic images to a machine learning model. The machine learning model may perform operations including determining a certainty level corresponding to a certainty that a predetermined AI system will provide an accurate prediction, determining whether the certainty level equals or exceeds a predetermined confidence threshold, and, upon determining that the certainty level does not equal or exceed a predetermined confidence threshold, determining to not use the predetermined AI system.

A method for processing electronic images using uncertainty estimation may be used to determine whether to use an artificial intelligence (AI) assisted prediction. The method may include receiving one or more electronic images associated with a pathology specimen and providing the one or more electronic images to a machine learning model. The machine learning model may perform operations including determining a certainty level corresponding to a certainty that a predetermined AI system will provide an accurate prediction, determining whether the certainty level equals or exceeds a predetermined confidence threshold, and, upon determining that the certainty level does not equal or exceed a predetermined confidence threshold, determining to not use the predetermined AI system.

Systems and methods to automatically de novo assemble a set of unordered read sequences into one or more, larger nucleotide sequences are presented. The method involves first creating two identical sets of the reads, dividing each read in both sets into smaller sorted mer sequences and then comparing the mers for each read in set 1 to the mers from each read in set 2 to exhaustively identify overlapping segments. Overlap information is used to construct a modified assembly string graph, traversal of which produces a sorted string graph layout file consisting of all the reads ordered left to right including their approximate starting offset position. The sorted string graph layout file is then processed by a novel multiple sequence alignment system that uses mer matches between all the overlapping reads at a given position to place matching individual bases from each read into columns from which an overall consensus sequence is determined.

Systems and methods to automatically de novo assemble a set of unordered read sequences into one or more, larger nucleotide sequences are presented. The method involves first creating two identical sets of the reads, dividing each read in both sets into smaller sorted mer sequences and then comparing the mers for each read in set 1 to the mers from each read in set 2 to exhaustively identify overlapping segments. Overlap information is used to construct a modified assembly string graph, traversal of which produces a sorted string graph layout file consisting of all the reads ordered left to right including their approximate starting offset position. The sorted string graph layout file is then processed by a novel multiple sequence alignment system that uses mer matches between all the overlapping reads at a given position to place matching individual bases from each read into columns from which an overall consensus sequence is determined.