Abstract: Techniques for identifying missing evidence are provided. A plurality of documents, each comprising digitally encoded natural language text data, is received. The plurality of documents is processed to determine a plurality of pair-wise comparisons between a plurality of therapies, where each of the plurality of pair-wise comparisons indicate a relative efficacy of at least one therapy in the plurality of therapies, as compared to at least one other therapy in the plurality of therapies. A knowledge graph is generated based at least in part on aggregating the plurality of pair-wise comparisons, and the knowledge graph is analyzed to identify one or more knowledge gaps within the knowledge graph. Finally, at least an indication of the identified one or more knowledge gaps is output.

Abstract: A method and apparatus for providing curated criteria to identify one or more candidates for a clinical trial is disclosed. A computer processor identifies a first input criterion for the clinical trial. The processor employs a trained first recurrent neural network (RNN) configured as an encoder to encode the first input criterion. The encoder extracts key features of the medical condition of the patient. The processor employs a trained second RNN configured as a decoder to generate a curated output criterion by processing the encoded first input criterion based on the derived key features. The processor employs a machine learning model to ingest the curated output criterion to identify the one or more candidates for the clinical trial.

Abstract: Methods and apparatuses for performing clinical trial editing using machine learning are provided. One example method generally includes receiving information of a first clinical trial that is being drafted and identifying, in a corpus of literature, a plurality of documents that are relevant to the first clinical trial, based on the title of the first clinical trial. The method further includes providing the plurality of documents and the plurality of criteria to a machine learning model configured to output for each respective criterion of the plurality of criteria, a confidence value for the respective criterion, receiving as output from the machine learning model the confidence value for each respective criterion and, upon determining that a first criterion has a first confidence value below a predefined threshold, prompting a user to verify the first criterion.

Abstract: Disclosed herein are human antibody molecules that immunospecifically bind to human CXCR2. The disclosed human antibody molecules are potent and selective antagonists of CXCR2 functions and prevent the recruitment of neutrophils into tissues without strongly depleting circulating neutrophil numbers. Pharmaceutical compositions, nucleic acid molecules, vectors, cells, and uses of the disclosed antibodies are also provided.

Abstract: Techniques for unbounded therapy evaluation are provided. A request to suggest a potential therapy based on a patient profile is received. A plurality of accepted therapies is determined based on the patient profile, where the plurality of accepted therapies is based on stored definitions obtained from one or more subject matter experts. Next, a plurality of therapy components is identified based on the plurality of accepted therapies. A plurality of potential therapies is then identified based on the plurality of therapy components, where none of the plurality of potential therapies are included in the plurality of accepted therapies. A score is generated for a potential therapy of the plurality of potential therapies based on analyzing a knowledge graph, where the score indicates a suitability of the potential therapy for a patient associated with the patient profile. Finally, the potential therapy is provided, along with an indication of the score.

Abstract: The present invention provides a fusion polypeptide comprising a first domain and a second domain, wherein the first domain comprises a polypeptide ligand which binds to a cell surface-associated antigen and the second domain comprises aglycosylated interferon ? 2b (IFN?2b) having a sequence of SEQ ID NO: 1 or SEQ ID NO: 2. The aglycosylated IFN?2b further comprises one or more amino acid substitutions or deletions which attenuate the activity of the aglycosylated IFN?2b.

Abstract: Disclosed herein are human antibody molecules that immunospecifically bind to human CXCR2. The disclosed human antibody molecules are potent and selective antagonists of CXCR2 functions and prevent the recruitment of neutrophils into tissues without strongly depleting circulating neutrophil numbers. Pharmaceutical compositions, nucleic acid molecules, vectors, cells, and uses of the disclosed antibodies are also provided.

Abstract: Systems, apparatuses, and methods for performing a software override of a power estimation mechanism are disclosed. A computing system includes a plurality of tuned parameters for generating an estimate of power consumption. The tuned parameters are generated based on post-silicon characterization of the system. After deployment, the system executes a plurality of different applications. When launching a particular application, the system loads a corresponding set of override parameters which are used to replace the plurality of tuned parameters. The system generates an estimate of power consumption using the set of override parameters rather than the previously determined tuned parameters. Then while executing the particular application, the system makes adjustments to power and frequency values for the various system components based on the estimate of power consumption.

Abstract: The present invention provides a fusion polypeptide comprising a first domain and a second domain, wherein the first domain comprises a polypeptide ligand which binds to a cell surface-associated antigen and the second domain comprises aglycosylated interferon ? 2b (IFN?2b) having a sequence of SEQ ID NO: 1 or SEQ ID NO: 2. The aglycosylated IFN?2b further comprises one or more amino acid substitutions or deletions which attenuate the activity of the aglycosylated IFN?2b.

Abstract: Method and apparatus for predicting beneficial clinical trials for patients. Embodiments include receiving one or more attributes of a first patient. Embodiments include selecting a potential clinical trial for the first patient, based on the one or more attributes. Embodiments include identifying a cohort of patients that are clinically similar to the first patient, based on the one or more attributes, wherein each patient in the cohort has undergone a respective trial that is either (i) the potential clinical trial, or (ii) a clinically similar clinical trial. Embodiments include determining, for each respective patient in the cohort, a respective outcome of the respective trial. Embodiments include generating a predicted outcome for the first patient, based on the respective outcomes for each patient in the cohort. Embodiments include refraining from recommending the potential clinical trial for the first patient, based on the predicted outcome.

