A computer-implemented method for analyzing cell-free deoxyribonucleic acid (cfDNA) fragments derived from a subject, the method comprising: a) subjecting the cfDNA fragments to library preparation and high-throughput sequencing to generate sequence information representative of cfDNA fragments from the subject, comprising: (i) tagging the cfDNA fragments with unique or non-unique molecular tags; (ii) amplifying the tagged cfDNA fragments; and (iii) tracking of the progeny sequences by tracking of the tag; (b) aligning the sequencing information with a reference sequence; (c) performing multi-parametric analysis of the aligned sequence information, thereby generating a multi-parametric model representative of the cfDNA fragments, wherein the multi-parametric model comprises two or more parameters selected from parameters which are indicative of (i) a length of the cfDNA fragments that align with each of a plurality of base positions in a genome, (ii) a number of the cfDNA fragments that align with each of a plurality of base positions in a genome, and (iii) a number of the cfDNA fragments that start or end at each of a plurality of base positions in a genome; and d) performing, with the computer, statistical analysis with a trained classifier to classify the multi-parametric model as being associated with one or more nucleosomal occupancy profiles representing distinct cohorts, wherein at least one of the nucleosomal occupancy profiles is associated with one or more assessments selected from the group consisting of: tumor indication, early detection of cancer, tumor type, tumor severity, tumor aggressiveness, tumor resistance to treatment, tumor clonality, tumor druggability, tumor progression, and plasma dysregulation score. a) subjecting the cfDNA fragments to library preparation and high-throughput sequencing to generate sequence information representative of cfDNA fragments from the subject, comprising: (i) tagging the cfDNA fragments with unique or non-unique molecular tags; (ii) amplifying the tagged cfDNA fragments; and (iii) tracking of the progeny sequences by tracking of the tag; (i) tagging the cfDNA fragments with unique or non-unique molecular tags; (ii) amplifying the tagged cfDNA fragments; and (iii) tracking of the progeny sequences by tracking of the tag; (b) aligning the sequencing information with a reference sequence; (c) performing multi-parametric analysis of the aligned sequence information, thereby generating a multi-parametric model representative of the cfDNA fragments, wherein the multi-parametric model comprises two or more parameters selected from parameters which are indicative of (i) a length of the cfDNA fragments that align with each of a plurality of base positions in a genome, (ii) a number of the cfDNA fragments that align with each of a plurality of base positions in a genome, and (iii) a number of the cfDNA fragments that start or end at each of a plurality of base positions in a genome; and d) performing, with the computer, statistical analysis with a trained classifier to classify the multi-parametric model as being associated with one or more nucleosomal occupancy profiles representing distinct cohorts, wherein at least one of the nucleosomal occupancy profiles is associated with one or more assessments selected from the group consisting of: tumor indication, early detection of cancer, tumor type, tumor severity, tumor aggressiveness, tumor resistance to treatment, tumor clonality, tumor druggability, tumor progression, and plasma dysregulation score. <EOS>