Abstract: Various embodiments of the present disclosure disclose a machine learning training approach for intelligently training a plurality of machine learning models associated with a multitask environment. The techniques include jointly training the plurality of machine learning models based on task similarities by generating a similarity matrix corresponding to a plurality machine learning models, generating a sharing loss value for the at least two machine learning models, generating, using a loss function and a training dataset, a prediction loss value for a particular machine learning model of the at least two machine learning models, generating an aggregated loss value for the particular machine learning model based on the similarity matrix, the sharing loss value, and the prediction loss value, and updating the particular machine learning model based on the aggregated loss value for the particular machine learning model.

Abstract: Embodiments of the disclosure provide for improved processing of data with different timescales, for example high-frequency data and low-frequency data. Embodiments specifically improve such processing of different timescale data processed by a machine learning model. Additionally or alternatively, some embodiments include improved processing of data with different timescales by selecting an optimal variant from a plurality of possible variants of a prediction model.

Abstract: Various embodiments of the present disclosure describe feature bias mitigation techniques for machine learning models. The techniques include generating or receiving a contextual bias correction function, a protected bias correction function, or an aggregate bias for a machine learning model. The aggregate bias correction function for the model may be based on the contextual or protected bias correction functions. At least one of the generated or received functions may be configured to generate an individualized threshold tailored to specific attributes of an input to the machine learning model. Each of the functions may generate a respective threshold based on one or more individual parameters of the input. An output from the machine learning model may be compared to the individualized threshold to generate a bias adjusted output that accounts for the individual parameters of the input.

Abstract: Various embodiments of the present disclosure describe feature bias mitigation techniques for machine learning models. The techniques include generating or receiving a contextual bias correction function, a protected bias correction function, or an aggregate bias for a machine learning model. The aggregate bias correction function for the model may be based on the contextual or protected bias correction functions. At least one of the generated or received functions may be configured to generate an individualized threshold tailored to specific attributes of an input to the machine learning model. Each of the functions may generate a respective threshold based on one or more individual parameters of the input. An output from the machine learning model may be compared to the individualized threshold to generate a bias adjusted output that accounts for the individual parameters of the input.

Abstract: Various embodiments of the present disclosure disclose machine-learning based data augmentation and prediction techniques for generating predictive classifications based on temporal data. A machine-learning based model is provided that can receive an input data object associated with a plurality of predictive temporal parameters; determine augmented temporal data objects based on the predictive temporal parameters; generate predictive data representations for the input data object based on the predictive temporal parameters and the augmented temporal data objects; generate a multi-channel predictive data representation based on the predictive data representations for the input data object; and generate a predictive classification for the input data object based on the multi-channel predictive data representation.

Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for processing document classification system outputs, wherein classification routine iterations are performed using masked document data objects comprising one or more masked text blocks. Text block importance score for text blocks are generated and compared to generate predictive data output comprising text blocks determined to be the most influential in classifying the document data objects with respect to one or more classification labels.

Abstract: Deep learning methods and systems for detecting biomarkers within optical coherence tomography volumes using such deep learning methods and systems are provided. Embodiments predict the presence or absence of clinically useful biomarkers in OCT images using deep neural networks. The lack of available training data for canonical deep learning approaches is overcome in embodiments by leveraging a large external dataset consisting of foveal scans using transfer learning. Embodiments represent the three-dimensional OCT volume by “tiling” each slice into a single two dimensional image, and adding an additional component to encourage the network to consider local spatial structure. Methods and systems, according to embodiments are able to identify the presence or absence of AMD-related biomarkers on par with clinicians. Beyond identifying biomarkers, additional models could be trained, according to embodiments, to predict the progression of these biomarkers over time.

Abstract: Systems and methods for training a signal generation model and generating imputed physiological waveform signals in accordance with embodiments of the invention are illustrated. One embodiment includes a method for measuring physiological waveform signals. The method includes steps for receiving a set of one or more input physiological waveform signals, processing the set of input physiological waveform signals, generating an output physiological waveform signal using a signal generation model, and providing outputs based on the generated output signal.

Abstract: The present invention provides methods of determining a Genetic Composite Index score by assessing the association between an individual's genotype and at least one disease or condition. The assessment comprises comparing an individual's genomic profile with a database of medically relevant genetic variations that have been established to associate with at least one disease or condition.

Abstract: The present disclosure provides methods and systems for assessing an individual's genotype correlations to a phenotype by analyzing the individual's genomic profile and using ancestral data to determine the correlations between genotypes and phenotypes.

Abstract: The present invention provides methods of determining a Genetic Composite Index score by assessing the association between an individual's genotype and at least one disease or condition. The assessment comprises comparing an individual's genomic profile with a database of medically relevant genetic variations that have been established to associate with at least one disease or condition.

Abstract: The present disclosure provides methods and systems for incorporating multiple environmental and genetic risk factors into an individual's genomic profile. Methods include assessing the association between an individual's genotype and at least one disease or condition by incorporating multiple genetic risk factors, environmental risk factors, or a combination of both.

Abstract: The present disclosure provides methods and systems for assessing an individual's genotype correlations to a phenotype by analyzing the individual's genomic profile and using ancestral data to determine the correlations between genotypes and phenotypes.

Abstract: A method of comparing nucleic acid sequences being ESTs included in a first database of sequences and nucleic acid sequences included in a second database of sequences to form groups of sequences from the two databases that all relate to the same gene. For each one or more n-groups of sequences of one of the two databases, associating therewith lists of nucleic acid sequences, each from one of said two databases, each sequence on the list containing the n-groups, and matching sequences on the lists to generate said group.

Abstract: A method of obtaining an mRNA sequence having alternative spliced variants from a database of ESTs, comprising: providing a raw database comprising a plurality of ESTs; and assembling ones of said ESTs into mRNA sequences, wherein said assembling includes identifying alternative spliced regions.