Abstract: Method and apparatus for predicting clinical trial eligibility for patients. Embodiments include determining a current value for each of a plurality of attributes of a first patient. Embodiments include identifying a cohort of patients that are clinically similar to the first patient, based on the plurality of attributes. Embodiments include analyzing data associated with the cohort of patients to determine an attribute trend for at least a first attribute the plurality of attributes. Embodiments include generating a predicted value for the first attribute, based on the current value of the first attribute and the attribute trend for the first attribute. Embodiments include identifying a plurality of clinical trials based on the first attribute. Embodiments include generating a probability that the first patient will be eligible for each of the plurality of clinical trials at a future time, based on the predicted value for the first attribute.

Abstract: A method and apparatus for identifying implied criteria for a clinical trial is disclosed. An example method generally includes generating a training data set from a corpus of clinical trial specifications. The training data set may include at least a first sample corresponding to a first trial. The first sample may include a first feature based on one or more explicitly stated trial criteria, a second feature based on metadata describing the first trial, and a third feature based on patient data of patients associated with the first trial. A machine learning model is trained, using a supervised learning approach, based on the training data set. A system processes a second trial as an input to the trained machine learning model to determine one or more implied criteria that are not explicitly enumerated in a specification for the second trial.

Abstract: Embodiments provide for reduced user input requests by identifying predefined diagnosis paradigms; creating a synthetic diagnosis paradigm via a machine learning process based on prior selections of action plans recommended by the predefined diagnosis paradigms and values entered therefor, wherein the synthetic diagnosis paradigm identifies the action plans to treat the plurality of conditions based on a subset of the attribute inputs used by the predefined diagnosis paradigms; generating a graphical user interface (GUI) to prompt input for values for the subset of attribute inputs; in response to receiving the values for the subset of attribute inputs, identifying at least one condition according to the predefined and synthetic diagnosis paradigms; and displaying the action plans in the GUI in association with the synthetic and predefined diagnosis paradigms according to evaluations of the action plans based on the respective logical structures.

Abstract: Disclosed herein are fully human antibody molecules that immunospecifically bind to human IL-5. The antibody molecules can bind to human IL-5 with an equilibrium affinity constant (KD) of at least about 40 pM as determined by surface plasmon resonance.

Abstract: Techniques for cognitive analysis of documents are provided. A document corresponding to a clinical trial is received. Criteria are identified for the clinical trial based on processing the electronic document using natural language processing (NLP) techniques, where the criteria specify attributes of individuals that are eligible for the trial. Further, the criteria are analyzed using NLP techniques to determine a respective confidence level for each respective criterion, where the confidence level indicates a degree of certainty that the criterion is applicable to the trial. A knowledge graph is generated based at least in part on the clinical trial, where the confidence level assigned to each criterion is included in the knowledge graph for subsequent use. Finally, one or more therapies in the knowledge graph are selected to be recommended, based on a patient profile and the confidence level assigned to each of the criteria.

Abstract: Method and apparatus for recommending clinical trials for patients. Embodiments include determining a plurality of clinical trials that a first patient is eligible to participate in. Embodiments further include determining a plurality of current attributes of the first patient. Embodiments further include determining a plurality of predicted attributes of the first patient for a future point in time. Embodiments further include generating a fitness measure for each of the plurality of clinical trials, with respect to the first patient, by processing data about each of the plurality of clinical trials, the plurality of current attributes, and the plurality of predicted attributes with a machine learning (ML) model. Embodiments further include ranking the plurality of clinical trials based on the generated fitness measures.

Abstract: A method and apparatus for filtering clinical trials using machine learning techniques is disclosed. An example method generally includes receiving a first set of filters that were applied to a first plurality of clinical trials with respect to a first patient. A system determines one or more attributes of the first patient and trains a machine learning (ML) model based on the first set of filters and the one or more attributes of the first patient. The system receives a selection of a second patient, determines one or more attributes of the second patient, and generates a second set of filters by processing the one or more attributes of the second patient using the trained ML model.

Abstract: Antibodies that specifically bind to CD38, as well as constructs comprising such antibodies fused to attenuated interferon alpha-2B proteins are provided. Anti-CD38-attenuated interferon alpha-2b fusion constructs may be used to inhibit proliferation in cancerous cells that express both CD38 and the receptor for IFN-alpha2b, as well as to induce apoptosis in such cells. Inhibition of proliferation and induction of apoptosis in cancerous cells may serve as the basis for the treatment of the underlying cancer.

Abstract: Disclosed herein are fully human antibody molecules that immunospecifically bind to human IL-5. The antibody molecules can bind to human IL-5 with an equilibrium affinity constant (KD) of at least about 40 pM as determined by surface plasmon resonance.

Abstract: A seating system for a vehicle includes a seat-back frame and a backboard attached to the seat-back frame. An attachment arrangement includes an elongated member attached to the seat-back frame and the backboard, and is configured to remain attached to the seat-back frame and the backboard by plastically deforming when the seat-back frame and the backboard are subjected to a separation force of a predetermined magnitude greater than a magnitude otherwise required to detach the elongated member from the backboard.