Multiple instance learning for peptide-mhc presentation prediction

2:

• The method according to claim 1, wherein the MIL classifier fθ is trained by using the loss function (L) of the form:
• L
• ⁡
• (
• θ
• )
• =
• 1
• N
• ⁢
• ∑
• i
• =
• 1
• N
• Loss
• i
• ⁢
• (
• y
• i
• ,
• h
• ⁡
• (
• f
• θ
• (
• x
• i
• ⁢
• 1
• )
• ,
• f
• θ
• (
• x
• i
• ⁢
• 2
• )
• ,
• …
• ,
• f
• θ
• (
• x
• i
• ⁢
• m
• )
• )
• )
• ,
• wherein xi1, xi2, . . . , xim are the instances of bag i, yi are the associated bag labels, h is a permutation-invariant pooling function, N is the number of bags and M is the number of instances in each bag.

7:

• The method according to claim 5, further comprising:
• training the parameters of the MIL classifier fθ by the loss function L(θ) that includes a probability calibration function C configured to predict in each training epoch k+1 the probabilities {circumflex over (p)}i of ŷi of the previous training epoch k, wherein
• y
• ˆ
• i
• =
• max
• j
• (
• f
• θ
• (
• x
• ij
• )
• )
• and xij corresponds to the tuple (si, ai), where si is the peptide and ai is the jth MHC molecule in Ai.

9:

• The method according to claim 1, further comprising:
• using the MIL classifier fθ to make predictions for all combinations of peptide sequences and MHC molecules present in the biological sample;
• and
• determining the peptides with the highest likelihood of being presented as candidates for being synthesized and included in a personalized cancer vaccine.

11:

• A system for predicting binding and presentation of peptides by major histocompatibility complex (MHC) molecules, the system comprising one or more processors which, alone or in combination, are configured to allow for execution of a method comprising:
• collecting or generating training data, wherein the training data includes a set of MHC molecules present in a biological sample as well as a set of observed peptide sequences that are presented by at least one of the MHC molecules present in the biological sample, wherein it is not known to which specific of the MHC molecules a peptide sequence is bound,
• organizing the training data in bags, with each bag having a set of training instances, wherein labels are known for the bags, but unknown for the training instances;
• using a loss function to train a multiple instance learning (MIL) classifier fθ at an instance-level;
• and
• predicting the label of new instances by applying the MIL classifier fθ directly and/or predicting the label of new bags by applying the MIL classifier fθ to each instance of a respective bag and aggregating the results among all instances of the respective bag.

12:

• A computer-implemented method for performing multiple instance learning, multiple instance learning (MIL), the method comprising:
• collecting or generating training data, wherein the training data includes bags with each bag having a set of training instances, wherein labels are known for the bags, but unknown for the training instances;
• training an MIL classifier at an instance-level by using a loss function that explicitly accounts for a model confidence in the model predictions during training, wherein individual training instances from positively labeled bags are weighted by a calibrated current model confidence function;
• and
• predicting the label of new instances by applying the MIL classifier directly and/or predicting the label of new bags by applying the MIL classifier to each instance of a respective bag and aggregating the results among all instances of the respective bag.

15:

• The method according to claim 12, wherein the training data include a set of stained images of histological samples obtained by taking slices of tissue from a biological sample, wherein the stained images are split into patches, and
• wherein the MIL classifier is trained to predict whether or not a particular patch of a stained image contains a cancerous region;
• or
• wherein the training data include a set of text documents, wherein each of the documents is considered to represent a bag of the training data and the paragraphs and/or sentences of the documents are considered to represent the training instances of the respective bag, and
• wherein the MIL classifier is trained to predict a topic of the documents.