A computer-implemented method for classifying resistances of a sample pathogen against one or more anti-infectives from biological sequence data of said sample pathogen, the biological sequence data comprising a sequence of deoxyribonucleic acid, DNA, called genome, or a sequence of ribonucleic acid, RNA, called transcriptome, of said pathogen, the method comprising: augmenting sequence data of a first plurality of biological sequences of pathogens, different from the sample pathogen, having known resistances against the one or more anti-infectives, to sequence data of a second plurality of variant biological sequences by generating sequence data of multiple variants of each sequence of the first plurality of sequences, each variant carrying the same resistances against the one or more anti-infectives as the corresponding sequence of the first plurality of sequences, wherein generating sequence data of multiple variants for each sequence of the first plurality of sequences, referred to as first sequence comprises: partitioning the first sequence into multiple parts, each part not exceeding predetermined length; and generating multiple variants of the first sequence by concatenating once randomly selected parts of said multiple parts to respective variants of the first sequence, thereby obtaining sequence data of the second plurality of biological sequences; training a neural network, NN, via an input layer, specimen sequence data of the second plurality of biological sequences of pathogens labelled with known resistances of the respective pathogens, using forward propagation that repeatedly compares outputs of the neural network with corresponding ground truth labels to calculate an error using a loss function, and using backpropagation to calculate a gradient of the loss function to adjust the weighting values of the NN, obtaining, after training the NN, biological sequence data for inferring resistances of a sample pathogen; and passing said biological sequence data to an input layer of the NN with weights and biases after teaching incorporated; generating, via an inference output layer of the NN, classification predictions of resistances of the sample pathogen against the one or more anti-infectives obtained from output of the neural network. augmenting sequence data of a first plurality of biological sequences of pathogens, different from the sample pathogen, having known resistances against the one or more anti-infectives, to sequence data of a second plurality of variant biological sequences by generating sequence data of multiple variants of each sequence of the first plurality of sequences, each variant carrying the same resistances against the one or more anti-infectives as the corresponding sequence of the first plurality of sequences, wherein generating sequence data of multiple variants for each sequence of the first plurality of sequences, referred to as first sequence comprises: partitioning the first sequence into multiple parts, each part not exceeding predetermined length; and generating multiple variants of the first sequence by concatenating once randomly selected parts of said multiple parts to respective variants of the first sequence, thereby obtaining sequence data of the second plurality of biological sequences; training a neural network, NN, via an input layer, specimen sequence data of the second plurality of biological sequences of pathogens labelled with known resistances of the respective pathogens, using forward propagation that repeatedly compares outputs of the neural network with corresponding ground truth labels to calculate an error using a loss function, and using backpropagation to calculate a gradient of the loss function to adjust the weighting values of the NN, obtaining, after training the NN, biological sequence data for inferring resistances of a sample pathogen; and passing said biological sequence data to an input layer of the NN with weights and biases after teaching incorporated; generating, via an inference output layer of the NN, classification predictions of resistances of the sample pathogen against the one or more anti-infectives obtained from output of the neural network. partitioning the first sequence into multiple parts, each part not exceeding predetermined length; and generating multiple variants of the first sequence by concatenating once randomly selected parts of said multiple parts to respective variants of the first sequence, thereby obtaining sequence data of the second plurality of biological sequences; training a neural network, NN, via an input layer, specimen sequence data of the second plurality of biological sequences of pathogens labelled with known resistances of the respective pathogens, using forward propagation that repeatedly compares outputs of the neural network with corresponding ground truth labels to calculate an error using a loss function, and using backpropagation to calculate a gradient of the loss function to adjust the weighting values of the NN, obtaining, after training the NN, biological sequence data for inferring resistances of a sample pathogen; and passing said biological sequence data to an input layer of the NN with weights and biases after teaching incorporated; generating, via an inference output layer of the NN, classification predictions of resistances of the sample pathogen against the one or more anti-infectives obtained from output of the neural network.