Cancer classification with genomic region modeling

A method comprising:

• performing, by a sequencing device, a sequencing assay with a targeted panel targeting a plurality of genomic regions, wherein the targeted panel includes, for each genomic region, one or more hybridization probes for capturing cfDNA fragments originating from the genomic region, wherein the sequencing assays yields sequencing data for a biological sample comprising a plurality of sequence reads representing cfDNA fragments, each sequence read overlapping at least one genomic region of a plurality of genomic regions;
• for each cfDNA fragment of the biological sample, determining, by a computer processor, a first score for the genomic region that the cfDNA fragment overlaps, the first score for a genomic region determined by inputting the sequence read for the cfDNA fragment into a shallow neural network trained for the genomic region, the shallow neural network configured to generate the first score representative of a likelihood that the cfDNA fragment is derived from a cancer biological sample, each shallow neural network of a plurality of shallow neural networks spanning the plurality of genomic regions comprises no more than three hidden layers and is trained by:
• accessing a first plurality of sequence reads obtained from cancer samples and a second plurality of sequence reads obtained from non-cancer samples, wherein the first plurality of sequence reads and the second plurality of sequence reads overlap the genomic region, and
• training the shallow neural network with the first plurality of sequence reads and the second plurality of sequence reads;
• generating, by the computer processor, a feature vector for the biological sample, each feature of the feature vector corresponding to a genomic region of the plurality of genomic regions and generated according to a count of cfDNA fragments having a score for the genomic region above a threshold score;
• and
• inputting, by the computer processor, the feature vector into a trained machine-learning model to generate a cancer prediction for the biological sample, the trained model trained by:
• accessing sequencing data for a plurality of cancer samples and a plurality of non-cancer samples,
• generating a feature vector for each cancer sample and for each non-cancer sample by applying the shallow neural networks to the sequencing data of each sample, and
• training the trained model with the feature vectors for the cancer samples and the non-cancer samples.